diff --git a/.gitignore b/.gitignore index cb7d5e7..b412fc2 100644 --- a/.gitignore +++ b/.gitignore @@ -41,4 +41,7 @@ coverage.xml *.log # Sphinx documentation -docs/_build/ \ No newline at end of file +docs/_build/ + +# IPython notebook checkpoints +.ipynb_checkpoints/ \ No newline at end of file diff --git a/Intro.ipynb b/Intro.ipynb index cda828c..efb6365 100644 --- a/Intro.ipynb +++ b/Intro.ipynb @@ -1,1500 +1,1546 @@ { - "metadata": { - "name": "", - "signature": "sha256:c816688f240e8aa16967770d5deb6034febeb5f6ea78fc0cda8e4d85adc1f4f7" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Introduction To Python" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction To Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a collection of various statements, features, etc. of IPython and the Python language. Much of this content is taken from other notebooks so I can't take credit for it, I just extracted the highlights I felt were most useful.\n", + "\n", + "Code cells are run by pressing shift-enter or using the play button in the toolbar." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a collection of various statements, features, etc. of IPython and the Python language. Much of this content is taken from other notebooks so I can't take credit for it, I just extracted the highlights I felt were most useful.\n", + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "x = math.cos(2 * math.pi)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the whole module into the current namespace instead." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "from math import *\n", + "x = cos(2 * pi)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Several ways to look at documentation for a module." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" + ] + } + ], + "source": [ + "print(dir(math))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function cos in module math:\n", "\n", - "Code cells are run by pressing shift-enter or using the play button in the toolbar." + "cos(...)\n", + " cos(x)\n", + " \n", + " Return the cosine of x (measured in radians).\n", + "\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = 10" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "10\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import math" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = math.cos(2 * math.pi)\n", - "print(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1.0\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import the whole module into the current namespace instead." + } + ], + "source": [ + "help(math.cos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = 1.0\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dynamically typed\n", + "x = 1\n", + "type(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operators" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, -1, 2, 0)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 + 2, 1 - 2, 1 * 2, 1 / 2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integer division of float numbers\n", + "3.0 // 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# power operator\n", + "2 ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True and False" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "not False" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True or False" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False, False, False, True, True)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 > 1, 2 < 1, 2 > 2, 2 < 2, 2 >= 2, 2 <= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# equality\n", + "[1,2] == [1,2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strings" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = \"Hello world\"\n", + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello test\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from math import *\n", - "x = cos(2 * pi)\n", - "print(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1.0\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Several ways to look at documentation for a module." + } + ], + "source": [ + "s2 = s.replace(\"world\", \"test\")\n", + "print(s2)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'H'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'world'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[6:]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hlowrd'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# define step size of 2\n", + "s[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('str1', 'str2', 'str3')\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(dir(math))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "help(math.cos)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Help on built-in function cos in module math:\n", - "\n", - "cos(...)\n", - " cos(x)\n", - " \n", - " Return the cosine of x (measured in radians).\n", - "\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Variables" + } + ], + "source": [ + "# automatically adds a space\n", + "print(\"str1\", \"str2\", \"str3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value = 1.000000\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = 1.0\n", - "type(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "float" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# dynamically typed\n", - "x = 1\n", - "type(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "int" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Operators" + } + ], + "source": [ + "# C-style formatting\n", + "print(\"value = %f\" % 1.0) " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value1 = 3.1415, value2 = 1.5\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "1 + 2, 1 - 2, 1 * 2, 1 / 2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "(3, -1, 2, 0)" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# integer division of float numbers\n", - "3.0 // 2.0" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "1.0" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# power operator\n", - "2 ** 2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "4" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "True and False" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "False" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "not False" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "True" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "True or False" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "True" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "2 > 1, 2 < 1, 2 > 2, 2 < 2, 2 >= 2, 2 <= 2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "(True, False, False, False, True, True)" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# equality\n", - "[1,2] == [1,2]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "True" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Strings" + } + ], + "source": [ + "# alternative, more intuitive way of formatting a string \n", + "s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n", + "print(s3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lists" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[1, 2, 3, 4]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s = \"Hello world\"\n", - "type(s)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "str" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len(s)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "11" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s2 = s.replace(\"world\", \"test\")\n", - "print(s2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello test\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s[0]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "'H'" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s[0:5]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "'Hello'" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s[6:]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, - "text": [ - "'world'" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "s[:]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "'Hello world'" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# define step size of 2\n", - "s[::2]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "'Hlowrd'" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# automatically adds a space\n", - "print(\"str1\", \"str2\", \"str3\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('str1', 'str2', 'str3')\n" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# C-style formatting\n", - "print(\"value = %f\" % 1.0) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "value = 1.000000\n" - ] - } - ], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# alternative, more intuitive way of formatting a string \n", - "s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n", - "print(s3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "value1 = 3.1415, value2 = 1.5\n" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Lists" + } + ], + "source": [ + "l = [1,2,3,4]\n", + "\n", + "print(type(l))\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 3]\n", + "[1, 3]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l = [1,2,3,4]\n", - "\n", - "print(type(l))\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "[1, 2, 3, 4]\n" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(l[1:3])\n", - "print(l[::2])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[2, 3]\n", - "[1, 3]\n" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l[0]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "1" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# don't have to be the same type\n", - "l = [1, 'a', 1.0, 1-1j]\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 'a', 1.0, (1-1j)]\n" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "start = 10\n", - "stop = 30\n", - "step = 2\n", - "range(start, stop, step)\n", - "\n", - "# consume the iterator created by range\n", - "list(range(start, stop, step))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create a new empty list\n", - "l = []\n", - "\n", - "# add an elements using `append`\n", - "l.append(\"A\")\n", - "l.append(\"d\")\n", - "l.append(\"d\")\n", - "\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['A', 'd', 'd']\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l[1:3] = [\"b\", \"c\"]\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['A', 'b', 'c']\n" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l.insert(0, \"i\")\n", - "l.insert(1, \"n\")\n", - "l.insert(2, \"s\")\n", - "l.insert(3, \"e\")\n", - "l.insert(4, \"r\")\n", - "l.insert(5, \"t\")\n", - "\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['i', 'n', 's', 'e', 'r', 't', 'A', 'b', 'c']\n" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l.remove(\"A\")\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['i', 'n', 's', 'e', 'r', 't', 'b', 'c']\n" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "del l[7]\n", - "del l[6]\n", - "\n", - "print(l)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['i', 'n', 's', 'e', 'r', 't']\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Tuples" + } + ], + "source": [ + "print(l[1:3])\n", + "print(l[::2])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'a', 1.0, (1-1j)]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "point = (10, 20)\n", - "print(point, type(point))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "((10, 20), )\n" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# unpacking\n", - "x, y = point\n", - "\n", - "print(\"x =\", x)\n", - "print(\"y =\", y)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('x =', 10)\n", - "('y =', 20)\n" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Dictionaries" + } + ], + "source": [ + "# don't have to be the same type\n", + "l = [1, 'a', 1.0, 1-1j]\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "start = 10\n", + "stop = 30\n", + "step = 2\n", + "range(start, stop, step)\n", + "\n", + "# consume the iterator created by range\n", + "list(range(start, stop, step))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'd', 'd']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "params = {\"parameter1\" : 1.0,\n", - " \"parameter2\" : 2.0,\n", - " \"parameter3\" : 3.0,}\n", - "\n", - "print(type(params))\n", - "print(params)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}\n" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "params[\"parameter1\"] = \"A\"\n", - "params[\"parameter2\"] = \"B\"\n", - "\n", - "# add a new entry\n", - "params[\"parameter4\"] = \"D\"\n", - "\n", - "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", - "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", - "print(\"parameter3 = \" + str(params[\"parameter3\"]))\n", - "print(\"parameter4 = \" + str(params[\"parameter4\"]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "parameter1 = A\n", - "parameter2 = B\n", - "parameter3 = 3.0\n", - "parameter4 = D\n" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Control Flow" + } + ], + "source": [ + "# create a new empty list\n", + "l = []\n", + "\n", + "# add an elements using `append`\n", + "l.append(\"A\")\n", + "l.append(\"d\")\n", + "l.append(\"d\")\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'b', 'c']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "statement1 = False\n", - "statement2 = False\n", - "\n", - "if statement1:\n", - " print(\"statement1 is True\")\n", - "elif statement2:\n", - " print(\"statement2 is True\")\n", - "else:\n", - " print(\"statement1 and statement2 are False\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "statement1 and statement2 are False\n" - ] - } - ], - "prompt_number": 43 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Loops" + } + ], + "source": [ + "l[1:3] = [\"b\", \"c\"]\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't', 'A', 'b', 'c']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for x in range(4):\n", - " print(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n", - " print(word)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "scientific\n", - "computing\n", - "with\n", - "python\n" - ] - } - ], - "prompt_number": 45 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for key, value in params.items():\n", - " print(key + \" = \" + str(value))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "parameter4 = D\n", - "parameter1 = A\n", - "parameter3 = 3.0\n", - "parameter2 = B\n" - ] - } - ], - "prompt_number": 46 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for idx, x in enumerate(range(-3,3)):\n", - " print(idx, x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(0, -3)\n", - "(1, -2)\n", - "(2, -1)\n", - "(3, 0)\n", - "(4, 1)\n", - "(5, 2)\n" - ] - } - ], - "prompt_number": 47 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "l1 = [x**2 for x in range(0,5)]\n", - "print(l1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[0, 1, 4, 9, 16]\n" - ] - } - ], - "prompt_number": 48 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i = 0\n", - "while i < 5:\n", - " print(i)\n", - " i = i + 1\n", - "print(\"done\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "done\n" - ] - } - ], - "prompt_number": 49 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Functions" + } + ], + "source": [ + "l.insert(0, \"i\")\n", + "l.insert(1, \"n\")\n", + "l.insert(2, \"s\")\n", + "l.insert(3, \"e\")\n", + "l.insert(4, \"r\")\n", + "l.insert(5, \"t\")\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't', 'b', 'c']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# include a docstring\n", - "def func(s):\n", - " \"\"\"\n", - " Print a string 's' and tell how many characters it has \n", - " \"\"\"\n", - " \n", - " print(s + \" has \" + str(len(s)) + \" characters\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 50 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "help(func)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Help on function func in module __main__:\n", - "\n", - "func(s)\n", - " Print a string 's' and tell how many characters it has\n", - "\n" - ] - } - ], - "prompt_number": 51 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "func(\"test\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "test has 4 characters\n" - ] - } - ], - "prompt_number": 52 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def square(x):\n", - " return x ** 2" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 53 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "square(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 54, - "text": [ - "25" - ] - } - ], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# multiple return values\n", - "def powers(x):\n", - " return x ** 2, x ** 3, x ** 4" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "powers(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 56, - "text": [ - "(25, 125, 625)" - ] - } - ], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x2, x3, x4 = powers(5)\n", - "print(x3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "125\n" - ] - } - ], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "f1 = lambda x: x**2\n", - "f1(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 58, - "text": [ - "25" - ] - } - ], - "prompt_number": 58 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "map(lambda x: x**2, range(-3,4))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 59, - "text": [ - "[9, 4, 1, 0, 1, 4, 9]" - ] - } - ], - "prompt_number": 59 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# convert iterator to list\n", - "list(map(lambda x: x**2, range(-3,4)))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 60, - "text": [ - "[9, 4, 1, 0, 1, 4, 9]" - ] - } - ], - "prompt_number": 60 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Classes" + } + ], + "source": [ + "l.remove(\"A\")\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['i', 'n', 's', 'e', 'r', 't']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class Point:\n", - " def __init__(self, x, y):\n", - " self.x = x\n", - " self.y = y\n", - " \n", - " def translate(self, dx, dy):\n", - " self.x += dx\n", - " self.y += dy\n", - " \n", - " def __str__(self):\n", - " return(\"Point at [%f, %f]\" % (self.x, self.y))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 61 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p1 = Point(0, 0)\n", - "print(p1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Point at [0.000000, 0.000000]\n" - ] - } - ], - "prompt_number": 62 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p2 = Point(1, 1)\n", - "\n", - "p1.translate(0.25, 1.5)\n", + } + ], + "source": [ + "del l[7]\n", + "del l[6]\n", + "\n", + "print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tuples" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "((10, 20), )\n" + ] + } + ], + "source": [ + "point = (10, 20)\n", + "print(point, type(point))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('x =', 10)\n", + "('y =', 20)\n" + ] + } + ], + "source": [ + "# unpacking\n", + "x, y = point\n", + "\n", + "print(\"x =\", x)\n", + "print(\"y =\", y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}\n" + ] + } + ], + "source": [ + "params = {\"parameter1\" : 1.0,\n", + " \"parameter2\" : 2.0,\n", + " \"parameter3\" : 3.0,}\n", + "\n", + "print(type(params))\n", + "print(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter1 = A\n", + "parameter2 = B\n", + "parameter3 = 3.0\n", + "parameter4 = D\n" + ] + } + ], + "source": [ + "params[\"parameter1\"] = \"A\"\n", + "params[\"parameter2\"] = \"B\"\n", + "\n", + "# add a new entry\n", + "params[\"parameter4\"] = \"D\"\n", + "\n", + "print(\"parameter1 = \" + str(params[\"parameter1\"]))\n", + "print(\"parameter2 = \" + str(params[\"parameter2\"]))\n", + "print(\"parameter3 = \" + str(params[\"parameter3\"]))\n", + "print(\"parameter4 = \" + str(params[\"parameter4\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Control Flow" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "statement1 and statement2 are False\n" + ] + } + ], + "source": [ + "statement1 = False\n", + "statement2 = False\n", + "\n", + "if statement1:\n", + " print(\"statement1 is True\")\n", + "elif statement2:\n", + " print(\"statement2 is True\")\n", + "else:\n", + " print(\"statement1 and statement2 are False\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loops" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n" + ] + } + ], + "source": [ + "for x in range(4):\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scientific\n", + "computing\n", + "with\n", + "python\n" + ] + } + ], + "source": [ + "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n", + " print(word)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parameter4 = D\n", + "parameter1 = A\n", + "parameter3 = 3.0\n", + "parameter2 = B\n" + ] + } + ], + "source": [ + "for key, value in params.items():\n", + " print(key + \" = \" + str(value))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, -3)\n", + "(1, -2)\n", + "(2, -1)\n", + "(3, 0)\n", + "(4, 1)\n", + "(5, 2)\n" + ] + } + ], + "source": [ + "for idx, x in enumerate(range(-3,3)):\n", + " print(idx, x)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 4, 9, 16]\n" + ] + } + ], + "source": [ + "l1 = [x**2 for x in range(0,5)]\n", + "print(l1)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "done\n" + ] + } + ], + "source": [ + "i = 0\n", + "while i < 5:\n", + " print(i)\n", + " i = i + 1\n", + "print(\"done\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# include a docstring\n", + "def func(s):\n", + " \"\"\"\n", + " Print a string 's' and tell how many characters it has \n", + " \"\"\"\n", + " \n", + " print(s + \" has \" + str(len(s)) + \" characters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function func in module __main__:\n", "\n", - "print(p1)\n", - "print(p2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Point at [0.250000, 1.500000]\n", - "Point at [1.000000, 1.000000]\n" - ] - } - ], - "prompt_number": 63 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exceptions" + "func(s)\n", + " Print a string 's' and tell how many characters it has\n", + "\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "try:\n", - " print(test)\n", - "except:\n", - " print(\"Caught an expection\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Caught an expection\n" - ] - } - ], - "prompt_number": 64 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "try:\n", - " print(test)\n", - "except Exception as e:\n", - " print(\"Caught an exception: \" + str(e))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Caught an exception: name 'test' is not defined\n" - ] - } - ], - "prompt_number": 65 } ], - "metadata": {} + "source": [ + "help(func)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test has 4 characters\n" + ] + } + ], + "source": [ + "func(\"test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def square(x):\n", + " return x ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "square(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# multiple return values\n", + "def powers(x):\n", + " return x ** 2, x ** 3, x ** 4" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(25, 125, 625)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "powers(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "125\n" + ] + } + ], + "source": [ + "x2, x3, x4 = powers(5)\n", + "print(x3)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1 = lambda x: x**2\n", + "f1(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 4, 1, 0, 1, 4, 9]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map(lambda x: x**2, range(-3,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 4, 1, 0, 1, 4, 9]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert iterator to list\n", + "list(map(lambda x: x**2, range(-3,4)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class Point:\n", + " def __init__(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " def translate(self, dx, dy):\n", + " self.x += dx\n", + " self.y += dy\n", + " \n", + " def __str__(self):\n", + " return(\"Point at [%f, %f]\" % (self.x, self.y))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Point at [0.000000, 0.000000]\n" + ] + } + ], + "source": [ + "p1 = Point(0, 0)\n", + "print(p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Point at [0.250000, 1.500000]\n", + "Point at [1.000000, 1.000000]\n" + ] + } + ], + "source": [ + "p2 = Point(1, 1)\n", + "\n", + "p1.translate(0.25, 1.5)\n", + "\n", + "print(p1)\n", + "print(p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exceptions" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caught an expection\n" + ] + } + ], + "source": [ + "try:\n", + " print(test)\n", + "except:\n", + " print(\"Caught an expection\")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caught an exception: name 'test' is not defined\n" + ] + } + ], + "source": [ + "try:\n", + " print(test)\n", + "except Exception as e:\n", + " print(\"Caught an exception: \" + str(e))" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ML-Exercise1.ipynb b/ML-Exercise1.ipynb index 13b040a..ec58569 100644 --- a/ML-Exercise1.ipynb +++ b/ML-Exercise1.ipynb @@ -1,1070 +1,2535 @@ { - "metadata": { - "name": "", - "signature": "sha256:2cf973cbf329c2a013c7395422504d8026dd2062b6060700661fa7052a7d6e12" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Machine Learning Exercise 1 - Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook covers a Python-based solution for the first programming exercise of the machine learning class on Coursera." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Part 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the first part of the exercise, we're tasked with implementing linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by importing some libraries and examining the data." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "path = os.getcwd() + '\\data\\ex1data1.txt'\n", - "data = pd.read_csv(path, header=None, names=['Population', 'Profit'])\n", - "data.head()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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PopulationProfit
0 6.1101 17.5920
1 5.5277 9.1302
2 8.5186 13.6620
3 7.0032 11.8540
4 5.8598 6.8233
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - " Population Profit\n", - "0 6.1101 17.5920\n", - "1 5.5277 9.1302\n", - "2 8.5186 13.6620\n", - "3 7.0032 11.8540\n", - "4 5.8598 6.8233" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data.describe()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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PopulationProfit
count 97.000000 97.000000
mean 8.159800 5.839135
std 3.869884 5.510262
min 5.026900 -2.680700
25% 5.707700 1.986900
50% 6.589400 4.562300
75% 8.578100 7.046700
max 22.203000 24.147000
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - " Population Profit\n", - "count 97.000000 97.000000\n", - "mean 8.159800 5.839135\n", - "std 3.869884 5.510262\n", - "min 5.026900 -2.680700\n", - "25% 5.707700 1.986900\n", - "50% 6.589400 4.562300\n", - "75% 8.578100 7.046700\n", - "max 22.203000 24.147000" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot it to get a better idea of what the data looks like." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data.plot(kind='scatter', x='Population', y='Profit', figsize=(12,8))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's implement linear regression using gradient descent to minimize the cost function. The equations implemented in the following code samples are detailed in \"ex1.pdf\" in the \"exercises\" folder." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we'll create a function to compute the cost of a given solution (characterized by the parameters theta)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def computeCost(X, y, theta):\n", - " inner = np.power(((X * theta.T) - y), 2)\n", - " return np.sum(inner) / (2 * len(X))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's add a column of ones to the training set so we can use a vectorized solution to computing the cost and gradients." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data.insert(0, 'Ones', 1)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's do some variable initialization." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# set X (training data) and y (target variable)\n", - "cols = data.shape[1]\n", - "X = data.iloc[:,0:cols-1]\n", - "y = data.iloc[:,cols-1:cols]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look to make sure X (training set) and y (target variable) look correct." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X.head()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - " Ones Population\n", - "0 1 6.1101\n", - "1 1 5.5277\n", - "2 1 8.5186\n", - "3 1 7.0032\n", - "4 1 5.8598" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "y.head()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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1 9.1302
2 13.6620
3 11.8540
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - " Profit\n", - "0 17.5920\n", - "1 9.1302\n", - "2 13.6620\n", - "3 11.8540\n", - "4 6.8233" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cost function is expecting numpy matrices so we need to convert X and y before we can use them. We also need to initialize theta." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X = np.matrix(X.values)\n", - "y = np.matrix(y.values)\n", - "theta = np.matrix(np.array([0,0]))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's what theta looks like." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "theta" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "matrix([[0, 0]])" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a quick look at the shape of our matrices." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X.shape, theta.shape, y.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "((97L, 2L), (1L, 2L), (97L, 1L))" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's compute the cost for our initial solution (0 values for theta)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "computeCost(X, y, theta)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "32.072733877455676" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far so good. Now we need to define a function to perform gradient descent on the parameters theta using the update rules defined in the text." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def gradientDescent(X, y, theta, alpha, iters):\n", - " temp = np.matrix(np.zeros(theta.shape))\n", - " parameters = int(theta.ravel().shape[1])\n", - " cost = np.zeros(iters)\n", - " \n", - " for i in range(iters):\n", - " error = (X * theta.T) - y\n", - " \n", - " for j in range(parameters):\n", - " term = np.multiply(error, X[:,j])\n", - " temp[0,j] = theta[0,j] - ((alpha / len(X)) * np.sum(term))\n", - " \n", - " theta = temp\n", - " cost[i] = computeCost(X, y, theta)\n", - " \n", - " return theta, cost" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize some additional variables - the learning rate alpha, and the number of iterations to perform." - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Exercise 1 - Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook covers a Python-based solution for the first programming exercise of the machine learning class on Coursera." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the first part of the exercise, we're tasked with implementing linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing some libraries and examining the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "alpha = 0.01\n", - "iters = 1000" - ], - "language": "python", + "data": { + "text/html": [ + "
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1 5.5277 9.1302
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" + ], + "text/plain": [ + " Population Profit\n", + "0 6.1101 17.5920\n", + "1 5.5277 9.1302\n", + "2 8.5186 13.6620\n", + "3 7.0032 11.8540\n", + "4 5.8598 6.8233" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [], - "prompt_number": 15 - }, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "path = os.getcwd() + '\\data\\ex1data1.txt'\n", + "data = pd.read_csv(path, header=None, names=['Population', 'Profit'])\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "
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PopulationProfit
count 97.000000 97.000000
mean 8.159800 5.839135
std 3.869884 5.510262
min 5.026900 -2.680700
25% 5.707700 1.986900
50% 6.589400 4.562300
75% 8.578100 7.046700
max 22.203000 24.147000
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" + ], + "text/plain": [ + " Population Profit\n", + "count 97.000000 97.000000\n", + "mean 8.159800 5.839135\n", + "std 3.869884 5.510262\n", + "min 5.026900 -2.680700\n", + "25% 5.707700 1.986900\n", + "50% 6.589400 4.562300\n", + "75% 8.578100 7.046700\n", + "max 22.203000 24.147000" + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "Now let's run the gradient descent algorithm to fit our parameters theta to the training set." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot it to get a better idea of what the data looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "g, cost = gradientDescent(X, y, theta, alpha, iters)\n", - "g" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "matrix([[-3.24140214, 1.1272942 ]])" - ] - } - ], - "prompt_number": 16 + "output_type": "execute_result" }, { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAtEAAAHuCAYAAABZDVDiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+Q5GldJ/j3Mwwzlj/YZoAdkGVtdvEO92TtmfFwL/xB\n", + "7UpVobvX0nYELupGNYe2RrjISuI1LCHOYbQ4aAEhxkq0gpZ6gUvsWG7rrpNdS1AcFZ6iOAMoIGJM\n", + "c+g6I+PAClpBq/PcH5k1Xd3z7e7K6vz1zXy9Iiqmvt/Kynwy+XTyyafe3+cptdYAAAD7d8OkBwAA\n", + "AG2jiQYAgAFpogEAYECaaAAAGJAmGgAABqSJBgCAAY28iS6lfEEp5bdLKfeVUj5cSnl9//wtpZTN\n", + "UsrHSinnSimHRj0WAAAYhjKOdaJLKV9Ya/3rUsqNSbaTvDLJ0SQP1VrfUEo5leSJtdZXjXwwAABw\n", + "ncYS56i1/nX/25uSPC7Jp9Nrotf759eTvHAcYwEAgOs1lia6lHJDKeW+JA8meXet9Q+S3FprfbB/\n", + "kweT3DqOsQAAwPW6cRwPUmt9JMmRUsrfS9Itpfzzy35eSymPyZU0nQMAgFGotZb93nYsTfSuWuv/\n", + 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}, + "output_type": "display_data" + } + ], + "source": [ + "data.plot(kind='scatter', x='Population', y='Profit', figsize=(12,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's implement linear regression using gradient descent to minimize the cost function. The equations implemented in the following code samples are detailed in \"ex1.pdf\" in the \"exercises\" folder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll create a function to compute the cost of a given solution (characterized by the parameters theta)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def computeCost(X, y, theta):\n", + " inner = np.power(((X * theta.T) - y), 2)\n", + " return np.sum(inner) / (2 * len(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add a column of ones to the training set so we can use a vectorized solution to computing the cost and gradients." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data.insert(0, 'Ones', 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's do some variable initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set X (training data) and y (target variable)\n", + "cols = data.shape[1]\n", + "X = data.iloc[:,0:cols-1]\n", + "y = data.iloc[:,cols-1:cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look to make sure X (training set) and y (target variable) look correct." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "computeCost(X, y, g)" - ], - "language": "python", + "data": { + "text/html": [ + "
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OnesPopulation
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" + ], + "text/plain": [ + " Profit\n", + "0 17.5920\n", + "1 9.1302\n", + "2 13.6620\n", + "3 11.8540\n", + "4 6.8233" + ] + }, + "execution_count": 9, "metadata": {}, - "source": [ - "Now let's plot the linear model along with the data to visually see how well it fits." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "y.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cost function is expecting numpy matrices so we need to convert X and y before we can use them. We also need to initialize theta." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = np.matrix(X.values)\n", + "y = np.matrix(y.values)\n", + "theta = np.matrix(np.array([0,0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what theta looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = np.linspace(data.Population.min(), data.Population.max(), 100)\n", - "f = g[0, 0] + (g[0, 1] * x)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.plot(x, f, 'r', label='Prediction')\n", - "ax.scatter(data.Population, data.Profit, label='Traning Data')\n", - "ax.legend(loc=2)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('Profit')\n", - "ax.set_title('Predicted Profit vs. Population Size')" - ], - "language": "python", + "data": { + "text/plain": [ + "matrix([[0, 0]])" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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mTZtW+5zBgwfTsWNHNt98c/bff39eeeWVnF8TSZKkYtRsl/1erZT6h0MIPPTQQxxwwAFf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\nVSxbVk0+XgMXbpEkrcwQrRYvHxfSNec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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "theta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at the shape of our matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "((97L, 2L), (1L, 2L), (97L, 1L))" + ] + }, + "execution_count": 12, "metadata": {}, - "source": [ - "Looks pretty good! Since the gradient decent function also outputs a vector with the cost at each training iteration, we can plot that as well. Notice that the cost always decreases - this is an example of a convex optimization problem." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, theta.shape, y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the cost for our initial solution (0 values for theta)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.plot(np.arange(iters), cost, 'r')\n", - "ax.set_xlabel('Iterations')\n", - "ax.set_ylabel('Cost')\n", - "ax.set_title('Error vs. Training Epoch')" - ], - "language": "python", + "data": { + "text/plain": [ + "32.072733877455676" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 19 - }, + "output_type": "execute_result" + } + ], + "source": [ + "computeCost(X, y, theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far so good. Now we need to define a function to perform gradient descent on the parameters theta using the update rules defined in the text." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def gradientDescent(X, y, theta, alpha, iters):\n", + " temp = np.matrix(np.zeros(theta.shape))\n", + " parameters = int(theta.ravel().shape[1])\n", + " cost = np.zeros(iters)\n", + " \n", + " for i in range(iters):\n", + " error = (X * theta.T) - y\n", + " \n", + " for j in range(parameters):\n", + " term = np.multiply(error, X[:,j])\n", + " temp[0,j] = theta[0,j] - ((alpha / len(X)) * np.sum(term))\n", + " \n", + " theta = temp\n", + " cost[i] = computeCost(X, y, theta)\n", + " \n", + " return theta, cost" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize some additional variables - the learning rate alpha, and the number of iterations to perform." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "alpha = 0.01\n", + "iters = 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's run the gradient descent algorithm to fit our parameters theta to the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, + "data": { + "text/plain": [ + "matrix([[-3.24140214, 1.1272942 ]])" + ] + }, + "execution_count": 16, "metadata": {}, - "source": [ - "Part 2" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "g, cost = gradientDescent(X, y, theta, alpha, iters)\n", + "g" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can compute the cost (error) of the trained model using our fitted parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "4.5159555030789118" + ] + }, + "execution_count": 17, "metadata": {}, - "source": [ - "Exercise 1 also included a housing price data set with 2 variables (size of the house in square feet and number of bedrooms) and a target (price of the house). Let's use the techniques we already applied to analyze that data set as well." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "computeCost(X, y, g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's plot the linear model along with the data to visually see how well it fits." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "path = os.getcwd() + '\\data\\ex1data2.txt'\n", - "data2 = pd.read_csv(path, header=None, names=['Size', 'Bedrooms', 'Price'])\n", - "data2.head()" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "html": [ - "
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This is very easy with pandas." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(data.Population.min(), data.Population.max(), 100)\n", + "f = g[0, 0] + (g[0, 1] * x)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.plot(x, f, 'r', label='Prediction')\n", + "ax.scatter(data.Population, data.Profit, label='Traning Data')\n", + "ax.legend(loc=2)\n", + "ax.set_xlabel('Population')\n", + "ax.set_ylabel('Profit')\n", + "ax.set_title('Predicted Profit vs. Population Size')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks pretty good! Since the gradient decent function also outputs a vector with the cost at each training iteration, we can plot that as well. Notice that the cost always decreases - this is an example of a convex optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "data2 = (data2 - data2.mean()) / data2.std()\n", - "data2.head()" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "html": [ - "
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regression procedure on the new data set." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.plot(np.arange(iters), cost, 'r')\n", + "ax.set_xlabel('Iterations')\n", + "ax.set_ylabel('Cost')\n", + "ax.set_title('Error vs. Training Epoch')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise 1 also included a housing price data set with 2 variables (size of the house in square feet and number of bedrooms) and a target (price of the house). Let's use the techniques we already applied to analyze that data set as well." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add ones column\n", - "data2.insert(0, 'Ones', 1)\n", - "\n", - "# set X (training data) and y (target variable)\n", - "cols = data2.shape[1]\n", - "X2 = data2.iloc[:,0:cols-1]\n", - "y2 = data2.iloc[:,cols-1:cols]\n", - "\n", - "# convert to matrices and initialize theta\n", - "X2 = np.matrix(X2.values)\n", - "y2 = np.matrix(y2.values)\n", - "theta2 = np.matrix(np.array([0,0,0]))\n", - "\n", - "# perform linear regression on the data set\n", - "g2, cost2 = gradientDescent(X2, y2, theta2, alpha, iters)\n", - "\n", - "# get the cost (error) of the model\n", - "computeCost(X2, y2, g2)" - ], - "language": "python", + "data": { + "text/html": [ + "
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SizeBedroomsPrice
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1 1600 3 329900
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" + ], + "text/plain": [ + " Size Bedrooms Price\n", + "0 2104 3 399900\n", + "1 1600 3 329900\n", + "2 2400 3 369000\n", + "3 1416 2 232000\n", + "4 3000 4 539900" + ] + }, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "0.13070336960771897" - ] - } - ], - "prompt_number": 22 - }, + "output_type": "execute_result" + } + ], + "source": [ + "path = os.getcwd() + '\\data\\ex1data2.txt'\n", + "data2 = pd.read_csv(path, header=None, names=['Size', 'Bedrooms', 'Price'])\n", + "data2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this task we add another pre-processing step - normalizing the features. This is very easy with pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "
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SizeBedroomsPrice
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2 0.502476-0.223675 0.228626
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" + ], + "text/plain": [ + " Size Bedrooms Price\n", + "0 0.130010 -0.223675 0.475747\n", + "1 -0.504190 -0.223675 -0.084074\n", + "2 0.502476 -0.223675 0.228626\n", + "3 -0.735723 -1.537767 -0.867025\n", + "4 1.257476 1.090417 1.595389" + ] + }, + "execution_count": 21, "metadata": {}, - "source": [ - "We can take a quick look at the training progess for this one as well." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = (data2 - data2.mean()) / data2.std()\n", + "data2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's repeat our pre-processing steps from part 1 and run the linear regression procedure on the new data set." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.plot(np.arange(iters), cost2, 'r')\n", - "ax.set_xlabel('Iterations')\n", - "ax.set_ylabel('Cost')\n", - "ax.set_title('Error vs. Training Epoch')" - ], - "language": "python", + "data": { + "text/plain": [ + "0.13070336960771897" + ] + }, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 23 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# add ones column\n", + "data2.insert(0, 'Ones', 1)\n", + "\n", + "# set X (training data) and y (target variable)\n", + "cols = data2.shape[1]\n", + "X2 = data2.iloc[:,0:cols-1]\n", + "y2 = data2.iloc[:,cols-1:cols]\n", + "\n", + "# convert to matrices and initialize theta\n", + "X2 = np.matrix(X2.values)\n", + "y2 = np.matrix(y2.values)\n", + "theta2 = np.matrix(np.array([0,0,0]))\n", + "\n", + "# perform linear regression on the data set\n", + "g2, cost2 = gradientDescent(X2, y2, theta2, alpha, iters)\n", + "\n", + "# get the cost (error) of the model\n", + "computeCost(X2, y2, g2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take a quick look at the training progess for this one as well." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, "metadata": {}, - "source": [ - "Instead of implementing these algorithms from scratch, we could also use scikit-learn's linear regression function. Let's apply scikit-learn's linear regressio algorithm to the data from part 1 and see what it comes up with." - ] + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn import linear_model\n", - "model = linear_model.LinearRegression()\n", - "model.fit(X, y)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAt8AAAH4CAYAAACSS4HXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmUXXWZ7//3kwoJZGAIo4RoCARkigIaQRTLATsoCEoj\n", + "ouBt24u0NoraehG7V5P+dV+HbsffD/VCN6LigC0iggoCaqlXmUUIkEgAw6wQJglhyPD8/tinyElR\n", + "c52z9xner7XOqn32dJ5K7ZV86ptnf3dkJpIkSZKab1LVBUiSJEndwvAtSZIklcTwLUmSJJXE8C1J\n", + "kiSVxPAtSZIklcTwLUmSJJXE8C1JXSoiTo2I/2z0vq0uIuZGxPqI8N9ASaUL5/mW1KkiYgWwHbCu\n", + "bvXZmfmBaiqauIi4GHhF7e1UIIFnau/Pycz3VVLYBETEemA1xffS718y8zNN+ry5wB3A5Mxc34zP\n", + "kKShTK66AElqogQOy8yfj7RjRPRk5roB6yaNJZyNdf/xyMxD6z7vbODuzPznQWqZnJlrm1lLgy3I\n", + 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- "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" - ] - } - ], - "prompt_number": 24 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.plot(np.arange(iters), cost2, 'r')\n", + "ax.set_xlabel('Iterations')\n", + "ax.set_ylabel('Cost')\n", + "ax.set_title('Error vs. Training Epoch')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of implementing these algorithms from scratch, we could also use scikit-learn's linear regression function. Let's apply scikit-learn's linear regressio algorithm to the data from part 1 and see what it comes up with." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" + ] + }, + "execution_count": 24, "metadata": {}, - "source": [ - "Here's what the scikit-learn model's predictions look like." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import linear_model\n", + "model = linear_model.LinearRegression()\n", + "model.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the scikit-learn model's predictions look like." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = np.array(X[:, 1].A1)\n", - "f = model.predict(X).flatten()\n", - "\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.plot(x, f, 'r', label='Prediction')\n", - "ax.scatter(data.Population, data.Profit, label='Traning Data')\n", - "ax.legend(loc=2)\n", - "ax.set_xlabel('Population')\n", - "ax.set_ylabel('Profit')\n", - "ax.set_title('Predicted Profit vs. Population Size')" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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oG2/0+54J0DmJnmgAAIB0O+MM6X//d9d2Swt9zzmOkWgAAIB0qa/3w3JrgP7n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\nEI2cET+C2VkAlRL3A6ddfHgePFjasEF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Thanks for reading. In Exercise 2 we'll take a look at logistic regression for classification problems." - ] + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "x = np.array(X[:, 1].A1)\n", + "f = model.predict(X).flatten()\n", + "\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.plot(x, f, 'r', label='Prediction')\n", + "ax.scatter(data.Population, data.Profit, label='Traning Data')\n", + "ax.legend(loc=2)\n", + "ax.set_xlabel('Population')\n", + "ax.set_ylabel('Profit')\n", + "ax.set_title('Predicted Profit vs. Population Size')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Thanks for reading. In Exercise 2 we'll take a look at logistic regression for classification problems." + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ML-Exercise2.ipynb b/ML-Exercise2.ipynb index c363014..16a4190 100644 --- a/ML-Exercise2.ipynb +++ b/ML-Exercise2.ipynb @@ -1,1094 +1,2010 @@ { - "metadata": { - "name": "", - "signature": "sha256:b9394346745bbe6f8a01e2833163b10bcd482eb8f212f6a87cd6ae142cbd43e4" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Machine Learning Exercise 2 - Logistic Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook covers a Python-based solution for the second programming exercise of the machine learning class on Coursera." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Part 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the first part of this exercise, we'll build a logistic regression model to predict whether a student gets admitted to a university. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Suppose that you are the administrator of a university department and you want to determine each applicant's chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant's scores on two exams and the admissions decision. To accomplish this, we're going to build a classification model that estimates the probability of admission based on the exam scores." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by examining the data." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "path = os.getcwd() + '\\data\\ex2data1.txt'\n", - "data = pd.read_csv(path, header=None, names=['Exam 1', 'Exam 2', 'Admitted'])\n", - "data.head()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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Exam 1Exam 2Admitted
0 34.623660 78.024693 0
1 30.286711 43.894998 0
2 35.847409 72.902198 0
3 60.182599 86.308552 1
4 79.032736 75.344376 1
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - " Exam 1 Exam 2 Admitted\n", - "0 34.623660 78.024693 0\n", - "1 30.286711 43.894998 0\n", - "2 35.847409 72.902198 0\n", - "3 60.182599 86.308552 1\n", - "4 79.032736 75.344376 1" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create a scatter plot of the two scores and use color coding to visualize if the example is positive (admitted) or negative (not admitted)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "positive = data[data['Admitted'].isin([1])]\n", - "negative = data[data['Admitted'].isin([0])]\n", - "\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.scatter(positive['Exam 1'], positive['Exam 2'], s=50, c='b', marker='o', label='Admitted')\n", - "ax.scatter(negative['Exam 1'], negative['Exam 2'], s=50, c='r', marker='x', label='Not Admitted')\n", - "ax.legend()\n", - "ax.set_xlabel('Exam 1 Score')\n", - "ax.set_ylabel('Exam 2 Score')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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KoQrb60E48Kupd+jBBjwSll4S5K+Ue5jCNvgqbK8H4cCvpsFEDzYADFMp9jCF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\n+AYBG0BxUOcPHCKI9cvJZFJlZeXq7u6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TVTeM2HyTc5q3BfYgJ6S5f0l2SnINXR6XVNV1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\nAluSJEnq0f8ButxExp+t9tQAAAAASUVORK5CYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like there is a clear decision boundary between the two classes. Now we need to implement logistic regression so we can train a model to predict the outcome. The equations implemented in the following code samples are detailed in \"ex2.pdf\" in the \"exercises\" folder." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we need to create a sigmoid function. The code for this is pretty simple." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def sigmoid(z):\n", - " return 1 / (1 + np.exp(-z))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's do a quick sanity check to make sure the function is working." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nums = np.arange(-10, 10, step=1)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.plot(nums, sigmoid(nums), 'r')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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X2HTTtqsB6BgjEwBMzznnJIceKgwDI08gBhhVVpcASGJkAmA0PfBAsvvuyT33JLNnt10N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\nRo3uMMDQ0yEGWJu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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Excellent! Now we need to write the cost function to evaluate a solution." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def cost(theta, X, y):\n", - " theta = np.matrix(theta)\n", - " X = np.matrix(X)\n", - " y = np.matrix(y)\n", - " first = np.multiply(-y, np.log(sigmoid(X * theta.T)))\n", - " second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))\n", - " return np.sum(first - second) / (len(X))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to do some setup, similar to what we did in exercise 1 for linear regression." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add a ones column - this makes the matrix multiplication work out easier\n", - "data.insert(0, 'Ones', 1)\n", - "\n", - "# set X (training data) and y (target variable)\n", - "cols = data.shape[1]\n", - "X = data.iloc[:,0:cols-1]\n", - "y = data.iloc[:,cols-1:cols]\n", - "\n", - "# convert to numpy arrays and initalize the parameter array theta\n", - "X = np.array(X.values)\n", - "y = np.array(y.values)\n", - "theta = np.zeros(3)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's quickly check the shape of our arrays to make sure everything looks good." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X.shape, theta.shape, y.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "((100L, 3L), (3L,), (100L, 1L))" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's compute the cost for our initial solution (0 values for theta)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cost(theta, X, y)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "0.69314718055994529" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good. Next we need a function to compute the gradient (parameter updates) given our training data, labels, and some parameters theta." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def gradient(theta, X, y):\n", - " theta = np.matrix(theta)\n", - " X = np.matrix(X)\n", - " y = np.matrix(y)\n", - " \n", - " parameters = int(theta.ravel().shape[1])\n", - " grad = np.zeros(parameters)\n", - " \n", - " error = sigmoid(X * theta.T) - y\n", - " \n", - " for i in range(parameters):\n", - " term = np.multiply(error, X[:,i])\n", - " grad[i] = np.sum(term) / len(X)\n", - " \n", - " return grad" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we don't actually perform gradient descent in this function - we just compute a single gradient step. In the exercise, an Octave function called \"fminunc\" is used to optimize the parameters given functions to compute the cost and the gradients. Since we're using Python, we can use SciPy's \"optimize\" namespace to do the same thing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at a single call to the gradient method using our data and initial paramter values of 0." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gradient(theta, X, y)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "array([ -0.1 , -12.00921659, -11.26284221])" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use SciPy's truncated newton (TNC) implementation to find the optimal parameters." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import scipy.optimize as opt\n", - "result = opt.fmin_tnc(func=cost, x0=theta, fprime=gradient, args=(X, y))\n", - "result" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "(array([-25.87355624, 0.21193682, 0.20722586]), 51, 1)" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what the our cost looks like with this solution." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cost(result[0], X, y)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "0.20357134412164668" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we need to write a function that will output predictions for a dataset X using our learned parameters theta. We can then use this function to score the training accuracy of our classifier." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def predict(theta, X):\n", - " probability = sigmoid(X * theta.T)\n", - " return [1 if x >= 0.5 else 0 for x in probability]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "theta_min = np.matrix(result[0])\n", - "predictions = predict(theta_min, X)\n", - "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]\n", - "accuracy = (sum(map(int, correct)) % len(correct))\n", - "print 'accuracy = {0}%'.format(accuracy)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "accuracy = 89%\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our logistic regression classifer correctly predicted if a student was admitted or not 89% of the time. Not bad! Keep in mind that this is training set accuracy though. We didn't keep a hold-out set or use cross-validation to get a true approximation of the accuracy so this number is likely higher than its true perfomance (this topic is covered in a later exercise)." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Part 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the second part of this exercise, we'll improve our logistic regression algorithm from part one by adding a regularization term. If you're not familiar with regularization, or would like some background on the equations used in this section, refer to \"ex2.pdf\" in the \"exercises\" folder. In a nutshell, regularization is a term in the cost function that causes the algorithm to prefer \"simpler\" models (in this case, models will smaller coefficients). The theory is that this helps to minimize overfitting and improve the model's ability to generalize. With that, let's get started." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to part 1, let's start by visualizing the data." - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Exercise 2 - Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook covers a Python-based solution for the second programming exercise of the machine learning class on Coursera." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the first part of this exercise, we'll build a logistic regression model to predict whether a student gets admitted to a university. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose that you are the administrator of a university department and you want to determine each applicant's chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant's scores on two exams and the admissions decision. To accomplish this, we're going to build a classification model that estimates the probability of admission based on the exam scores." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by examining the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "path = os.getcwd() + '\\data\\ex2data2.txt'\n", - "data2 = pd.read_csv(path, header=None, names=['Test 1', 'Test 2', 'Accepted'])\n", - "data2.head()" - ], - "language": "python", + "data": { + "text/html": [ + "
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Exam 1Exam 2Admitted
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" + ], + "text/plain": [ + " Exam 1 Exam 2 Admitted\n", + "0 34.623660 78.024693 0\n", + "1 30.286711 43.894998 0\n", + "2 35.847409 72.902198 0\n", + "3 60.182599 86.308552 1\n", + "4 79.032736 75.344376 1" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "html": [ - "
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Test 1Test 2Accepted
0 0.051267 0.69956 1
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2-0.213710 0.69225 1
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - " Test 1 Test 2 Accepted\n", - "0 0.051267 0.69956 1\n", - "1 -0.092742 0.68494 1\n", - "2 -0.213710 0.69225 1\n", - "3 -0.375000 0.50219 1\n", - "4 -0.513250 0.46564 1" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "path = os.getcwd() + '\\data\\ex2data1.txt'\n", + "data = pd.read_csv(path, header=None, names=['Exam 1', 'Exam 2', 'Admitted'])\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a scatter plot of the two scores and use color coding to visualize if the example is positive (admitted) or negative (not admitted)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "positive = data2[data2['Accepted'].isin([1])]\n", - "negative = data2[data2['Accepted'].isin([0])]\n", - "\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.scatter(positive['Test 1'], positive['Test 2'], s=50, c='b', marker='o', label='Accepted')\n", - "ax.scatter(negative['Test 1'], negative['Test 2'], s=50, c='r', marker='x', label='Rejected')\n", - "ax.legend()\n", - "ax.set_xlabel('Test 1 Score')\n", - "ax.set_ylabel('Test 2 Score')" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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JYAzMZDAx5ZpNJBKJUGwjbxU7kwxrKACBRMGNUGMmAyCcKnYmmfQ0pOkiNl3c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\nLYrFNqmqarqqqqYrFttU9mJbktauvUU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In particular, you'll notice that there is no linear decision boundary that will perform well on this data. One way to deal with this using a linear technique like logistic regression is to construct features that are derived from polynomials of the original features. Let's start by creating a bunch of polynomial features." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "positive = data[data['Admitted'].isin([1])]\n", + "negative = data[data['Admitted'].isin([0])]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.scatter(positive['Exam 1'], positive['Exam 2'], s=50, c='b', marker='o', label='Admitted')\n", + "ax.scatter(negative['Exam 1'], negative['Exam 2'], s=50, c='r', marker='x', label='Not Admitted')\n", + "ax.legend()\n", + "ax.set_xlabel('Exam 1 Score')\n", + "ax.set_ylabel('Exam 2 Score')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like there is a clear decision boundary between the two classes. Now we need to implement logistic regression so we can train a model to predict the outcome. The equations implemented in the following code samples are detailed in \"ex2.pdf\" in the \"exercises\" folder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to create a sigmoid function. The code for this is pretty simple." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " return 1 / (1 + np.exp(-z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's do a quick sanity check to make sure the function is working." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "degree = 5\n", - "x1 = data2['Test 1']\n", - "x2 = data2['Test 2']\n", - "\n", - "data2.insert(3, 'Ones', 1)\n", - "\n", - "for i in range(1, degree):\n", - " for j in range(0, i):\n", - " data2['F' + str(i) + str(j)] = np.power(x1, i-j) * np.power(x2, j)\n", - "\n", - "data2.drop('Test 1', axis=1, inplace=True)\n", - "data2.drop('Test 2', axis=1, inplace=True)\n", - "\n", - "data2.head()" - ], - "language": "python", + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "html": [ - "
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AcceptedOnesF10F20F21F30F31F32F40F41F42F43
0 1 1 0.051267 0.002628 0.035864 0.000135 0.001839 0.025089 0.000007 0.000094 0.001286 0.017551
1 1 1-0.092742 0.008601-0.063523-0.000798 0.005891-0.043509 0.000074-0.000546 0.004035-0.029801
2 1 1-0.213710 0.045672-0.147941-0.009761 0.031616-0.102412 0.002086-0.006757 0.021886-0.070895
3 1 1-0.375000 0.140625-0.188321-0.052734 0.070620-0.094573 0.019775-0.026483 0.035465-0.047494
4 1 1-0.513250 0.263426-0.238990-0.135203 0.122661-0.111283 0.069393-0.062956 0.057116-0.051818
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First the cost function:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "nums = np.arange(-10, 10, step=1)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.plot(nums, sigmoid(nums), 'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent! Now we need to write the cost function to evaluate a solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def cost(theta, X, y):\n", + " theta = np.matrix(theta)\n", + " X = np.matrix(X)\n", + " y = np.matrix(y)\n", + " first = np.multiply(-y, np.log(sigmoid(X * theta.T)))\n", + " second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))\n", + " return np.sum(first - second) / (len(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to do some setup, similar to what we did in exercise 1 for linear regression." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add a ones column - this makes the matrix multiplication work out easier\n", + "data.insert(0, 'Ones', 1)\n", + "\n", + "# set X (training data) and y (target variable)\n", + "cols = data.shape[1]\n", + "X = data.iloc[:,0:cols-1]\n", + "y = data.iloc[:,cols-1:cols]\n", + "\n", + "# convert to numpy arrays and initalize the parameter array theta\n", + "X = np.array(X.values)\n", + "y = np.array(y.values)\n", + "theta = np.zeros(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's quickly check the shape of our arrays to make sure everything looks good." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "def costReg(theta, X, y, learningRate):\n", - " theta = np.matrix(theta)\n", - " X = np.matrix(X)\n", - " y = np.matrix(y)\n", - " first = np.multiply(-y, np.log(sigmoid(X * theta.T)))\n", - " second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))\n", - " reg = (learningRate / 2 * len(X)) * np.sum(np.power(theta[:,1:theta.shape[1]], 2))\n", - " return np.sum(first - second) / (len(X)) + reg" - ], - "language": "python", + "data": { + "text/plain": [ + "((100L, 3L), (3L,), (100L, 1L))" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, theta.shape, y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compute the cost for our initial solution (0 values for theta)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "0.69314718055994529" + ] + }, + "execution_count": 9, "metadata": {}, - "source": [ - "Notice the \"reg\" term in the equation. Also note the addition of a \"learning rate\" parameter. This is a hyperparameter that controls the effectiveness of the regularization term. Now we need to add regularization to the gradient function:" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "cost(theta, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good. Next we need a function to compute the gradient (parameter updates) given our training data, labels, and some parameters theta." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def gradient(theta, X, y):\n", + " theta = np.matrix(theta)\n", + " X = np.matrix(X)\n", + " y = np.matrix(y)\n", + " \n", + " parameters = int(theta.ravel().shape[1])\n", + " grad = np.zeros(parameters)\n", + " \n", + " error = sigmoid(X * theta.T) - y\n", + " \n", + " for i in range(parameters):\n", + " term = np.multiply(error, X[:,i])\n", + " grad[i] = np.sum(term) / len(X)\n", + " \n", + " return grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we don't actually perform gradient descent in this function - we just compute a single gradient step. In the exercise, an Octave function called \"fminunc\" is used to optimize the parameters given functions to compute the cost and the gradients. Since we're using Python, we can use SciPy's \"optimize\" namespace to do the same thing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at a single call to the gradient method using our data and initial paramter values of 0." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "def gradientReg(theta, X, y, learningRate):\n", - " theta = np.matrix(theta)\n", - " X = np.matrix(X)\n", - " y = np.matrix(y)\n", - " \n", - " parameters = int(theta.ravel().shape[1])\n", - " grad = np.zeros(parameters)\n", - " \n", - " error = sigmoid(X * theta.T) - y\n", - " \n", - " for i in range(parameters):\n", - " term = np.multiply(error, X[:,i])\n", - " \n", - " if (i == 0):\n", - " grad[i] = np.sum(term) / len(X)\n", - " else:\n", - " grad[i] = (np.sum(term) / len(X)) + ((learningRate / len(X)) * theta[:,i])\n", - " \n", - " return grad" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ -0.1 , -12.00921659, -11.26284221])" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, + "output_type": "execute_result" + } + ], + "source": [ + "gradient(theta, X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use SciPy's truncated newton (TNC) implementation to find the optimal parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "(array([-25.87355624, 0.21193682, 0.20722586]), 51, 1)" + ] + }, + "execution_count": 12, "metadata": {}, - "source": [ - "Initialize variables like we did in part 1." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "import scipy.optimize as opt\n", + "result = opt.fmin_tnc(func=cost, x0=theta, fprime=gradient, args=(X, y))\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what the our cost looks like with this solution." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# set X and y (remember from above that we moved the label to column 0)\n", - "cols = data2.shape[1]\n", - "X2 = data2.iloc[:,1:cols]\n", - "y2 = data2.iloc[:,0:1]\n", - "\n", - "# convert to numpy arrays and initalize the parameter array theta\n", - "X2 = np.array(X2.values)\n", - "y2 = np.array(y2.values)\n", - "theta2 = np.zeros(11)" - ], - "language": "python", + "data": { + "text/plain": [ + "0.20357134412164668" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, + "output_type": "execute_result" + } + ], + "source": [ + "cost(result[0], X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need to write a function that will output predictions for a dataset X using our learned parameters theta. We can then use this function to score the training accuracy of our classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def predict(theta, X):\n", + " probability = sigmoid(X * theta.T)\n", + " return [1 if x >= 0.5 else 0 for x in probability]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's initialize our learning rate to a sensible value. We can play with this later if necessary (i.e. if the penalization is too strong or not strong enough)." + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy = 89%\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "learningRate = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 22 - }, + } + ], + "source": [ + "theta_min = np.matrix(result[0])\n", + "predictions = predict(theta_min, X)\n", + "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]\n", + "accuracy = (sum(map(int, correct)) % len(correct))\n", + "print 'accuracy = {0}%'.format(accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our logistic regression classifer correctly predicted if a student was admitted or not 89% of the time. Not bad! Keep in mind that this is training set accuracy though. We didn't keep a hold-out set or use cross-validation to get a true approximation of the accuracy so this number is likely higher than its true perfomance (this topic is covered in a later exercise)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the second part of this exercise, we'll improve our logistic regression algorithm from part one by adding a regularization term. If you're not familiar with regularization, or would like some background on the equations used in this section, refer to \"ex2.pdf\" in the \"exercises\" folder. In a nutshell, regularization is a term in the cost function that causes the algorithm to prefer \"simpler\" models (in this case, models will smaller coefficients). The theory is that this helps to minimize overfitting and improve the model's ability to generalize. With that, let's get started." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to part 1, let's start by visualizing the data." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "
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Test 1Test 2Accepted
0 0.051267 0.69956 1
1-0.092742 0.68494 1
2-0.213710 0.69225 1
3-0.375000 0.50219 1
4-0.513250 0.46564 1
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" + ], + "text/plain": [ + " Test 1 Test 2 Accepted\n", + "0 0.051267 0.69956 1\n", + "1 -0.092742 0.68494 1\n", + "2 -0.213710 0.69225 1\n", + "3 -0.375000 0.50219 1\n", + "4 -0.513250 0.46564 1" + ] + }, + "execution_count": 16, "metadata": {}, - "source": [ - "Now let's try calling our new regularized functions with the default (0) values for theta to make sure the calculations are working." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "path = os.getcwd() + '\\data\\ex2data2.txt'\n", + "data2 = pd.read_csv(path, header=None, names=['Test 1', 'Test 2', 'Accepted'])\n", + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "costReg(theta2, X2, y2, learningRate)" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "metadata": {}, - 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"ax.set_xlabel('Test 1 Score')\n", + "ax.set_ylabel('Test 2 Score')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This data looks a bit more complicated than the previous example. In particular, you'll notice that there is no linear decision boundary that will perform well on this data. One way to deal with this using a linear technique like logistic regression is to construct features that are derived from polynomials of the original features. Let's start by creating a bunch of polynomial features." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "
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AcceptedOnesF10F20F21F30F31F32F40F41F42F43
0 1 1 0.051267 0.002628 0.035864 0.000135 0.001839 0.025089 0.000007 0.000094 0.001286 0.017551
1 1 1-0.092742 0.008601-0.063523-0.000798 0.005891-0.043509 0.000074-0.000546 0.004035-0.029801
2 1 1-0.213710 0.045672-0.147941-0.009761 0.031616-0.102412 0.002086-0.006757 0.021886-0.070895
3 1 1-0.375000 0.140625-0.188321-0.052734 0.070620-0.094573 0.019775-0.026483 0.035465-0.047494
4 1 1-0.513250 0.263426-0.238990-0.135203 0.122661-0.111283 0.069393-0.062956 0.057116-0.051818
\n", + "
" + ], + "text/plain": [ + " Accepted Ones F10 F20 F21 F30 F31 F32 \\\n", + "0 1 1 0.051267 0.002628 0.035864 0.000135 0.001839 0.025089 \n", + "1 1 1 -0.092742 0.008601 -0.063523 -0.000798 0.005891 -0.043509 \n", + "2 1 1 -0.213710 0.045672 -0.147941 -0.009761 0.031616 -0.102412 \n", + "3 1 1 -0.375000 0.140625 -0.188321 -0.052734 0.070620 -0.094573 \n", + "4 1 1 -0.513250 0.263426 -0.238990 -0.135203 0.122661 -0.111283 \n", + "\n", + " F40 F41 F42 F43 \n", + "0 0.000007 0.000094 0.001286 0.017551 \n", + "1 0.000074 -0.000546 0.004035 -0.029801 \n", + "2 0.002086 -0.006757 0.021886 -0.070895 \n", + "3 0.019775 -0.026483 0.035465 -0.047494 \n", + "4 0.069393 -0.062956 0.057116 -0.051818 " + ] + }, + "execution_count": 18, "metadata": {}, - "source": [ - "Now we can use the same optimization function from part 1 to compute the optimal solution." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "degree = 5\n", + "x1 = data2['Test 1']\n", + "x2 = data2['Test 2']\n", + "\n", + "data2.insert(3, 'Ones', 1)\n", + "\n", + "for i in range(1, degree):\n", + " for j in range(0, i):\n", + " data2['F' + str(i) + str(j)] = np.power(x1, i-j) * np.power(x2, j)\n", + "\n", + "data2.drop('Test 1', axis=1, inplace=True)\n", + "data2.drop('Test 2', axis=1, inplace=True)\n", + "\n", + "data2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to modify the cost and gradient functions from part 1 to include the regularization term. First the cost function:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def costReg(theta, X, y, learningRate):\n", + " theta = np.matrix(theta)\n", + " X = np.matrix(X)\n", + " y = np.matrix(y)\n", + " first = np.multiply(-y, np.log(sigmoid(X * theta.T)))\n", + " second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))\n", + " reg = (learningRate / 2 * len(X)) * np.sum(np.power(theta[:,1:theta.shape[1]], 2))\n", + " return np.sum(first - second) / (len(X)) + reg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the \"reg\" term in the equation. Also note the addition of a \"learning rate\" parameter. This is a hyperparameter that controls the effectiveness of the regularization term. Now we need to add regularization to the gradient function:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def gradientReg(theta, X, y, learningRate):\n", + " theta = np.matrix(theta)\n", + " X = np.matrix(X)\n", + " y = np.matrix(y)\n", + " \n", + " parameters = int(theta.ravel().shape[1])\n", + " grad = np.zeros(parameters)\n", + " \n", + " error = sigmoid(X * theta.T) - y\n", + " \n", + " for i in range(parameters):\n", + " term = np.multiply(error, X[:,i])\n", + " \n", + " if (i == 0):\n", + " grad[i] = np.sum(term) / len(X)\n", + " else:\n", + " grad[i] = (np.sum(term) / len(X)) + ((learningRate / len(X)) * theta[:,i])\n", + " \n", + " return grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize variables like we did in part 1." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set X and y (remember from above that we moved the label to column 0)\n", + "cols = data2.shape[1]\n", + "X2 = data2.iloc[:,1:cols]\n", + "y2 = data2.iloc[:,0:1]\n", + "\n", + "# convert to numpy arrays and initalize the parameter array theta\n", + "X2 = np.array(X2.values)\n", + "y2 = np.array(y2.values)\n", + "theta2 = np.zeros(11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's initialize our learning rate to a sensible value. We can play with this later if necessary (i.e. if the penalization is too strong or not strong enough)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "learningRate = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try calling our new regularized functions with the default (0) values for theta to make sure the calculations are working." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "result2 = opt.fmin_tnc(func=costReg, x0=theta2, fprime=gradientReg, args=(X2, y2, learningRate))\n", - "result2" - ], - "language": "python", + "data": { + "text/plain": [ + "0.6931471805599454" + ] + }, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "(array([ 0.35872309, -3.22200653, 18.97106363, -4.25297831,\n", - " 18.23053189, 20.36386672, 8.94114455, -43.77439015,\n", - " -17.93440473, -50.75071857, -2.84162964]), 110, 1)" - ] - } - ], - "prompt_number": 25 - }, + "output_type": "execute_result" + } + ], + "source": [ + "costReg(theta2, X2, y2, learningRate)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "array([ 0.00847458, 0.01878809, 0.05034464, 0.01150133, 0.01835599,\n", + " 0.00732393, 0.00819244, 0.03934862, 0.00223924, 0.01286005,\n", + " 0.00309594])" + ] + }, + "execution_count": 24, "metadata": {}, - "source": [ - "Finally, we can use the prediction function from part 1 to see how accurate our solution is on the training data." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "gradientReg(theta2, X2, y2, learningRate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the same optimization function from part 1 to compute the optimal solution." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "theta_min = np.matrix(result2[0])\n", - "predictions = predict(theta_min, X2)\n", - "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y2)]\n", - "accuracy = (sum(map(int, correct)) % len(correct))\n", - "print 'accuracy = {0}%'.format(accuracy)" - ], - "language": "python", + "data": { + "text/plain": [ + "(array([ 0.35872309, -3.22200653, 18.97106363, -4.25297831,\n", + " 18.23053189, 20.36386672, 8.94114455, -43.77439015,\n", + " -17.93440473, -50.75071857, -2.84162964]), 110, 1)" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "accuracy = 91%\n" - ] - } - ], - "prompt_number": 26 - }, + "output_type": "execute_result" + } + ], + "source": [ + "result2 = opt.fmin_tnc(func=costReg, x0=theta2, fprime=gradientReg, args=(X2, y2, learningRate))\n", + "result2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use the prediction function from part 1 to see how accurate our solution is on the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although we implemented these algorithms from scratch, it's worth noting that we could also use a high-level python library like scikit-learn to solve this problem." + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy = 91%\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sklearn import linear_model\n", - "model = linear_model.LogisticRegression(penalty='l2', C=1.0)\n", - "model.fit(X2, y2.ravel())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 27, - "text": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" - ] - } - ], - "prompt_number": 27 - }, + } + ], + "source": [ + "theta_min = np.matrix(result2[0])\n", + "predictions = predict(theta_min, X2)\n", + "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y2)]\n", + "accuracy = (sum(map(int, correct)) % len(correct))\n", + "print 'accuracy = {0}%'.format(accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although we implemented these algorithms from scratch, it's worth noting that we could also use a high-level python library like scikit-learn to solve this problem." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "model.score(X2, y2)" - ], - "language": "python", + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" + ] + }, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "0.66101694915254239" - ] - } - ], - "prompt_number": 28 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import linear_model\n", + "model = linear_model.LogisticRegression(penalty='l2', C=1.0)\n", + "model.fit(X2, y2.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "0.66101694915254239" + ] + }, + "execution_count": 28, "metadata": {}, - "source": [ - "That's all for Exercise 2! Stay tuned for the next exercise where we'll dive into neural networks." - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "model.score(X2, y2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's all for Exercise 2! Stay tuned for the next exercise where we'll dive into neural networks." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Matplotlib.ipynb b/Matplotlib.ipynb index af9b4b5..bee1763 100644 --- a/Matplotlib.ipynb +++ b/Matplotlib.ipynb @@ -1,570 +1,9424 @@ { - "metadata": { - "name": "", - "signature": "sha256:b6de035e1b3eb89933b60d516894f7cfde19d04e0bcc14921272deefe6b5765d" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.\n", - "\n", - "Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# needed to display the graphs\n", - "%matplotlib inline\n", - "from pylab import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = linspace(0, 5, 10)\n", - "y = x ** 2\n", - "\n", - "fig = plt.figure()\n", - "\n", - "# left, bottom, width, height (range 0 to 1)\n", - "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n", - "\n", - "axes.plot(x, y, 'r')\n", - "\n", - "axes.set_xlabel('x')\n", - "axes.set_ylabel('y')\n", - "axes.set_title('title');" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.\n", + "\n", + "Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure()\n", - "\n", - "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n", - "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n", - "\n", - "# main figure\n", - "axes1.plot(x, y, 'r')\n", - "axes1.set_xlabel('x')\n", - "axes1.set_ylabel('y')\n", - "axes1.set_title('title')\n", - "\n", - "# insert\n", - "axes2.plot(y, x, 'g')\n", - "axes2.set_xlabel('y')\n", - "axes2.set_ylabel('x')\n", - "axes2.set_title('insert title');" - ], - "language": 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, + "output_type": "display_data" + } + ], + "source": [ + "x = linspace(0, 5, 10)\n", + "y = x ** 2\n", + "\n", + "fig = plt.figure()\n", + "\n", + "# left, bottom, width, height (range 0 to 1)\n", + "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n", + "\n", + "axes.plot(x, y, 'r')\n", + "\n", + "axes.set_xlabel('x')\n", + "axes.set_ylabel('y')\n", + "axes.set_title('title');" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, axes = plt.subplots(nrows=1, ncols=2)\n", - "\n", - "for ax in axes:\n", - " ax.plot(x, y, 'r')\n", - " ax.set_xlabel('x')\n", - " ax.set_ylabel('y')\n", - " ax.set_title('title')\n", - " \n", - "fig.tight_layout()" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEgCAYAAACuDOSlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "\n", + "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n", + "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n", + "\n", + "# main figure\n", + "axes1.plot(x, y, 'r')\n", + "axes1.set_xlabel('x')\n", + "axes1.set_ylabel('y')\n", + "axes1.set_title('title')\n", + "\n", + "# insert\n", + "axes2.plot(y, x, 'g')\n", + "axes2.set_xlabel('y')\n", + "axes2.set_ylabel('x')\n", + "axes2.set_title('insert title');" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# example with a legend and latex symbols\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n", - "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n", - "ax.legend(loc=2) # upper left corner\n", - 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2)\n", + "\n", + "for ax in axes:\n", + " ax.plot(x, y, 'r')\n", + " ax.set_xlabel('x')\n", + " ax.set_ylabel('y')\n", + " ax.set_title('title')\n", + " \n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# line customization\n", - "fig, ax = plt.subplots(figsize=(12,6))\n", - "\n", - "ax.plot(x, x+1, color=\"blue\", linewidth=0.25)\n", - "ax.plot(x, x+2, color=\"blue\", linewidth=0.50)\n", - "ax.plot(x, x+3, color=\"blue\", linewidth=1.00)\n", - "ax.plot(x, x+4, color=\"blue\", linewidth=2.00)\n", - "\n", - "# possible linestype options \u2018-\u2018, \u2018\u2013\u2019, \u2018-.\u2019, \u2018:\u2019, \u2018steps\u2019\n", - "ax.plot(x, x+5, color=\"red\", lw=2, linestyle='-')\n", - "ax.plot(x, x+6, color=\"red\", lw=2, ls='-.')\n", - "ax.plot(x, x+7, color=\"red\", lw=2, ls=':')\n", - "\n", - "# custom dash\n", - "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n", - "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n", - "\n", - "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', \n", - "# '1', '2', '3', '4', ...\n", - "ax.plot(x, x+ 9, color=\"green\", lw=2, ls='*', marker='+')\n", - "ax.plot(x, x+10, color=\"green\", lw=2, ls='*', marker='o')\n", - "ax.plot(x, x+11, color=\"green\", lw=2, ls='*', marker='s')\n", - "ax.plot(x, x+12, color=\"green\", lw=2, ls='*', marker='1')\n", - "\n", - "# marker size and color\n", - "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n", - "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n", - "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, \n", - " markerfacecolor=\"red\")\n", - "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n", - " markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\");" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEhCAYAAACOZ4wDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0VeW9//H3lzAIGmSeB5m8Ag4MImAH4wBisQJiHao4\n", + "oKLSOoA/q61txXu9Yp1trVZRuNQqgq0KUtAEJIqKYRQZiswIQhjClBCGkHx/f5wDhBDICRn2OTmf\n", + "11pnZZ999j75shdrf/I8z97PNndHRETkRCoFXYCIiEQ/hYWIiBRJYSEiIkVSWIiISJEUFiIiUiSF\n", + "hYiIFElhIVKKzKyFmWWamZ1gmzwza12edYmUlMJCpITMbK2ZXQLg7t+7e6KHb2Ays1Qzuz3YCkVK\n", + "TmEhUnIOHK8lobtepUJQWIiUgJm9BbQAPgp3Pz0U7mZKMLP/BX4CvBz+7M+F7F/NzJ41s3Vmlm5m\n", + "r5rZKeX97xApisJCpATcfRDwPXCluycC7x35yB8FZgK/CndN3VfIVzwFtAXOC/9sCvyx7CsXKR6F\n", + "hUjZK7SLKjwIficw3N13unsWMBK4vjyLE4lE5aALEIkDxxu3qA/UAOblu3jK0B9xEoUUFiIld6JB\n", + "7BN9tg3YC3Rw902lW5JI6dJfMCIltxloU9zP3D0PGAW8aGb1AcysqZn1LpMqRUpAYSFSciOB35vZ\n", + 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lIqJwZSgZlpfXr1M/zkfiLq4JvU4Js6Fg/9nY08KsucOFuesFw2xqYSsuInIy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\n7XHHSc2ldvvx442r1NesMbZimjjReDzmxUKUeLN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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, + "output_type": "display_data" + } + ], + "source": [ + "# example with a legend and latex symbols\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(x, x**2, label=r\"$y = \\alpha^2$\")\n", + "ax.plot(x, x**3, label=r\"$y = \\alpha^3$\")\n", + "ax.legend(loc=2) # upper left corner\n", + "ax.set_xlabel(r'$\\alpha$', fontsize=18)\n", + "ax.set_ylabel(r'$y$', fontsize=18)\n", + "ax.set_title('title');" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# axis controls\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", - "\n", - "axes[0].plot(x, x**2, x, x**3)\n", - "axes[0].set_title(\"default axes ranges\")\n", - "\n", - "axes[1].plot(x, x**2, x, x**3)\n", - "axes[1].axis('tight')\n", - "axes[1].set_title(\"tight axes\")\n", - "\n", - "axes[2].plot(x, x**2, x, x**3)\n", - "axes[2].set_ylim([0, 60])\n", - "axes[2].set_xlim([2, 5])\n", - "axes[2].set_title(\"custom axes range\");" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAr0AAAFwCAYAAAC1usMvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FOXaBvB7CCQEEpoEsoFDR3qzHAsqsRGUY+FT8WBH\n", + "7F1BwUrs4sGKior9HCk2sOCBWAjYK0iTXgInCU1KAoSSzPfHk2FnZmd3Z/vs7P27rlzk3c3uTmDY\n", + "vffd531eRVVVEBERERG5WZ1EHwARERERUawx9BIRERGR6zH0EhEREZHrMfQSERERkesx9BIRERGR\n", + "6zH0EhEREZHrBQy9iqL8TVGUOYqiLFEUZbGiKLfUXl6oKMpGRVHm134Nis/hEhERERGFTgnUp1dR\n", + "lFwAuaqqLlAUJQvAbwDOBTAUQIWqqk/H5zCJiIiIiMJXN9CVqqqWAyiv/b5SUZQ/AbSqvVqJ8bER\n", + "EREREUWF7ZpeRVHaAegH4Mfai25WFOUPRVFeVxSlSQyOjYiIiIgoKmyF3trShg8A3KqqaiWAiQDa\n", + "A+gLoAzAUzE7QiIiIiKiCAWs6QUARVHqAfgMwH9VVX3W4vp2AD5VVbWX6fLAd0xEREREFCWqqgYs\n", + "vQ1Y06soigLgdQBL9YFXURSPqqpltcMhABb5efDQjpZcr7CwEIWFhYk+DHIYnhdkhecFWeF5QVYk\n", + 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cBBitqqdUdRuwGaiVfVGNCV3r965n5287qVeunusoxhjfit0rEIQaxW16XxM8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\n9XyjaN926hQsW2ZM8b59MGWK+Td5zBhoKmvmjZK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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, + "output_type": "display_data" + } + ], + "source": [ + "# line customization\n", + "fig, ax = plt.subplots(figsize=(12,6))\n", + "\n", + "ax.plot(x, x+1, color=\"blue\", linewidth=0.25)\n", + "ax.plot(x, x+2, color=\"blue\", linewidth=0.50)\n", + "ax.plot(x, x+3, color=\"blue\", linewidth=1.00)\n", + "ax.plot(x, x+4, color=\"blue\", linewidth=2.00)\n", + "\n", + "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n", + "ax.plot(x, x+5, color=\"red\", lw=2, linestyle='-')\n", + "ax.plot(x, x+6, color=\"red\", lw=2, ls='-.')\n", + "ax.plot(x, x+7, color=\"red\", lw=2, ls=':')\n", + "\n", + "# custom dash\n", + "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n", + "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n", + "\n", + "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', \n", + "# '1', '2', '3', '4', ...\n", + "ax.plot(x, x+ 9, color=\"green\", lw=2, ls='*', marker='+')\n", + "ax.plot(x, x+10, color=\"green\", lw=2, ls='*', marker='o')\n", + "ax.plot(x, x+11, color=\"green\", lw=2, ls='*', marker='s')\n", + "ax.plot(x, x+12, color=\"green\", lw=2, ls='*', marker='1')\n", + "\n", + "# marker size and color\n", + "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n", + "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n", + "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, \n", + " markerfacecolor=\"red\")\n", + "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n", + " markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\");" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# scaling\n", - "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", - " \n", - "axes[0].plot(x, x**2, x, exp(x))\n", - "axes[0].set_title(\"Normal scale\")\n", - "\n", - "axes[1].plot(x, x**2, x, exp(x))\n", - "axes[1].set_yscale(\"log\")\n", - "axes[1].set_title(\"Logarithmic scale (y)\");" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEKCAYAAAAPY6NUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XucTfX+x/HXB7lFSeVSyS3XQqkUTjV1KEWoVMotFBLd\n", + "OL84dQ7VSQqlKLocGpJLKiF3GblfymXcI0QyOC65DzOf3x9rTe2mMWbGzP7uy+f5eMzD3mutvdd7\n", + "j/1d6zvf9V3fr6gqxhhjjDHGGE8u1wGMMcYYY4wJJVZBNsYYY4wxJoBVkI0xxhhjjAlgFWRjjDHG\n", + "GGMCWAXZGGOMMcaYAFZBNsYYY4wxJoBVkM9CRD4RkVczuG0lEVkpIr+JSJdz3G9vERl5Lu9hjAke\n", + "EblSRA6LiGRg2zIikiwidgw2xpgQZAfns1P/JyP+D5itqheo6uBs2C9gJ1NjQpGIbBORO1Keq+rP\n", + "qlpYs2FwefsD2ZjMEZHHRGSe6xwmcliFK2PO2iLkKw2sy8F9ZjSHc1aZN1FACaMyaYyJTCKSx3WG\n", + "SGSVmFRE5DoR+cHvJjEGyJ9qfSO/G8UBEVkgItX85d8CMcBg/7UVRKShiKwQkUMi8rOI9Ap4nxgR\n", + "2ZHqvf/UIsUfrcjf+f8e9C/h3pRG7loissjPtUtEBonIef66OiKyV0Su8J/XEJH9IlIxvc/kr3tB\n", + 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Vq4mUMSZ1Dp46yJD5Q/jfwv/RslJLJt03iWtKXuN2WH7p8OH/Jk4HD0K9ek7C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\nXeeSpxkzID4eWraEdu2cJfrlPSOnn3/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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, + "output_type": "display_data" + } + ], + "source": [ + "# axis controls\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", + "\n", + "axes[0].plot(x, x**2, x, x**3)\n", + "axes[0].set_title(\"default axes ranges\")\n", + "\n", + "axes[1].plot(x, x**2, x, x**3)\n", + "axes[1].axis('tight')\n", + "axes[1].set_title(\"tight axes\")\n", + "\n", + "axes[2].plot(x, x**2, x, x**3)\n", + "axes[2].set_ylim([0, 60])\n", + "axes[2].set_xlim([2, 5])\n", + "axes[2].set_title(\"custom axes range\");" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# axis grid\n", - "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n", - "\n", - "# default grid appearance\n", - "axes[0].plot(x, x**2, x, x**3, lw=2)\n", - "axes[0].grid(True)\n", - "\n", - "# custom grid appearance\n", - "axes[1].plot(x, x**2, x, x**3, 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hfOB85nnu+vwu1hxaQ/WK1Vn6wFK6N+1udliWIfNMCWFt07dOZ8y3Y9Bonu72\nNP/t9V+UssV/Wa+5U7/kyJQQXjqTdoa+/+vLlhNbuLLqlax8cCXX1b/O7LCEEKJEWmte/vFlJkZP\nBOC1217jnzf90+SoAo+MmfKCnca0SC6eOZFygu4fdWfLiS00rdGUDSM2+KyRstNrIoSwHod2MG7l\nOCZGTyRIBTGz30xppDwkR6aE8NCBMwfoNb8XR5KP0PaKtqx+aDUNqzc0OywhhChRVk4WI5eM5JNf\nPyE0OJRP7/qUe9reY3ZYAUvGTAnhgR0nd9Dn0z4kXkikS8MuLP/zcmpXqW12WJYlY6aEsI6LWRe5\n78v7WLZ/GVUrVGXRkEX0bC7rexdH1uYTwg9+PPIjUfOiSLyQSK/mvfhu6HfSSJUrsjaf1dglD/B/\nLknpSdz+ye0s228stP79sO/91kjZ6XUpiTRTXrDTmBbJxT1Lf1/K7Z/cTkpGCve2vZelDyylWmg1\nvzyXnV4Te4kwOwCfscubnV3yAP/mknA+gaiPolh/dD0NwxqyfsR6Ojfs7Lfns9PrUhKvmimlVLBS\naodSaqlzu5ZSao1Sar9SarVSKtw3YQphvvk75zP488GkZ6fz6PWPsuDuBVQMqWh2WMJDUr9EeRKX\nFMdNc29iZ8JOWtVuxU8jf6LtFW3NDss2vD0y9RSwF8gdQPAMsEZr3QpY69y2LTutnSa5XN60zdMY\numgoOTqH5256jvf7vl+qdfY8YafXxKLKdf0S5cfuxN3cOOdGYs/G0rF+R9aPWE/TcPt8XW0FHjdT\nSqlGwJ3AbCB3YNYAYJ7z+jxgkFfRCWEyrTUvrnuRcavGAfBm7zd55bZXZDK7ACf1S5QXm49vpvvc\n7vyR+ge3NL2FdcPWcWXVK80Oy3a8OTI1Ffg74Mh3W12tdYLzegJQ14vHtzw7jWmRXIpyaAdjvh3D\nyz++TLAKZu7AuUzoNsEnj+0OO70mFlTu65ewv9UHV3Pbx7dxLv0cA64ewMqHVlK9YnWzw7Ilj+aZ\nUkr1AxK11juUUlGu9tFaa6WUy/OHhw8fToRz0Z7w8HAiIyPzvtLIfQOR7bLdzmWVeLzZjomJ8frx\n/nTznxi2aBifLf2MCsEV+OLvXzCw9UBL5BcI27nX4yw4AtXb+gVnGD58Ut5WZGQUkZFRRES4Xoss\nLs71QFwr7B8fD7kvmRXi8XT/+HjjNqvE483+he/j6eOvO7yOV9b/lxxHJ25v2Yc3Oz9NpZCib/n+\nzDciwvzQqWbrAAAbx0lEQVR/T0/2j46OzqtlSUlF93XFo3mmlFKTgYeBbKASUB34GugERGmt45VS\n9YF1WuvWhe4rc7QIS7uQeYF7vriHlbErCQsNY8kDS4iKiDI7rIBmpXmmvKlfzvtLDROWNmPbDB5f\n/jgazfiu43mj9xsEKTl531N+m2dKa/2c1rqx1roZMAT4Xmv9MLAEGObcbRiwyJPHF8Ispy6covcn\nvVkZu5I6Veqwbtg6aaRsRuqXsCuHdvB/P/4fjy1/DI3mlR6v8GbvN6WRKgO++hfO/Zj2GtBLKbUf\n6OHctq3CX5EFsvKei9aahXsW0nZ6WzYe20iTGk3YMGID1ze43vcBuslOr4nFlcv6Jezl4NmD9JjX\ng3+t+xcKxQd9P+C5m5+Tk2XKiNdr82mtfwB+cF4/C8ic9CKgJJxPYPS3o/n6t68B6NGsB/MGzaNR\n9UYmRyb8TeqXCHQO7eCdLe/w7NpnuZh9kSurXsns/rPpf3V/s0MrV2RtPlFuaa35bPdnjFkxhrMX\nzxIWGsYbvd9gVMdR8mnOx6w0ZspbUsOEVew/s5+Ri0fy07GfAHiw/YNM6zNNlrfyMXfql9dHpoQI\nRCdTT/L48sdZ/PtiAHq36M2s/rNoUqOJyZEJ67PPZIfFnQEXaOySB7iXS44jh7c2v8UL614gPTud\netXqMaPfDAZcPaAsQnSbnV6XksioNC/YaUxLeclFa838nfNpN70di39fTPWK1ZndfzYrH1xpuUbK\nTq+JvUSYHYDPWHDmCo/YJQ8oOZd9p/dx09ybeHrN06RnpzO0w1D2jN5juUYK7PW6lESOTIly40TK\nCR5d9ijLDywH4I6WdzCz/0wZGyWEsLxsRzZTNk3hxXUvkpGTQcOwhszoN4O+rfqaHZpAmimv2Gnt\nNDvnorXmo5iPGL9qPMkZydSoWINpfaYxtMNQS4+NstNrIoTw3J7EPYxYPIKtf2wFYGTkSN68/U3C\nK8la3FYhzZSwtWPJx3hk2SOsjF0JQL9W/ZjRbwYNwhqYHJkQQlxetiOb//70X1764SUyczJpVL0R\ns/rPok/LPmaHJgqRMVNesNOYFrvlorVm1i+zaDe9HStjV1KzUk3mD57PkiFLAqaRstNrIoQonV0J\nu+g6uyvPf/88mTmZPNLxEfaM3iONlEXJkSlhO/Hn47n9k9tZc2gNAINaD+L9vu9Tr1o9kyMT9hBn\ndgA+Y5czreySB0DDxlm8/MNrvPzjy2Q5smhaoymzB8ymZ/PAmwLNTq9LSWSeKWEbDu1g5i8z+fua\nv3M+8zy1K9fm3Tvf5f5291t6bFR5IPNMCVGymPgYRiweQUx8DACjbxjNaz1fI6ximMmRlW8yz5Qo\nNw6fO8xflvyFdXHrALin7T28e8e71K1W1+TIhBDi8jJzMnnlx1eYvGEy2Y5smoU348MBH3Jrs1vN\nDk24ScZMecFOY1oCNReHdvDuz+/S/v32rItbxxVVrmBik4l8ce8XAd9IBeprIoRw3/aT2+k0qxP/\n/vHfZDuyebLzk/z6+K/SSAUYOTIlAtbBswcZuWQkPx75EYAh1wzh7T5vs2frHpMjE/llZpodgRDW\nk5Gdwcs/vsxrG14jR+fQomYL5gycQ/em3c0OTeTj7jf6MmZKBJzCC3vWrVqX9/u+z+A2g80OTQB/\n/AGbNl26/PILZGTImCkhcm09sZURi0ew59QeFIpxXcfxfz3+jyoVqpgdWrmXlgbbtl2qX5s3Q0KC\njJkSNiMLe1pLZibExBRsno4eNTsqf5O1+awmUPJIz05nUvQkXt/4Og7toFXtVswZMIcbm9yYt0+g\n5OIOq+eitRFj/vq1cydkZ5f+saSZ8kJ0dLRtZqm2ei6FF/asX60+H/T7wOV6VFbPxV1WzOPkyaJH\nndLTC+4TFgZdu0K3bsalSxeoVcuceP0jwuwAfMbqb3buCoQ8Nh/fzIjFI9h3eh9BKoinuz3Nv2/9\nN5UrVC6wXyDk4i6r5XLxYsGjTps2QUJCwX2CgiAy8lL96tYNrrqq5Mf2qJlSSjUGPgauBDQwU2v9\ntlKqFvA5xke3OOA+rXWSJ88hRK59p/cxYvEINh/fDMCwDsOYevtUalauaXJk9paVVfSo05EjRfdr\n3dooOLkNVNu2EBxc9vGWhtQwUVYuZl3kxXUvMmXzFBzaQZs6bZg7cC5dGnUxOzRb09qoV/nrV0xM\n0aNOtWsXbJw6dYJq1Ur/fB6NmVJK1QPqaa1jlFLVgF+AQcAI4LTW+r9KqX8CNbXWzxS6r4w3EG45\nmnyUt7e8zbs/v5u3sOfM/jO586o7zQ7NluLjCxaebdtcH3Xq0qX0R52sNs+UdzUsSmsdXeYx+0N0\nNFjs4KdHrJhHenY6C3YtYPKGycSejSVIBfGPP/2DiVETqRRSqdj7WTEXT5VlLhcvGkfK89ew+PiC\n+wQFQfv2BZunli2hpGkI/TbPlNY6Hoh3Xj+vlPoNaAgMAG5x7jYPiAaecfUYQhTn5xM/M2XTFL7c\n+yU5OgeQhT19LSvLGBuQv/DExRXdr1Uro+D86U+Bc9TJHVLDhL+cunCK97e9z3tb3yPxQiIA7a5o\nx9yBc+nUsJPJ0dmD1sbYzMJHnbKyCu5Xq1bRo05hfpr/1OsxU0qpCOA6YAtQV2ud+w1kAhDYE/2U\nwIpjWjxldi45jhwW7VvElM1T2HhsIwAhQSH8+Zo/M77reG5ocIPbj2V2Lr7iqzyys2HfPti+3bj8\n8otxuXix4H7VqkHnzpcKT9euxiFwuyvPNUz4zp7EPby1+S3m/zqfjJwMACLrRTKh6wTuv+Z+QoND\nTY4wcP3xx6X6tX07bN1q3JZfUBBce23RsU5ltfiFV82U8/D4V8BTWuvU/Et2aK21Usrl93nDhw8n\nwjkqLTw8nMjIyLw3jdyJCmW7bLdzlfXzL1+9nBWxK1ietZy4pDg4DFVDqzLmvjGM6TyG2O2xnN9/\nHhq4H19MTIzp/55mba9ZE82RI6BUFNu3w7p10Rw8CJmZxu+NAy0AUVx1FTRrFk27djBsWBTXXAPr\n1/smntzrca4Od1mIZzXsDMOHT8rbioyMIjIyiogI14Nt4+JcH/Wzwv7x8cZXMVaJx9P94+OLH+zs\nz3i01sz/cT3vrl7G1hM/O2/tRrfG3Rh7+wDuv7FLkaWsSnr8ws8RCP/+xe0fEVHaf0/YuBHWrYPf\nf4cDB2D/fjh3ruj9a9UyPvC1bg0NGkCbNlAl38wSoaGuGyl34omOjs6rZUlujpj0eJ4ppVQFYBmw\nQmv9lvO2fUCU1jpeKVUfWKe1bl3ofjJmSnAk6Qhvb3mb2Ttmk5KRAkCLmi0Y13UcwyOHUy3UgxGA\n5Ux6Ouzadelo0/btxrarSTKbNYOOHY3L9dcblzp1yi5Wq42ZAqlhwnPp2el8+uunTN08lT2njEmC\nK4dUZkTkCJ7q+hStarcyOULr0xoOHSp4xHz7djhzpui+NWpcql+5NaxVq7I76uS3MVPKaLU/BPbm\nFiGnJcAw4D/On4s8eXxhX5uPb2bq5ql8tfervPFQ3Zt2Z0LXCfRr1Y/gIBsMyPGDCxfg118vFZzt\n22HPHtfzobRqVbDwXHed3aY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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "display_data" + } + ], + "source": [ + "# scaling\n", + "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", + " \n", + "axes[0].plot(x, x**2, x, exp(x))\n", + "axes[0].set_title(\"Normal scale\")\n", + "\n", + "axes[1].plot(x, x**2, x, exp(x))\n", + "axes[1].set_yscale(\"log\")\n", + "axes[1].set_title(\"Logarithmic scale (y)\");" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# twin axes example\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "ax1.plot(x, x**2, lw=2, color=\"blue\")\n", - "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n", - "for label in ax1.get_yticklabels():\n", - " label.set_color(\"blue\")\n", - " \n", - "ax2 = ax1.twinx()\n", - "ax2.plot(x, x**3, lw=2, color=\"red\")\n", - "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n", - 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qbpPo4pb6QJ/Y88IVNpXATnEQoT9wM4ST+Jxz4XjzTbjuOhtF7NQJunePYNL7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\nLOfKQrrO8SnwqQgTRfh72MG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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, + "output_type": "display_data" + } + ], + "source": [ + "# axis grid\n", + "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n", + "\n", + "# default grid appearance\n", + "axes[0].plot(x, x**2, x, x**3, lw=2)\n", + "axes[0].grid(True)\n", + "\n", + "# custom grid appearance\n", + "axes[1].plot(x, x**2, x, x**3, lw=2)\n", + "axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# other plot styles\n", - "xx = np.linspace(-0.75, 1., 100)\n", - "n = array([0,1,2,3,4,5])\n", - "\n", - "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n", - "\n", - "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n", - "axes[0].set_title(\"scatter\")\n", - "\n", - "axes[1].step(n, n**2, lw=2)\n", - "axes[1].set_title(\"step\")\n", - "\n", - "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n", - "axes[2].set_title(\"bar\")\n", - "\n", - "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n", - "axes[3].set_title(\"fill_between\");" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEACAYAAADMTRPsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8VHXW+PHPoUtRURRQUFDsXX6izwoSVLCtiF1XBdva\n", + "u64UUUABBRX7WlGxgCAggisIrkTBgiJNKSKuLKCAsHSpSc7vjzNhAgRI5s7k3pk579drXszcTOae\n", + "5Nknx/Mt5yuqinPOOZctyoUdgHPOOVeWPPE555zLKp74nHPOZRVPfM4557KKJz7nnHNZxROfc865\n", + "rBLZxCdCfRHGijBdhB9FuCN2vasIC0SYHHucGXaszjmXkUReR2QxIj8U87V7ESlAZI8i1zoi8jMi\n", + "sxBpVZahloZEdR+fCHWAOqpMEaE68D3QBrgEWK1Kn1ADdM65TCfSDFgDvIXqUUWu1wdeBQ4BGqO6\n", + "DJHDgf7ACcC+wKfAwagWlHncOxHZik+VRapMiT1fA8zEfpkAElpgzjmXLVTHAcuL+Uof4P6trp0H\n", + "DEB1E6pzgTlAk9QGmJjIJr6iRGgAHAd8E7t0uwhTRegrwu7hReacc1lG5DxgAarTtvrKPsCCIq8X\n", + 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xgHazzckuulP5NxERyRYzOxm4DqgA9gIPm9mVsfu4u5tZh+to6gLTvqEl0sKi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\nFaQHeHjtwzS3NDO4/+B8dyX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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "display_data" + } + ], + "source": [ + "# twin axes example\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "ax1.plot(x, x**2, lw=2, color=\"blue\")\n", + "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n", + "for label in ax1.get_yticklabels():\n", + " label.set_color(\"blue\")\n", + " \n", + "ax2 = ax1.twinx()\n", + "ax2.plot(x, x**3, lw=2, color=\"red\")\n", + "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n", + "for label in ax2.get_yticklabels():\n", + " label.set_color(\"red\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# histograms\n", - "n = np.random.randn(100000)\n", - "fig, axes = plt.subplots(1, 2, figsize=(12,4))\n", - "\n", - "axes[0].hist(n)\n", - "axes[0].set_title(\"Default histogram\")\n", - "axes[0].set_xlim((min(n), max(n)))\n", - "\n", - "axes[1].hist(n, cumulative=True, bins=50)\n", - "axes[1].set_title(\"Cumulative detailed histogram\")\n", - "axes[1].set_xlim((min(n), max(n)));" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAsgAAADSCAYAAABXeTaiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8nGW5//HPlbTp3oQ2pQtdUtkqqKwCP5fTHKUNgqce\n", + "Xrj/0IIe0d/BBU7FWlEsIiIey6nbUUCWHhVQDrZEpaYRSS2CQKGttQu00JQutDSke9qmyVy/P55n\n", + "0sl0kplktkzyfb9e8+rMM8/z3Pckd6bX3HM9123ujoiIiIiIBIry3QERERERkZ5EAbKIiIiISAwF\n", + "yCIiIiIiMRQgi4iIiIjEUIAsIiIiIhJDAbKIiIiISAwFyCIi0qeYWb2ZvTff/RCRnksBcoEws7lm\n", + "9ou4bXVm9ul89UkkVqIxKtJDeXgTyRgzO93MVprZPjNrNbMbw+2VZrYlZr+kH9D0fpp//fLdAUlL\n", + "Wm/wZlbk7pFMdUZEpK8wMwNwrbYlx3wFeNzdz06yXyof0Lo9rsxsLnCyu3+iu+cQzSBnlZnNNrOt\n", + "4afJ9Wb2HjMrMrOvmdnGcPtyMzsp3P8HZvaqme0Nt78r3H4JMAf4iJntDz+hfht4N/DjcNsPw32n\n", + "mFmtmb0RtvmhmP7cb2Y/NbPHzOwAUJnrn4n0DgnG9qW0H6Mrwv1KzeweM9se7n+LmRWFz11lZn81\n", + 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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "display_data" + } + ], + "source": [ + "# other plot styles\n", + "xx = np.linspace(-0.75, 1., 100)\n", + "n = array([0,1,2,3,4,5])\n", + "\n", + "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n", + "\n", + "axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n", + "axes[0].set_title(\"scatter\")\n", + "\n", + "axes[1].step(n, n**2, lw=2)\n", + "axes[1].set_title(\"step\")\n", + "\n", + "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n", + "axes[2].set_title(\"bar\")\n", + "\n", + "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n", + "axes[3].set_title(\"fill_between\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# annotations\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(xx, xx**2, xx, xx**3)\n", - "\n", - "ax.text(0.15, 0.2, r\"$y=x^2$\", 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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, + "output_type": "display_data" + } + ], + "source": [ + "# histograms\n", + "n = np.random.randn(100000)\n", + "fig, axes = plt.subplots(1, 2, figsize=(12,4))\n", + "\n", + "axes[0].hist(n)\n", + "axes[0].set_title(\"Default histogram\")\n", + "axes[0].set_xlim((min(n), max(n)))\n", + "\n", + "axes[1].hist(n, cumulative=True, bins=50)\n", + "axes[1].set_title(\"Cumulative detailed histogram\")\n", + "axes[1].set_xlim((min(n), max(n)));" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# color map\n", - "alpha = 0.7\n", - "phi_ext = 2 * pi * 0.5\n", - "\n", - "def flux_qubit_potential(phi_m, phi_p):\n", - " return ( + alpha - 2 * cos(phi_p)*cos(phi_m) - \n", - " alpha * cos(phi_ext - 2*phi_p))\n", - "\n", - "phi_m = linspace(0, 2*pi, 100)\n", - "phi_p = linspace(0, 2*pi, 100)\n", - "X,Y = meshgrid(phi_p, phi_m)\n", - "Z = flux_qubit_potential(X, Y).T\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, \n", - " cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", - "cb = fig.colorbar(p, ax=ax)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD7CAYAAABt0P8jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4lMX2wPHvpBFCx9AkAipgRZoigkhQSgAhKEWKqIgK\n", + "UizXHxdEUfSKXZR7RaVJV0RARIN0QlcwQIL00CSEBAgJJCQhbX5/zCIRAym7b3Y3ez7Psw9b3n3n\n", + "EJaT2XlnziitNUIIITyHl7MDEEIIUbwk8QshhIeRxC+EEB5GEr8QQngYSfxCCOFhJPELIYSH8XF2\n", + "AJcopWReqRBCFJLWWhX2PS7V49dau9TtzTffdHoMElPJiclV45KY3DemonKpxC+EEMJ6kviFEMLD\n", + "SOK/huDgYGeH8A8SU8G4YkzgmnFJTAXjijEVlbJnnMiRlFLaVWIRQgh3oJRCu/vFXSGEENaTxC+E\n", + "EB5GEr8QQngYSfxCCOFhJPELIYSHkcQvhBAeRhK/EEJ4GEn8QgjhYSTxCyGEh5HEL4QQHkYSvxBC\n", + "eBhJ/EII4WEk8QshhIeRxC+EEB7GrsSvlPpaKRWvlNp1jWP+q5Q6qJSKVEo1tqc9IYQQ9rO3xz8d\n", + 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PWYTjFGM5ib4ay5Ry3mVXWPNKseQEKcVOXOZ/eKkQHzhFGBAqw/pWfLBJY67D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\nFeYA7YQXutie7YdR08Po8WUF2XFClYIsZgDOogM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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, + "output_type": "display_data" + } + ], + "source": [ + "# annotations\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(xx, xx**2, xx, xx**3)\n", + "\n", + "ax.text(0.15, 0.2, r\"$y=x^2$\", fontsize=20, color=\"blue\")\n", + "ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=20, color=\"green\");" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d.axes3d import Axes3D" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAVsAAAEACAYAAAAdhtXZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztvXuwLEd9Jvj9qrqqu8/jnnulCxJIV9aAtVo8BgwYSVhr\n", + "HgMbKzNhO2J2FwIbxxqbGGI25HE4NoLxsBFmN3bsWSIIW+FhRiEelplh15oJIDzyLo9h/RgeFjLC\n", + "eoAlPEg8LAk9uNJ9nEe/O/ePyuz88lTWqb7ndPd5/b6IEyc7q7o6Kysz68vfU4wxUCgUCsV8kex3\n", + 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flux_qubit_potential(X, Y).T\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, \n", + " cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", + "cb = fig.colorbar(p, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d.axes3d import Axes3D" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# surface plots\n", - "fig = plt.figure(figsize=(14,6))\n", - "\n", - "# `ax` is a 3D-aware axis instance because of the projection='3d' \n", - "# keyword argument to add_subplot\n", - "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", - "\n", - "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n", - "\n", - "# surface_plot with color grading and color bar\n", - "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", - "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, \n", - " cmap=cm.coolwarm, linewidth=0, antialiased=False)\n", - "cb = fig.colorbar(p, shrink=0.5)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAwkAAAFdCAYAAABIGyFEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXm0HNV17/+tuYfb3bqSkAVCiEFCWAINIANmiInt2DEE\n", + "24nj5bd+K3m838rPIYCd52E5L/Zz4mU/x3Hyi2PHsY2J87J4/vlhiDHOIw7EJgaMkRBCQkIDCA2g\n", + "GUlIukNPVV3D+f3Rd1efqq7que/tvvd81uol3e7q6qrqrn3OPnvv75YYYxAIBAKBQCAQCAQCQp7p\n", + "AxAIBAKBQCAQCASDhXASBAKBQCAQCAQCQQDhJAgEAoFAIBAIBIIAwkkQCAQCgUAgEAgEAYSTIBAI\n", + "BAKBQCAQCAIIJ0EgEAgEAoFAIBAEUJu8LvRRBQKBYOaRZvoABhgxTgkEguliTtliEUkQCAQCgUAg\n", + "EAgEAYSTIBAIBAKBQCAQCAIIJ0EgEAgEAoFAIBAEEE6CQCAQCAQCgUAgCCCcBIFAIBAIBAKBQBBA\n", + "OAkCgUAgEAgEAoEggHASBAKBQCAQCAQCQQDhJAgEAoFAIBAIBIIAwkkQCAQCgUAgEAgEAYSTIBAI\n", + "BAKBQCAQCAIIJ0EgEAgEAoFAIBAEEE6CQCAQCAQCgUAgCCCcBIFAIBAIBAKBQBBAOAkCgUAgEAgE\n", + 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yKkHSZNxy4sXqcxGTcV4ViZc47XTiTvvj00ei6Nf93k6Of6s2zy7XS14DgJ6u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\n7NnDsHSpAlmOjyIAwMmTDta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qQM+OwJ3aj6xp8FBdsdWyI1PfQefjba+gwmagOreiXjKm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KmAs2cnACTzU0QdUNYsUxk+CZCDz6IZAA0+EeEHxgoAB82mi5/97CJu3qyhqyv+ArOKipIkQVGU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\nBhy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0MWpVv7MJUnb+K8AyANullLullBTAvwB4T26ddwP4odq+fAbAGELIecPdx7OOJGho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- "text": [ - "" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "display_data" + } + ], + "source": [ + "# surface plots\n", + "fig = plt.figure(figsize=(14,6))\n", + "\n", + "# `ax` is a 3D-aware axis instance because of the projection='3d' \n", + "# keyword argument to add_subplot\n", + "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", + "\n", + "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n", + "\n", + "# surface_plot with color grading and color bar\n", + "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", + "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, \n", + " cmap=cm.coolwarm, linewidth=0, antialiased=False)\n", + "cb = fig.colorbar(p, shrink=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# wire frame\n", - "fig = plt.figure(figsize=(8,6))\n", - "\n", - "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", - "\n", - "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FHX6x98zsy2bkFCsoOipqDSlKaCRplTpCOqB5fA8\n", + "xMKhvzt7Pc/zvLMiolhPRAVFERQQQZoBAVGiqKhYUEFFEUOS3ezutN8f42xmazbJpgDf9+vl645k\n", + "MzszO/t9vk/7PJJpmggEAoFAIEiP3NAnIBAIBALBvoAwmAKBQCAQZIAwmAKBQCAQZIAwmAKBQCAQ\n", + "ZIAwmAKBQCAQZIAwmAKBQCAQZICrit+LnhOBQCAQHGhIyX4oPEyBQCAQCDJAGEyBQCAQCDJAGEyB\n", + "QCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQ\n", + "CDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJA\n", + "GEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDLA1dAnIBA0BKZpomka\n", + "qqridrtRFAVJkpAkqaFPTSAQNFIk0zTT/T7tLwWCfQ3TNDEMA03T0DSNcDiMLMvR30mShNfrxeVy\n", + "IcsysiwLIyoQHHgk/dILD1NwwGAYBqqqUlpaSl5eHrIsoyhK1GCqqoqqqgCEQqGooZRlGZfLhaIo\n", + 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WLVIxTRg1ys1XX2V+Xvb1JbsH9SHh5Qz7ut3uqN5l/Dnai00m/Yz7mhENh+Ge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\n0DRo21YnGIRNmyxvzOu1CrjOOSeHv/0twhtvVHD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O4IUXAnjkEZ28rA2tVnj8cWHGbLPh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\nUpHZy6+/1vD997bF1yVpwJa2O7k4VRxvQZIkcnNzi+yuPH78OKN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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "display_data" + } + ], + "source": [ + "# wire frame\n", + "fig = plt.figure(figsize=(8,6))\n", + "\n", + "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", + "\n", + "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# contour plot with projections\n", - "fig = plt.figure(figsize=(8,6))\n", - "\n", - "ax = fig.add_subplot(1,1,1, projection='3d')\n", - "\n", - "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n", - "cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)\n", - "cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)\n", - "cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)\n", - "\n", - "ax.set_xlim3d(-pi, 2*pi);\n", - "ax.set_ylim3d(0, 3*pi);\n", - "ax.set_zlim3d(-pi, 2*pi);" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XNWdv987vanL6t2Sm4x7wzYYm14WyFLDErKU1E1I\n", + "SDY9+0uyKUsCbMICSQiEBAgQIAkkoWPABXds2ZaNu+Uiy7LVpelzZ+7vD/mOR/JImpFmRjPSeZ+H\n", + "hwRp5pw7unM+99slRVEQCAQCgUAwOJrR3oBAIBAIBKmAEEyBQCAQCCJACKZAIBAIBBEgBFMgEAgE\n", + "gggQgikQCAQCQQQIwRQIBAKBIAJ0Q/xc1JwIBAKBYLwhhfuPwsIUCAQCgSAChGAKBAKBQBABQjAF\n", + "AoFAIIgAIZgCgUAgEESAEEyBQCAQCCJACKZAIBAIBBEgBFMgEAgEgggQgikQCAQCQQQIwRQIBAKB\n", + "IAKEYAoEAoFAEAFCMAUCgUAgiAAhmAKBQCAQRIAQTIFAIBAIIkAIpkAgEAgEESAEUyAQCASCCBCC\n", + "KRAIBAJBBAjBFAgEAoEgAoRgCgQCgUAQAUIwBQKBQCCIACGYAoFAIBBEgG60NyAQjAaKoiDLMj6f\n", + "D71ej1arRZIkJEka7a0JBIIkRVIUZbCfD/pDgSDVUBSFQCCALMvIsozH40GjOeto0Wq16HQ6dDod\n", + "Go0GjUYjRFQgGH+E/dILwRSMGwKBAD6fj0AggCRJKIqC1+sNCqaiKH3+UYVSo9Gg0+nQarXBf4SI\n", + 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e7OTW6wr48t1lmIxD34OF+UYe+uEkTp7y8HRDGQV33cSeu7+DIstR7SFVk37U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8BvNa9ioNwximYHCpKg3ZhUTZlbjpnfWFKnGvNIoeuoOce9eSSCTGVSBhBAfRjh8i2bAXvf00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\nzUwY4mN0f1otJApeF3yzO4ycDA+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- "text": [ - "" - ] - } - ], - "prompt_number": 18 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# contour plot with projections\n", + "fig = plt.figure(figsize=(8,6))\n", + "\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n", + "cset = ax.contour(X, Y, Z, zdir='z', offset=-pi, cmap=cm.coolwarm)\n", + "cset = ax.contour(X, Y, Z, zdir='x', offset=-pi, cmap=cm.coolwarm)\n", + "cset = ax.contour(X, Y, Z, zdir='y', offset=3*pi, cmap=cm.coolwarm)\n", + "\n", + "ax.set_xlim3d(-pi, 2*pi);\n", + "ax.set_ylim3d(0, 3*pi);\n", + "ax.set_zlim3d(-pi, 2*pi);" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/NLTK.ipynb b/NLTK.ipynb index 3e9f257..fcf9bf8 100644 --- a/NLTK.ipynb +++ b/NLTK.ipynb @@ -1,1885 +1,2380 @@ { - "metadata": { - "name": "", - "signature": "sha256:cc05ee1eaedfbd2c5f47e26019cc51ba2d127ff07c9955c6491478d996cf0dda" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "NLTK" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NLTK" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, and an active discussion forum." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "showing info http://nltk.github.com/nltk_data/\n" ] }, { - "cell_type": "markdown", + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, "metadata": {}, - "source": [ - "NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, and an active discussion forum." + "output_type": "execute_result" + } + ], + "source": [ + "# import the library and download sample texts\n", + "import nltk\n", + "nltk.download()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*** Introductory Examples for the NLTK Book ***\n", + "Loading text1, ..., text9 and sent1, ..., sent9\n", + "Type the name of the text or sentence to view it.\n", + "Type: 'texts()' or 'sents()' to list the materials.\n", + "text1: Moby Dick by Herman Melville 1851\n", + "text2: Sense and Sensibility by Jane Austen 1811\n", + "text3: The Book of Genesis\n", + "text4: Inaugural Address Corpus\n", + "text5: Chat Corpus\n", + "text6: Monty Python and the Holy Grail\n", + "text7: Wall Street Journal\n", + "text8: Personals Corpus\n", + "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# needed to display the graphs\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# import the library and download sample texts\n", - "import nltk\n", - "nltk.download()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "showing info http://nltk.github.com/nltk_data/\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "True" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from nltk.book import *" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "*** Introductory Examples for the NLTK Book ***\n", - "Loading text1, ..., text9 and sent1, ..., sent9\n", - "Type the name of the text or sentence to view it.\n", - "Type: 'texts()' or 'sents()' to list the materials.\n", - "text1:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Moby Dick by Herman Melville 1851\n", - "text2:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Sense and Sensibility by Jane Austen 1811\n", - "text3:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " The Book of Genesis\n", - "text4:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Inaugural Address Corpus\n", - "text5:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Chat Corpus\n", - "text6:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Monty Python and the Holy Grail\n", - "text7:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Wall Street Journal\n", - "text8: Personals Corpus\n", - "text9:" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " The Man Who Was Thursday by G . K . Chesterton 1908\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# examine concordances (word + context)\n", - "text1.concordance(\"monstrous\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Displaying 11 of 11 matches:\n", - "ong the former , one was of a most monstrous size . ... This came towards us , \n", - "ON OF THE PSALMS . \" Touching that monstrous bulk of the whale or ork we have r\n", - "ll over with a heathenish array of monstrous clubs and spears . Some were thick\n", - "d as you gazed , and wondered what monstrous cannibal and savage could ever hav\n", - "that has survived the flood ; most monstrous and most mountainous ! That Himmal\n", - "they might scout at Moby Dick as a monstrous fable , or still worse and more de\n", - "th of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l\n", - "ing Scenes . In connexion with the monstrous pictures of whales , I am strongly\n", - "ere to enter upon those still more monstrous stories of them which are to be fo\n", - "ght have been rummaged out of this monstrous cabinet there is no telling . But \n", - "of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "text1.similar(\"monstrous\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "imperial subtly impalpable pitiable curious abundant perilous\n", - "trustworthy untoward singular lamentable few determined maddens\n", - "horrible tyrannical lazy mystifying christian exasperate\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "text2.common_contexts([\"monstrous\", \"very\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "a_pretty is_pretty a_lucky am_glad be_glad\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# see where in a text certain words are found to occur\n", - "text4.dispersion_plot([\"citizens\", \"democracy\", \"freedom\", \"duties\", \"America\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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zM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0n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\n9T9kxgDDol5HqgAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# count of all tokens (including punctuation)\n", - "len(text3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "44764" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# number of distinct tokens\n", - "len(set(text3))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "2789" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# the texts are just lists of strings\n", - "text2[141525:]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "[u'among',\n", - " u'the',\n", - " u'merits',\n", - " u'and',\n", - " u'the',\n", - " u'happiness',\n", - " u'of',\n", - " u'Elinor',\n", - " u'and',\n", - " u'Marianne',\n", - " u',',\n", - " u'let',\n", - " u'it',\n", - " u'not',\n", - " u'be',\n", - " u'ranked',\n", - " u'as',\n", - " u'the',\n", - " u'least',\n", - " u'considerable',\n", - " u',',\n", - " u'that',\n", - " u'though',\n", - " u'sisters',\n", - " u',',\n", - " u'and',\n", - " u'living',\n", - " u'almost',\n", - " u'within',\n", - " u'sight',\n", - " u'of',\n", - " u'each',\n", - " u'other',\n", - " u',',\n", - " u'they',\n", - " u'could',\n", - " u'live',\n", - " u'without',\n", - " u'disagreement',\n", - " u'between',\n", - " u'themselves',\n", - " u',',\n", - " u'or',\n", - " u'producing',\n", - " u'coolness',\n", - " u'between',\n", - " u'their',\n", - " u'husbands',\n", - " u'.',\n", - " u'THE',\n", - " u'END']" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# build a frequency distribution\n", - "fdist1 = FreqDist(text1) \n", - "fdist1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "FreqDist({u',': 18713, u'the': 13721, u'.': 6862, u'of': 6536, u'and': 6024, u'a': 4569, u'to': 4542, u';': 4072, u'in': 3916, u'that': 2982, ...})" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fdist1.most_common(20)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "[(u',', 18713),\n", - " (u'the', 13721),\n", - " (u'.', 6862),\n", - " (u'of', 6536),\n", - " (u'and', 6024),\n", - " (u'a', 4569),\n", - " (u'to', 4542),\n", - " (u';', 4072),\n", - " (u'in', 3916),\n", - " (u'that', 2982),\n", - " (u\"'\", 2684),\n", - " (u'-', 2552),\n", - " (u'his', 2459),\n", - " (u'it', 2209),\n", - " (u'I', 2124),\n", - " (u's', 1739),\n", - " (u'is', 1695),\n", - " (u'he', 1661),\n", - " (u'with', 1659),\n", - " (u'was', 1632)]" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fdist1['whale']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "906" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fdist1.plot(20, cumulative=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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0kZRbfnEEMD+WXwFc2pyX1uRQrrsO3nkH+veH2to0+jNT0EjBQxYa\nKXhIRSMFD6lopOAhJY1CVCSHYmYfxsO1gE7AAuAQYFwsHwccFo8PBW4zs0/MbBbwGjBEUm+gu5lN\njdfdmFcnX+tO4AtZ+P7oI7jkknDsrRPHcZzlqUhAkbSGpHpgHvCYmb1I2NBrXrxkHtAzHm8C5O9h\nPxvYtJnyObGc+PMtADNbArwvqceKPurr64vyfcMNMHs29OsHhx4KNTU1q62zOjqKRgoestBIwUMq\nGil4SEUjBQ8paRSi4GrD5cTMlgH9Ja0PPCRp3xWeN0llHy2wzTbbUFdXR9euXQEYNGgQtbW1n77h\nuaZhTU0NH38Mf/5zA/37w3nn1bDGGss/v+L1fu7nfu7nHeG8vr6eSZMm0djYyGoxs4o+gB8B3wNm\nAL1iWW9gRjyuA+ryrp8ADAF6AS/nlR8NjM27Zo943Bl4t7nX7t+/v7WUa681A7O+fc2WLg1lCxYs\naHH9QnQUjRQ8ZKGRgodUNFLwkIpGCh5S0Qhho/nv80qM8tooN4JLUjfgS8A04F7g+HjZ8cDd8fhe\nYLiktSRtDfQBpprZXGChpCExSX8scE9enZzWEYQkf6v55BP42c/C8Q9/GFYTdhzHcZanzeehSOpH\nSJivER83mdnlMcdxO7AFMAs40swaYp3zgBOBJcCZZvZQLB8I3AB0Ax40s1GxvAtwEzAAmA8Mt5DQ\nX9GLteT+x42Db30LttsOXnop7HXiOI5TjaxqHopPbFzN/S9ZAn37wsyZIbAcd1wbmXMcx0mQpCY2\npkRL5qGMHx+CyWc/C8ccs/xzqYwJT0EjBQ9ZaKTgIRWNFDykopGCh5Q0ClHVAWV1LF0KP/1pOP7B\nD6BzRcbEOY7jtA+8y2sV9z9+PAwfDltuGVopa67ZhuYcx3ESxLu8WsGyZXDRReH4vPM8mDiO46yO\nqg4oq8qh3HUXvPgibL45HH9889ek0p+ZgkYKHrLQSMFDKhopeEhFIwUPKWkUoqoDSiGWLYOf/CQc\n19VBly6V9eM4jtMe8BxKM/d/zz1w2GGwySbw+usQV2ZxHMepejyHUgRmTbmTc8/1YOI4jtNSqjqg\nNJdD+ctf4NlnoWdPOOmkVddPpT8zBY0UPGShkYKHVDRS8JCKRgoeUtIoRFUHlBUxa8qdfP/70K1b\nZf04juO0JzyHknf/EyfCl78MG28Mb7wB66xTQXOO4zgJ4jmUFmAGF14Yjr/3PQ8mjuM4xVLVASU/\nh/LYY/DMM9CjB5x2Wsvqp9KfmYJGCh6y0EjBQyoaKXhIRSMFDylpFKKqA0o+udzJd78L3btX1ovj\nOE57xHMoZjz+OAwdCjU1MGsWrL9+pZ05juOkiedQVkNu3slZZ3kwcRzHaS2V2AJ4c0mPSXpR0guS\ncrss9pD0sKRXJU3MbRMcnxstaaakGZL2zysfKGl6fO7KvPIuksbH8smStmzOS//+/Xn6aXjkEVhv\nPRg1qrh7SaU/MwWNFDxkoZGCh1Q0UvCQikYKHlLSKEQlWiifAN8xs52APYDTJe0I1AEPm9l2hD3g\n6wAk9QWOAvoCw4AxcQ95gLHACDPrA/SRNCyWjwDmx/IrgEsLmcm1TkaNgg02yPI2HcdxqouK51Ak\n3Q38Jj72MbN5knoBk8xsB0mjgWVmdmm8fgJwAfAm8KiZ7RjLhwNDzezUeM35ZjZFUmfgHTPbuJnX\nNjDWXTfkTjbcsA1u2HEcpx2TbA5F0lbAAGAK0NPM5sWn5gE94/EmwOy8arOBTZspnxPLiT/fAjCz\nJcD7knoU8nHGGR5MHMdxSqVim9pKWhe4EzjTzBY19WKBmVloPZSXvffem9dfr2PJkq5ccAEMGjSI\n2tpaampC+ibX11jofPbs2ay77rotvr6588WLF7PZZpu1un6OmpqaVtfPr1up+qm8nx3l88ji/Uzh\n80jl/Uzh86jU+1lfX8+kSZNobGxktZhZmz+ANYGHgLPyymYAveJxb2BGPK4D6vKumwAMAXoBL+eV\nHw2Mzbtmj3jcGXi3OR/9+/e3733PWs2CBQtaX7mDaaTgIQuNFDykopGCh1Q0UvCQikYIG81/t7d5\nDiUm1McRkubfySu/LJZdKqkOqDGzupiUvxUYTOjK+iuwrZmZpCnAKGAq8ABwlZlNkDQS6Gdmp8Xc\nymFmNrwZLzZ3rtGz54rPOI7jOM2xqhxKJQJKLfAE8DyQe/HRhKBwO7AFMAs40swaYp3zgBOBJYQu\nsodi+UDgBqAb8KCZ5YYgdwFuIuRn5gPDzWxWM16sre/fcRynPZNUQEmJAQMG2LRp01pdv6Gh4dP+\nxmrXSMFDFhopeEhFIwUPqWik4CEVjWRHeTmO4zgdh6puoXiXl+M4TnF4C8VxHMcpO1UdUJrbU74Y\nUllXJwWNFDxkoZGCh1Q0UvCQikYKHlLSKERVBxTHcRwnOzyHUsX37ziOUyyeQ3Ecx3HKTlUHFM+h\nZKeRgocsNFLwkIpGCh5S0UjBQ0oahajqgOI4juNkh+dQqvj+HcdxisVzKI7jOE7ZqeqA4jmU7DRS\n8JCFRgoeUtFIwUMqGil4SEmjEFUdUBzHcZzs8BxKFd+/4zhOsXgOxXEcxyk7FQkokq6XNE/S9Lyy\nHpIelvSqpImSavKeGy1ppqQZkvbPKx8oaXp87sq88i6SxsfyyZK2bM6H51Cy00jBQxYaKXhIRSMF\nD6lopOAhJY1CVKqF8gdg2ApldcDDZrYd8Eg8J24BfBTQN9YZE7cRBhgLjDCzPkAfSTnNEYTthPsA\nVwCXNmdi0aJFJd3EU089VVL9jqSRgocsNFLwkIpGCh5S0UjBQ0oahahIQDGzJ4EFKxQfQthrnvjz\nsHh8KHCbmX0St/F9DRgiqTfQ3cymxutuzKuTr3Un8IXmfLz++usl3cc//vGPkup3JI0UPGShkYKH\nVDRS8JCKRgoeUtIoREo5lJ5mNi8ezwN6xuNNgNl5180GNm2mfE4sJ/58C8DMlgDvS+pRJt+O4zgO\naQWUT4lDr8o+/Kpnz56rv2gVNDY2luyho2ik4CELjRQ8pKKRgodUNFLwkJJGQcysIg9gK2B63vkM\noFc87g3MiMd1QF3edROAIUAv4OW88qOBsXnX7BGPOwPvFvBg/vCHP/zhj+Iehb7XO5MO9wLHExLo\nxwN355XfKulXhK6sPsBUMzNJCyUNAaYCxwJXraA1GTiCkORfiUJjqR3HcZziqcjERkm3AfsAGxHy\nJT8G7gFuB7YAZgFHmllDvP484ERgCXCmmT0UywcCNwDdgAfNbFQs7wLcBAwA5gPDY0LfcRzHKRNV\nPVPecRzHyY6UurzanDj0+D0z+6jCPnqZ2dxKemgNceRcH6BLrszMnmhjD8u9d6l8ppWmvf5OOe2b\nJEd5tSE3A69I+kWFfVxX4dcvGkknAY8TBkBcCDwEXFABKyu+d0V/ppL2lPQNScfHx3HFGJDUtSVl\nq6j/RnxMKeZ1V8ODGWq1GElntqSsQN3FkhYVeCws0seRktaLxz+SdJek3YqoXytp3Xh8rKRfFVpx\nYxUa20t6RNKL8XwXST8sRiPWK+n3s02p1CivVB6EoLpTC6/tRfgCmxDP+xJm6reV15viz7NK0FgM\nLCrwWFiEzguE3FV9PN8BuKsVfnoBBwMHAZ+pwGd6M/AMMAa4Ovco8vWea0lZWz6Aaa2oczmwHrAm\nYSDLf4FjS33d3O9IG9//9PizFpgUf7+mFFMfELArMA04HXi8SA9PEEakTovnAl4sUiOL38+SP9eW\nPqq6ywvAzJYBL7bw8hsIy8b8IJ7PJAwkaKsWxkBJmwAnSrqR8Av6aRLMzN5bnYCZ5f7r+inwNuEX\nFuAbhMmiLaXRzP4nCUldzWyGpO2LqI+kIwm/7I/Hot9IOsfM/lSMzooU+ZkOBPpa/Msrhti9tgmw\ndvzvN/d5rAesXaxexlzTijr7m9k5kg4nDIz5KvAkYYDLKpF0NHAMsLWk+/Ke6k4YGNPWLI0/DwKu\nMbP7JV1URP0lZmaSDgN+a2bXShpRpIe1zWxKbqWoqPdJkRqt/v3Mo9Wfa7FUfUApko3MbLykOgAz\n+0TSkjZ8/d8R/sP4LPBsM89vXYTWIWa2S975WEnPAz9qYf23JG1AGN79sKQFhF/WYvghsLuZ/QdA\n0saE+yspoBTJC4R5T2+3ou7+wLcIw9l/mVe+CDivZGclYGZjWlEt931wEHCHmb0vqaVfZM8A7wAb\nA78gBFcI78U/W+GlVOZI+j3wJeCS2AVZTBf/oji69JvAXpI6Ef7DL4Z3JW2bO5F0BOE9KoZSfj9z\nlPK5tuqFnJaxWNKGuRNJewDvt9WLm9lVwFWSxgL/B+xN+I/4STOrL1LuA0nfBG6L58MJ3WEt9XJ4\nPLxA0iTCf+UTivQg4N288/k0fRG1FRsDL0maCuQS+WZmh6yuopmNA8ZJOsLM7iinyTbiPkkzgEbg\nNEmficerxczeBN4E9iijv2I4krCY7OVm1hBbk+cUUf8oQovrRDObK2kLQqAshjMIf6fbS3ob+Bch\nQK2WvFbeurTy9zOPVn+uxeLDhosgznu5GtiJ0KWyMXCEmbXpf2AxyXkS8OdYdDihWX9V4VoraWwN\nXAl8PhY9TZjjMytDq6vzcDmhj/pWQiA5CnjezL7fhh6GNlduZpOK1DmIkFP7NBlvZj8pxVsliP8w\nNZjZUknrEBZgXe1oMUlPm9mekhaT1w0bMTNbrxx+Uya2ir5GWBWkB7CQ8F6s9vci7/fSWPmfLDOz\nxymC1n6uxeIBpUgkrQnkcgWvmFmxfaJZeJhOWFrmg3i+DjDZzPq1tZdSkHQZMIWQODXgKcJ9tVlA\nyQJJ/0cYoLAfIXfxdUICuNg+94og6Qt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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# apply a list comprehension to get words over 15 characters\n", - "V = set(text1)\n", - "long_words = [w for w in V if len(w) > 15]\n", - "sorted(long_words)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "[u'CIRCUMNAVIGATION',\n", - " u'Physiognomically',\n", - " u'apprehensiveness',\n", - " u'cannibalistically',\n", - " u'characteristically',\n", - " u'circumnavigating',\n", - " u'circumnavigation',\n", - " u'circumnavigations',\n", - " u'comprehensiveness',\n", - " u'hermaphroditical',\n", - " u'indiscriminately',\n", - " u'indispensableness',\n", - " u'irresistibleness',\n", - " u'physiognomically',\n", - " u'preternaturalness',\n", - " u'responsibilities',\n", - " u'simultaneousness',\n", - " u'subterraneousness',\n", - " u'supernaturalness',\n", - " u'superstitiousness',\n", - " u'uncomfortableness',\n", - " u'uncompromisedness',\n", - " u'undiscriminating',\n", - " u'uninterpenetratingly']" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fdist2 = FreqDist(text5)\n", - "sorted(w for w in set(text5) if len(w) > 7 and fdist2[w] > 7)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "[u'#14-19teens',\n", - " u'#talkcity_adults',\n", - " u'((((((((((',\n", - " u'........',\n", - " u'Question',\n", - " u'actually',\n", - " u'anything',\n", - " u'computer',\n", - " u'cute.-ass',\n", - " u'everyone',\n", - " u'football',\n", - " u'innocent',\n", - " u'listening',\n", - " u'remember',\n", - " u'seriously',\n", - " u'something',\n", - " u'together',\n", - " u'tomorrow',\n", - " u'watching']" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# word sequences that appear together unusually often\n", - "text4.collocations()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "United States; fellow citizens; four years; years ago; Federal\n", - "Government; General Government; American people; Vice President; Old\n", - "World; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;\n", - "God bless; every citizen; Indian tribes; public debt; one another;\n", - "foreign nations; political parties\n" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Raw Text Processing" + } + ], + "source": [ + "from nltk.book import *" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Displaying 11 of 11 matches:\n", + "ong the former , one was of a most monstrous size . ... This came towards us , \n", + "ON OF THE PSALMS . \" Touching that monstrous bulk of the whale or ork we have r\n", + "ll over with a heathenish array of monstrous clubs and spears . Some were thick\n", + "d as you gazed , and wondered what monstrous cannibal and savage could ever hav\n", + "that has survived the flood ; most monstrous and most mountainous ! That Himmal\n", + "they might scout at Moby Dick as a monstrous fable , or still worse and more de\n", + "th of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l\n", + "ing Scenes . In connexion with the monstrous pictures of whales , I am strongly\n", + "ere to enter upon those still more monstrous stories of them which are to be fo\n", + "ght have been rummaged out of this monstrous cabinet there is no telling . But \n", + "of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# download raw text from an online repository\n", - "import urllib2\n", - "url = \"http://www.gutenberg.org/files/2554/2554.txt\"\n", - "response = urllib2.urlopen(url)\n", - "raw = response.read().decode('utf8')\n", - "len(raw)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "1176896" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "raw[:75]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "u'The Project Gutenberg EBook of Crime and Punishment, by Fyodor Dostoevsky\\r\\n'" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# tokenize the raw text\n", - "from nltk import word_tokenize\n", - "tokens = word_tokenize(raw)\n", - "len(tokens)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "254352" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tokens[:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "[u'The',\n", - " u'Project',\n", - " u'Gutenberg',\n", - " u'EBook',\n", - " u'of',\n", - " u'Crime',\n", - " u'and',\n", - " u'Punishment',\n", - " u',',\n", - " u'by']" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "text = nltk.Text(tokens)\n", - "text[1024:1062]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "[u'CHAPTER',\n", - " u'I',\n", - " u'On',\n", - " u'an',\n", - " u'exceptionally',\n", - " u'hot',\n", - " u'evening',\n", - " u'early',\n", - " u'in',\n", - " u'July',\n", - " u'a',\n", - " u'young',\n", - " u'man',\n", - " u'came',\n", - " u'out',\n", - " u'of',\n", - " u'the',\n", - " u'garret',\n", - " u'in',\n", - " u'which',\n", - " u'he',\n", - " u'lodged',\n", - " u'in',\n", - " u'S.',\n", - " u'Place',\n", - " u'and',\n", - " u'walked',\n", - " u'slowly',\n", - " u',',\n", - " u'as',\n", - " u'though',\n", - " u'in',\n", - " u'hesitation',\n", - " u',',\n", - " u'towards',\n", - " u'K.',\n", - " u'bridge',\n", - " u'.']" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "text.collocations()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Katerina Ivanovna; Pyotr Petrovitch; Pulcheria Alexandrovna; Avdotya\n", - "Romanovna; Rodion Romanovitch; Marfa Petrovna; Sofya Semyonovna; old\n", - "woman; Project Gutenberg-tm; Porfiry Petrovitch; Amalia Ivanovna;\n", - "great deal; Nikodim Fomitch; young man; Ilya Petrovitch; n't know;\n", - "Project Gutenberg; Dmitri Prokofitch; Andrey Semyonovitch; Hay Market\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "raw.find(\"PART I\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "5338" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# HTML parsing using the Beautiful Soup library\n", - "from bs4 import BeautifulSoup\n", - "url = \"http://news.bbc.co.uk/2/hi/health/2284783.stm\"\n", - "html = urllib2.urlopen(url).read().decode('utf8')\n", - "raw = BeautifulSoup(html).get_text()\n", - "tokens = word_tokenize(raw)\n", - "tokens[0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "[u'BBC',\n", - " u'NEWS',\n", - " u'|',\n", - " u'Health',\n", - " u'|',\n", - " u'Blondes',\n", - " u\"'to\",\n", - " u'die',\n", - " u'out',\n", - " u'in']" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# isolate just the article text\n", - "tokens = tokens[110:390]\n", - "text = nltk.Text(tokens)\n", - "text.concordance('gene')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Displaying 5 of 5 matches:\n", - "hey say too few people now carry the gene for blondes to last beyond the next \n", - "blonde hair is caused by a recessive gene . In order for a child to have blond\n", - " have blonde hair , it must have the gene on both sides of the family in the g\n", - "ere is a disadvantage of having that gene or by chance . They do n't disappear\n", - "des would disappear is if having the gene was a disadvantage and I do not thin\n" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Regular Expressions" + } + ], + "source": [ + "# examine concordances (word + context)\n", + "text1.concordance(\"monstrous\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imperial subtly impalpable pitiable curious abundant perilous\n", + "trustworthy untoward singular lamentable few determined maddens\n", + "horrible tyrannical lazy mystifying christian exasperate\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# regular expression library\n", - "import re\n", - "wordlist = [w for w in nltk.corpus.words.words('en') if w.islower()]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# match the end of a word\n", - "[w for w in wordlist if re.search('ed$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "[u'abaissed',\n", - " u'abandoned',\n", - " u'abased',\n", - " u'abashed',\n", - " u'abatised',\n", - " u'abed',\n", - " u'aborted',\n", - " u'abridged',\n", - " u'abscessed',\n", - " u'absconded']" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# wildcard matches any single character\n", - "[w for w in wordlist if re.search('^..j..t..$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "[u'abjectly',\n", - " u'adjuster',\n", - " u'dejected',\n", - " u'dejectly',\n", - " u'injector',\n", - " u'majestic',\n", - " u'objectee',\n", - " u'objector',\n", - " u'rejecter',\n", - " u'rejector']" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# combination of caret (start of word) and sets\n", - "[w for w in wordlist if re.search('^[ghi][mno][jlk][def]$', w)]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "[u'gold', u'golf', u'hold', u'hole']" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "chat_words = sorted(set(w for w in nltk.corpus.nps_chat.words()))\n", - "\n", - "# plus symbol matches any number of times repeating\n", - "[w for w in chat_words if re.search('^m+i+n+e+$', w)]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "[u'miiiiiiiiiiiiinnnnnnnnnnneeeeeeeeee',\n", - " u'miiiiiinnnnnnnnnneeeeeeee',\n", - " u'mine',\n", - " u'mmmmmmmmiiiiiiiiinnnnnnnnneeeeeeee']" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "wsj = sorted(set(nltk.corpus.treebank.words()))\n", - "\n", - "# more advanced regex example\n", - "[w for w in wsj if re.search('^[0-9]+\\.[0-9]+$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "[u'0.0085',\n", - " u'0.05',\n", - " u'0.1',\n", - " u'0.16',\n", - " u'0.2',\n", - " u'0.25',\n", - " u'0.28',\n", - " u'0.3',\n", - " u'0.4',\n", - " u'0.5']" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[w for w in wsj if re.search('^[A-Z]+\\$$', w)]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "[u'C$', u'US$']" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[w for w in wsj if re.search('^[0-9]{4}$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 34, - "text": [ - "[u'1614',\n", - " u'1637',\n", - " u'1787',\n", - " u'1901',\n", - " u'1903',\n", - " u'1917',\n", - " u'1925',\n", - " u'1929',\n", - " u'1933',\n", - " u'1934']" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[w for w in wsj if re.search('^[0-9]+-[a-z]{3,5}$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "[u'10-day',\n", - " u'10-lap',\n", - " u'10-year',\n", - " u'100-share',\n", - " u'12-point',\n", - " u'12-year',\n", - " u'14-hour',\n", - " u'15-day',\n", - " u'150-point',\n", - " u'190-point']" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[w for w in wsj if re.search('^[a-z]{5,}-[a-z]{2,3}-[a-z]{,6}$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "[u'black-and-white',\n", - " u'bread-and-butter',\n", - " u'father-in-law',\n", - " u'machine-gun-toting',\n", - " u'savings-and-loan']" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[w for w in wsj if re.search('(ed|ing)$', w)][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 37, - "text": [ - "[u'62%-owned',\n", - " u'Absorbed',\n", - " u'According',\n", - " u'Adopting',\n", - " u'Advanced',\n", - " u'Advancing',\n", - " u'Alfred',\n", - " u'Allied',\n", - " u'Annualized',\n", - " u'Anything']" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# using \"findall\" to extract partial matches from words\n", - "fd = nltk.FreqDist(vs for word in wsj \n", - " for vs in re.findall(r'[aeiou]{2,}', word))\n", - "fd.most_common(12)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "[(u'io', 549),\n", - " (u'ea', 476),\n", - " (u'ie', 331),\n", - " (u'ou', 329),\n", - " (u'ai', 261),\n", - " (u'ia', 253),\n", - " (u'ee', 217),\n", - " (u'oo', 174),\n", - " (u'ua', 109),\n", - " (u'au', 106),\n", - " (u'ue', 105),\n", - " (u'ui', 95)]" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Normalizing Text" + } + ], + "source": [ + "text1.similar(\"monstrous\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_pretty is_pretty a_lucky am_glad be_glad\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# NLTK has several word stemmers built in\n", - "porter = nltk.PorterStemmer()\n", - "lancaster = nltk.LancasterStemmer()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[porter.stem(t) for t in tokens][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 40, - "text": [ - "[u'UK',\n", - " u'Blond',\n", - " u\"'to\",\n", - " u'die',\n", - " u'out',\n", - " u'in',\n", - " u'200',\n", - " u\"years'\",\n", - " u'Scientist',\n", - " u'believ']" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[lancaster.stem(t) for t in tokens][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "[u'uk',\n", - " u'blond',\n", - " u\"'to\",\n", - " u'die',\n", - " u'out',\n", - " u'in',\n", - " u'200',\n", - " u\"years'\",\n", - " u'sci',\n", - " u'believ']" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "wnl = nltk.WordNetLemmatizer()\n", - "[wnl.lemmatize(t) for t in tokens][0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 42, - "text": [ - "[u'UK',\n", - " u'Blondes',\n", - " u\"'to\",\n", - " u'die',\n", - " u'out',\n", - " u'in',\n", - " u'200',\n", - " u\"years'\",\n", - " u'Scientists',\n", - " u'believe']" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# also has a tokenizer that takes a regular expression as a parameter\n", - "text = 'That U.S.A. poster-print costs $12.40...'\n", - "pattern = r'''(?x) # set flag to allow verbose regexps\n", - " ([A-Z]\\.)+ # abbreviations, e.g. U.S.A.\n", - " | \\w+(-\\w+)* # words with optional internal hyphens\n", - " | \\$?\\d+(\\.\\d+)?%? # currency and percentages, e.g. $12.40, 82%\n", - " | \\.\\.\\. # ellipsis\n", - " | [][.,;\"'?():-_`] # these are separate tokens; includes ], [\n", - "'''\n", - "nltk.regexp_tokenize(text, pattern)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 43, - "text": [ - "['That', 'U.S.A.', 'poster-print', 'costs', '$12.40', '...']" - ] - } - ], - "prompt_number": 43 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Tagging" + } + ], + "source": [ + "text2.common_contexts([\"monstrous\", \"very\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + 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"1RkvaafU5Pl0WtAsC05SZgN3G8W3+q6vlN1K8ZPwf0yn1I4FfijpVuAF4OxUb80RVar3EeAn6YsT\n", + "D9PimlREPAQcDXxT0gqKI7n/jIifVPps93MG75V0WzqttwdwXkSsAD4HXCFpGcVPcWyf6v87cKu/\n", + "OGG58E91mJlZtnwkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy\n", + "9T9kxgDDol5HqgAAAABJRU5ErkJggg==\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# see where in a text certain words are found to occur\n", + "text4.dispersion_plot([\"citizens\", \"democracy\", \"freedom\", \"duties\", \"America\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "44764" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# count of all tokens (including punctuation)\n", + "len(text3)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2789" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of distinct tokens\n", + "len(set(text3))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'among',\n", + " u'the',\n", + " u'merits',\n", + " u'and',\n", + " u'the',\n", + " u'happiness',\n", + " u'of',\n", + " u'Elinor',\n", + " u'and',\n", + " u'Marianne',\n", + " u',',\n", + " u'let',\n", + " u'it',\n", + " u'not',\n", + " u'be',\n", + " u'ranked',\n", + " u'as',\n", + " u'the',\n", + " u'least',\n", + " u'considerable',\n", + " u',',\n", + " u'that',\n", + " u'though',\n", + " u'sisters',\n", + " u',',\n", + " u'and',\n", + " u'living',\n", + " u'almost',\n", + " u'within',\n", + " u'sight',\n", + " u'of',\n", + " u'each',\n", + " u'other',\n", + " u',',\n", + " u'they',\n", + " u'could',\n", + " u'live',\n", + " u'without',\n", + " u'disagreement',\n", + " u'between',\n", + " u'themselves',\n", + " u',',\n", + " u'or',\n", + " u'producing',\n", + " u'coolness',\n", + " u'between',\n", + " u'their',\n", + " u'husbands',\n", + " u'.',\n", + " u'THE',\n", + " u'END']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the texts are just lists of strings\n", + "text2[141525:]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "FreqDist({u',': 18713, u'the': 13721, u'.': 6862, u'of': 6536, u'and': 6024, u'a': 4569, u'to': 4542, u';': 4072, u'in': 3916, u'that': 2982, ...})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build a frequency distribution\n", + "fdist1 = FreqDist(text1) \n", + "fdist1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u',', 18713),\n", + " (u'the', 13721),\n", + " (u'.', 6862),\n", + " (u'of', 6536),\n", + " (u'and', 6024),\n", + " (u'a', 4569),\n", + " (u'to', 4542),\n", + " (u';', 4072),\n", + " (u'in', 3916),\n", + " (u'that', 2982),\n", + " (u\"'\", 2684),\n", + " (u'-', 2552),\n", + " (u'his', 2459),\n", + " (u'it', 2209),\n", + " (u'I', 2124),\n", + " (u's', 1739),\n", + " (u'is', 1695),\n", + " (u'he', 1661),\n", + " (u'with', 1659),\n", + " (u'was', 1632)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fdist1.most_common(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "906" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fdist1['whale']" 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"text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fdist1.plot(20, cumulative=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'CIRCUMNAVIGATION',\n", + " u'Physiognomically',\n", + " u'apprehensiveness',\n", + " u'cannibalistically',\n", + " u'characteristically',\n", + " u'circumnavigating',\n", + " u'circumnavigation',\n", + " u'circumnavigations',\n", + " u'comprehensiveness',\n", + " u'hermaphroditical',\n", + " u'indiscriminately',\n", + " u'indispensableness',\n", + " u'irresistibleness',\n", + " u'physiognomically',\n", + " u'preternaturalness',\n", + " u'responsibilities',\n", + " u'simultaneousness',\n", + " u'subterraneousness',\n", + " u'supernaturalness',\n", + " u'superstitiousness',\n", + " u'uncomfortableness',\n", + " u'uncompromisedness',\n", + " u'undiscriminating',\n", + " u'uninterpenetratingly']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply a list comprehension to get words over 15 characters\n", + "V = set(text1)\n", + "long_words = [w for w in V if len(w) > 15]\n", + "sorted(long_words)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'#14-19teens',\n", + " u'#talkcity_adults',\n", + " u'((((((((((',\n", + " u'........',\n", + " u'Question',\n", + " u'actually',\n", + " u'anything',\n", + " u'computer',\n", + " u'cute.-ass',\n", + " u'everyone',\n", + " u'football',\n", + " u'innocent',\n", + " u'listening',\n", + " u'remember',\n", + " u'seriously',\n", + " u'something',\n", + " u'together',\n", + " u'tomorrow',\n", + " u'watching']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fdist2 = FreqDist(text5)\n", + "sorted(w for w in set(text5) if len(w) > 7 and fdist2[w] > 7)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "United States; fellow citizens; four years; years ago; Federal\n", + "Government; General Government; American people; Vice President; Old\n", + "World; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;\n", + "God bless; every citizen; Indian tribes; public debt; one another;\n", + "foreign nations; political parties\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Use a built-in tokenizer and tagger\n", - "text = word_tokenize(\"They refuse to permit us to obtain the refuse permit\")\n", - "nltk.pos_tag(text)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 44, - "text": [ - "[('They', 'PRP'),\n", - " ('refuse', 'VBP'),\n", - " ('to', 'TO'),\n", - " ('permit', 'VB'),\n", - " ('us', 'PRP'),\n", - " ('to', 'TO'),\n", - " ('obtain', 'VB'),\n", - " ('the', 'DT'),\n", - " ('refuse', 'NN'),\n", - " ('permit', 'NN')]" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Word similarity using a pre-tagged text\n", - "text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())\n", - "text.similar('woman')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "man time day year car moment world family house country child boy\n", - "state job way war girl place word work\n" - ] - } - ], - "prompt_number": 45 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Tagged words are saved as tuples\n", - "nltk.corpus.brown.tagged_words()[0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 46, - "text": [ - "[(u'The', u'AT'),\n", - " (u'Fulton', u'NP-TL'),\n", - " (u'County', u'NN-TL'),\n", - " (u'Grand', u'JJ-TL'),\n", - " (u'Jury', u'NN-TL'),\n", - " (u'said', u'VBD'),\n", - " (u'Friday', u'NR'),\n", - " (u'an', u'AT'),\n", - " (u'investigation', u'NN'),\n", - " (u'of', u'IN')]" - ] - } - ], - "prompt_number": 46 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nltk.corpus.brown.tagged_words(tagset='universal')[0:10]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 47, - "text": [ - "[(u'The', u'DET'),\n", - " (u'Fulton', u'NOUN'),\n", - " (u'County', u'NOUN'),\n", - " (u'Grand', u'ADJ'),\n", - " (u'Jury', u'NOUN'),\n", - " (u'said', u'VERB'),\n", - " (u'Friday', u'NOUN'),\n", - " (u'an', u'DET'),\n", - " (u'investigation', u'NOUN'),\n", - " (u'of', u'ADP')]" - ] - } - ], - "prompt_number": 47 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from nltk.corpus import brown\n", - "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n", - "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n", - "tag_fd.most_common()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 48, - "text": [ - "[(u'NOUN', 30640),\n", - " (u'VERB', 14399),\n", - " (u'ADP', 12355),\n", - " (u'.', 11928),\n", - " (u'DET', 11389),\n", - " (u'ADJ', 6706),\n", - " (u'ADV', 3349),\n", - " (u'CONJ', 2717),\n", - " (u'PRON', 2535),\n", - " (u'PRT', 2264),\n", - " (u'NUM', 2166),\n", - " (u'X', 106)]" - ] - } - ], - "prompt_number": 48 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Part of speech tag count for words following \"often\" in a text\n", - "brown_lrnd_tagged = brown.tagged_words(categories='learned', tagset='universal')\n", - "tags = [b[1] for (a, b) in nltk.bigrams(brown_lrnd_tagged) if a[0] == 'often']\n", - "fd = nltk.FreqDist(tags)\n", - "fd.tabulate()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "VERB ADV ADP ADJ . PRT \n", - " 37 8 7 6 4 2 \n" - ] - } - ], - "prompt_number": 49 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Load some raw sentences to tag\n", - "from nltk.corpus import brown\n", - "brown_tagged_sents = brown.tagged_sents(categories='news')\n", - "brown_sents = brown.sents(categories='news')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 50 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Default tagger (assigns same tag to each token)\n", - "tags = [tag for (word, tag) in brown.tagged_words(categories='news')]\n", - "nltk.FreqDist(tags).max()\n", - "raw = 'I do not like green eggs and ham, I do not like them Sam I am!'\n", - "tokens = word_tokenize(raw)\n", - "default_tagger = nltk.DefaultTagger('NN')\n", - "default_tagger.tag(tokens)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 51, - "text": [ - "[('I', 'NN'),\n", - " ('do', 'NN'),\n", - " ('not', 'NN'),\n", - " ('like', 'NN'),\n", - " ('green', 'NN'),\n", - " ('eggs', 'NN'),\n", - " ('and', 'NN'),\n", - " ('ham', 'NN'),\n", - " (',', 'NN'),\n", - " ('I', 'NN'),\n", - " ('do', 'NN'),\n", - " ('not', 'NN'),\n", - " ('like', 'NN'),\n", - " ('them', 'NN'),\n", - " ('Sam', 'NN'),\n", - " ('I', 'NN'),\n", - " ('am', 'NN'),\n", - " ('!', 'NN')]" - ] - } - ], - "prompt_number": 51 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Evaluate the performance against a tagged corpus\n", - "default_tagger.evaluate(brown_tagged_sents)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 52, - "text": [ - "0.13089484257215028" - ] - } - ], - "prompt_number": 52 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Training a unigram tagger\n", - "from nltk.corpus import brown\n", - "brown_tagged_sents = brown.tagged_sents(categories='news')\n", - "brown_sents = brown.sents(categories='news')\n", - "unigram_tagger = nltk.UnigramTagger(brown_tagged_sents)\n", - "unigram_tagger.tag(brown_sents[2007])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 53, - "text": [ - "[(u'Various', u'JJ'),\n", - " (u'of', u'IN'),\n", - " (u'the', u'AT'),\n", - " (u'apartments', u'NNS'),\n", - " (u'are', u'BER'),\n", - " (u'of', u'IN'),\n", - " (u'the', u'AT'),\n", - " (u'terrace', u'NN'),\n", - " (u'type', u'NN'),\n", - " (u',', u','),\n", - " (u'being', u'BEG'),\n", - " (u'on', u'IN'),\n", - " (u'the', u'AT'),\n", - " (u'ground', u'NN'),\n", - " (u'floor', u'NN'),\n", - " (u'so', u'QL'),\n", - " (u'that', u'CS'),\n", - " (u'entrance', u'NN'),\n", - " (u'is', u'BEZ'),\n", - " (u'direct', u'JJ'),\n", - " (u'.', u'.')]" - ] - } - ], - "prompt_number": 53 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Now evalute it\n", - "unigram_tagger.evaluate(brown_tagged_sents)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 54, - "text": [ - "0.9349006503968017" - ] - } - ], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Combining taggers\n", - "t0 = nltk.DefaultTagger('NN')\n", - "t1 = nltk.UnigramTagger(brown_tagged_sents, backoff=t0)\n", - "t2 = nltk.BigramTagger(brown_tagged_sents, backoff=t1)\n", - "t2.evaluate(brown_tagged_sents)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 55, - "text": [ - "0.9730592517453309" - ] - } - ], - "prompt_number": 55 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Classifying Text" + } + ], + "source": [ + "# word sequences that appear together unusually often\n", + "text4.collocations()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Raw Text Processing" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1176896" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# download raw text from an online repository\n", + "import urllib2\n", + "url = \"http://www.gutenberg.org/files/2554/2554.txt\"\n", + "response = urllib2.urlopen(url)\n", + "raw = response.read().decode('utf8')\n", + "len(raw)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "u'The Project Gutenberg EBook of Crime and Punishment, by Fyodor Dostoevsky\\r\\n'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw[:75]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "254352" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tokenize the raw text\n", + "from nltk import word_tokenize\n", + "tokens = word_tokenize(raw)\n", + "len(tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'The',\n", + " u'Project',\n", + " u'Gutenberg',\n", + " u'EBook',\n", + " u'of',\n", + " u'Crime',\n", + " u'and',\n", + " u'Punishment',\n", + " u',',\n", + " u'by']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'CHAPTER',\n", + " u'I',\n", + " u'On',\n", + " u'an',\n", + " u'exceptionally',\n", + " u'hot',\n", + " u'evening',\n", + " u'early',\n", + " u'in',\n", + " u'July',\n", + " u'a',\n", + " u'young',\n", + " u'man',\n", + " u'came',\n", + " u'out',\n", + " u'of',\n", + " u'the',\n", + " u'garret',\n", + " u'in',\n", + " u'which',\n", + " u'he',\n", + " u'lodged',\n", + " u'in',\n", + " u'S.',\n", + " u'Place',\n", + " u'and',\n", + " u'walked',\n", + " u'slowly',\n", + " u',',\n", + " u'as',\n", + " u'though',\n", + " u'in',\n", + " u'hesitation',\n", + " u',',\n", + " u'towards',\n", + " u'K.',\n", + " u'bridge',\n", + " u'.']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = nltk.Text(tokens)\n", + "text[1024:1062]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Katerina Ivanovna; Pyotr Petrovitch; Pulcheria Alexandrovna; Avdotya\n", + "Romanovna; Rodion Romanovitch; Marfa Petrovna; Sofya Semyonovna; old\n", + "woman; Project Gutenberg-tm; Porfiry Petrovitch; Amalia Ivanovna;\n", + "great deal; Nikodim Fomitch; young man; Ilya Petrovitch; n't know;\n", + "Project Gutenberg; Dmitri Prokofitch; Andrey Semyonovitch; Hay Market\n" + ] + } + ], + "source": [ + "text.collocations()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5338" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.find(\"PART I\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'BBC',\n", + " u'NEWS',\n", + " u'|',\n", + " u'Health',\n", + " u'|',\n", + " u'Blondes',\n", + " u\"'to\",\n", + " u'die',\n", + " u'out',\n", + " u'in']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# HTML parsing using the Beautiful Soup library\n", + "from bs4 import BeautifulSoup\n", + "url = \"http://news.bbc.co.uk/2/hi/health/2284783.stm\"\n", + "html = urllib2.urlopen(url).read().decode('utf8')\n", + "raw = BeautifulSoup(html).get_text()\n", + "tokens = word_tokenize(raw)\n", + "tokens[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Displaying 5 of 5 matches:\n", + "hey say too few people now carry the gene for blondes to last beyond the next \n", + "blonde hair is caused by a recessive gene . In order for a child to have blond\n", + " have blonde hair , it must have the gene on both sides of the family in the g\n", + "ere is a disadvantage of having that gene or by chance . They do n't disappear\n", + "des would disappear is if having the gene was a disadvantage and I do not thin\n" + ] + } + ], + "source": [ + "# isolate just the article text\n", + "tokens = tokens[110:390]\n", + "text = nltk.Text(tokens)\n", + "text.concordance('gene')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regular Expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# regular expression library\n", + "import re\n", + "wordlist = [w for w in nltk.corpus.words.words('en') if w.islower()]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'abaissed',\n", + " u'abandoned',\n", + " u'abased',\n", + " u'abashed',\n", + " u'abatised',\n", + " u'abed',\n", + " u'aborted',\n", + " u'abridged',\n", + " u'abscessed',\n", + " u'absconded']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# match the end of a word\n", + "[w for w in wordlist if re.search('ed$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'abjectly',\n", + " u'adjuster',\n", + " u'dejected',\n", + " u'dejectly',\n", + " u'injector',\n", + " u'majestic',\n", + " u'objectee',\n", + " u'objector',\n", + " u'rejecter',\n", + " u'rejector']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# wildcard matches any single character\n", + "[w for w in wordlist if re.search('^..j..t..$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'gold', u'golf', u'hold', u'hole']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# combination of caret (start of word) and sets\n", + "[w for w in wordlist if re.search('^[ghi][mno][jlk][def]$', w)]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'miiiiiiiiiiiiinnnnnnnnnnneeeeeeeeee',\n", + " u'miiiiiinnnnnnnnnneeeeeeee',\n", + " u'mine',\n", + " u'mmmmmmmmiiiiiiiiinnnnnnnnneeeeeeee']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat_words = sorted(set(w for w in nltk.corpus.nps_chat.words()))\n", + "\n", + "# plus symbol matches any number of times repeating\n", + "[w for w in chat_words if re.search('^m+i+n+e+$', w)]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'0.0085',\n", + " u'0.05',\n", + " u'0.1',\n", + " u'0.16',\n", + " u'0.2',\n", + " u'0.25',\n", + " u'0.28',\n", + " u'0.3',\n", + " u'0.4',\n", + " u'0.5']" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wsj = sorted(set(nltk.corpus.treebank.words()))\n", + "\n", + "# more advanced regex example\n", + "[w for w in wsj if re.search('^[0-9]+\\.[0-9]+$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'C$', u'US$']" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w in wsj if re.search('^[A-Z]+\\$$', w)]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'1614',\n", + " u'1637',\n", + " u'1787',\n", + " u'1901',\n", + " u'1903',\n", + " u'1917',\n", + " u'1925',\n", + " u'1929',\n", + " u'1933',\n", + " u'1934']" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w in wsj if re.search('^[0-9]{4}$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'10-day',\n", + " u'10-lap',\n", + " u'10-year',\n", + " u'100-share',\n", + " u'12-point',\n", + " u'12-year',\n", + " u'14-hour',\n", + " u'15-day',\n", + " u'150-point',\n", + " u'190-point']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w in wsj if re.search('^[0-9]+-[a-z]{3,5}$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'black-and-white',\n", + " u'bread-and-butter',\n", + " u'father-in-law',\n", + " u'machine-gun-toting',\n", + " u'savings-and-loan']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w in wsj if re.search('^[a-z]{5,}-[a-z]{2,3}-[a-z]{,6}$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'62%-owned',\n", + " u'Absorbed',\n", + " u'According',\n", + " u'Adopting',\n", + " u'Advanced',\n", + " u'Advancing',\n", + " u'Alfred',\n", + " u'Allied',\n", + " u'Annualized',\n", + " u'Anything']" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w in wsj if re.search('(ed|ing)$', w)][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'io', 549),\n", + " (u'ea', 476),\n", + " (u'ie', 331),\n", + " (u'ou', 329),\n", + " (u'ai', 261),\n", + " (u'ia', 253),\n", + " (u'ee', 217),\n", + " (u'oo', 174),\n", + " (u'ua', 109),\n", + " (u'au', 106),\n", + " (u'ue', 105),\n", + " (u'ui', 95)]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using \"findall\" to extract partial matches from words\n", + "fd = nltk.FreqDist(vs for word in wsj \n", + " for vs in re.findall(r'[aeiou]{2,}', word))\n", + "fd.most_common(12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalizing Text" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# NLTK has several word stemmers built in\n", + "porter = nltk.PorterStemmer()\n", + "lancaster = nltk.LancasterStemmer()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'UK',\n", + " u'Blond',\n", + " u\"'to\",\n", + " u'die',\n", + " u'out',\n", + " u'in',\n", + " u'200',\n", + " u\"years'\",\n", + " u'Scientist',\n", + " u'believ']" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[porter.stem(t) for t in tokens][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'uk',\n", + " u'blond',\n", + " u\"'to\",\n", + " u'die',\n", + " u'out',\n", + " u'in',\n", + " u'200',\n", + " u\"years'\",\n", + " u'sci',\n", + " u'believ']" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[lancaster.stem(t) for t in tokens][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[u'UK',\n", + " u'Blondes',\n", + " u\"'to\",\n", + " u'die',\n", + " u'out',\n", + " u'in',\n", + " u'200',\n", + " u\"years'\",\n", + " u'Scientists',\n", + " u'believe']" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnl = nltk.WordNetLemmatizer()\n", + "[wnl.lemmatize(t) for t in tokens][0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['That', 'U.S.A.', 'poster-print', 'costs', '$12.40', '...']" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# also has a tokenizer that takes a regular expression as a parameter\n", + "text = 'That U.S.A. poster-print costs $12.40...'\n", + "pattern = r'''(?x) # set flag to allow verbose regexps\n", + " ([A-Z]\\.)+ # abbreviations, e.g. U.S.A.\n", + " | \\w+(-\\w+)* # words with optional internal hyphens\n", + " | \\$?\\d+(\\.\\d+)?%? # currency and percentages, e.g. $12.40, 82%\n", + " | \\.\\.\\. # ellipsis\n", + " | [][.,;\"'?():-_`] # these are separate tokens; includes ], [\n", + "'''\n", + "nltk.regexp_tokenize(text, pattern)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tagging" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('They', 'PRP'),\n", + " ('refuse', 'VBP'),\n", + " ('to', 'TO'),\n", + " ('permit', 'VB'),\n", + " ('us', 'PRP'),\n", + " ('to', 'TO'),\n", + " ('obtain', 'VB'),\n", + " ('the', 'DT'),\n", + " ('refuse', 'NN'),\n", + " ('permit', 'NN')]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use a built-in tokenizer and tagger\n", + "text = word_tokenize(\"They refuse to permit us to obtain the refuse permit\")\n", + "nltk.pos_tag(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "man time day year car moment world family house country child boy\n", + "state job way war girl place word work\n" + ] + } + ], + "source": [ + "# Word similarity using a pre-tagged text\n", + "text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())\n", + "text.similar('woman')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'The', u'AT'),\n", + " (u'Fulton', u'NP-TL'),\n", + " (u'County', u'NN-TL'),\n", + " (u'Grand', u'JJ-TL'),\n", + " (u'Jury', u'NN-TL'),\n", + " (u'said', u'VBD'),\n", + " (u'Friday', u'NR'),\n", + " (u'an', u'AT'),\n", + " (u'investigation', u'NN'),\n", + " (u'of', u'IN')]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Tagged words are saved as tuples\n", + "nltk.corpus.brown.tagged_words()[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'The', u'DET'),\n", + " (u'Fulton', u'NOUN'),\n", + " (u'County', u'NOUN'),\n", + " (u'Grand', u'ADJ'),\n", + " (u'Jury', u'NOUN'),\n", + " (u'said', u'VERB'),\n", + " (u'Friday', u'NOUN'),\n", + " (u'an', u'DET'),\n", + " (u'investigation', u'NOUN'),\n", + " (u'of', u'ADP')]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nltk.corpus.brown.tagged_words(tagset='universal')[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'NOUN', 30640),\n", + " (u'VERB', 14399),\n", + " (u'ADP', 12355),\n", + " (u'.', 11928),\n", + " (u'DET', 11389),\n", + " (u'ADJ', 6706),\n", + " (u'ADV', 3349),\n", + " (u'CONJ', 2717),\n", + " (u'PRON', 2535),\n", + " (u'PRT', 2264),\n", + " (u'NUM', 2166),\n", + " (u'X', 106)]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nltk.corpus import brown\n", + "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n", + "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n", + "tag_fd.most_common()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VERB ADV ADP ADJ . PRT \n", + " 37 8 7 6 4 2 \n" + ] + } + ], + "source": [ + "# Part of speech tag count for words following \"often\" in a text\n", + "brown_lrnd_tagged = brown.tagged_words(categories='learned', tagset='universal')\n", + "tags = [b[1] for (a, b) in nltk.bigrams(brown_lrnd_tagged) if a[0] == 'often']\n", + "fd = nltk.FreqDist(tags)\n", + "fd.tabulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load some raw sentences to tag\n", + "from nltk.corpus import brown\n", + "brown_tagged_sents = brown.tagged_sents(categories='news')\n", + "brown_sents = brown.sents(categories='news')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('I', 'NN'),\n", + " ('do', 'NN'),\n", + " ('not', 'NN'),\n", + " ('like', 'NN'),\n", + " ('green', 'NN'),\n", + " ('eggs', 'NN'),\n", + " ('and', 'NN'),\n", + " ('ham', 'NN'),\n", + " (',', 'NN'),\n", + " ('I', 'NN'),\n", + " ('do', 'NN'),\n", + " ('not', 'NN'),\n", + " ('like', 'NN'),\n", + " ('them', 'NN'),\n", + " ('Sam', 'NN'),\n", + " ('I', 'NN'),\n", + " ('am', 'NN'),\n", + " ('!', 'NN')]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default tagger (assigns same tag to each token)\n", + "tags = [tag for (word, tag) in brown.tagged_words(categories='news')]\n", + "nltk.FreqDist(tags).max()\n", + "raw = 'I do not like green eggs and ham, I do not like them Sam I am!'\n", + "tokens = word_tokenize(raw)\n", + "default_tagger = nltk.DefaultTagger('NN')\n", + "default_tagger.tag(tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13089484257215028" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the performance against a tagged corpus\n", + "default_tagger.evaluate(brown_tagged_sents)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'Various', u'JJ'),\n", + " (u'of', u'IN'),\n", + " (u'the', u'AT'),\n", + " (u'apartments', u'NNS'),\n", + " (u'are', u'BER'),\n", + " (u'of', u'IN'),\n", + " (u'the', u'AT'),\n", + " (u'terrace', u'NN'),\n", + " (u'type', u'NN'),\n", + " (u',', u','),\n", + " (u'being', u'BEG'),\n", + " (u'on', u'IN'),\n", + " (u'the', u'AT'),\n", + " (u'ground', u'NN'),\n", + " (u'floor', u'NN'),\n", + " (u'so', u'QL'),\n", + " (u'that', u'CS'),\n", + " (u'entrance', u'NN'),\n", + " (u'is', u'BEZ'),\n", + " (u'direct', u'JJ'),\n", + " (u'.', u'.')]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Training a unigram tagger\n", + "from nltk.corpus import brown\n", + "brown_tagged_sents = brown.tagged_sents(categories='news')\n", + "brown_sents = brown.sents(categories='news')\n", + "unigram_tagger = nltk.UnigramTagger(brown_tagged_sents)\n", + "unigram_tagger.tag(brown_sents[2007])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9349006503968017" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now evalute it\n", + "unigram_tagger.evaluate(brown_tagged_sents)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9730592517453309" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Combining taggers\n", + "t0 = nltk.DefaultTagger('NN')\n", + "t1 = nltk.UnigramTagger(brown_tagged_sents, backoff=t0)\n", + "t2 = nltk.BigramTagger(brown_tagged_sents, backoff=t1)\n", + "t2.evaluate(brown_tagged_sents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classifying Text" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'last_letter': 'k'}" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define a feature extractor\n", + "def gender_features(word):\n", + " return {'last_letter': word[-1]}\n", + "gender_features('Shrek')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Prepare a list of examples\n", + "from nltk.corpus import names\n", + "labeled_names = ([(name, 'male') for name in names.words('male.txt')] +\n", + " [(name, 'female') for name in names.words('female.txt')])\n", + "import random\n", + "random.shuffle(labeled_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Process the names data\n", + "featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]\n", + "train_set, test_set = featuresets[500:], featuresets[:500]\n", + "classifier = nltk.NaiveBayesClassifier.train(train_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'male'" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.classify(gender_features('Neo'))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'female'" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.classify(gender_features('Trinity'))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.752\n" + ] + } + ], + "source": [ + "print(nltk.classify.accuracy(classifier, test_set))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Informative Features\n", + " last_letter = u'a' female : male = 35.4 : 1.0\n", + " last_letter = u'k' male : female = 31.9 : 1.0\n", + " last_letter = u'f' male : female = 17.4 : 1.0\n", + " last_letter = u'p' male : female = 11.3 : 1.0\n", + " last_letter = u'm' male : female = 10.2 : 1.0\n" + ] + } + ], + "source": [ + "classifier.show_most_informative_features(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Document classification\n", + "from nltk.corpus import movie_reviews\n", + "documents = [(list(movie_reviews.words(fileid)), category)\n", + " for category in movie_reviews.categories()\n", + " for fileid in movie_reviews.fileids(category)]\n", + "random.shuffle(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())\n", + "word_features = all_words.keys()[:2000]\n", + "\n", + "def document_features(document):\n", + " document_words = set(document)\n", + " features = {}\n", + " for word in word_features:\n", + " features['contains(%s)' % word] = (word in document_words)\n", + " return features" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "featuresets = [(document_features(d), c) for (d,c) in documents]\n", + "train_set, test_set = featuresets[100:], featuresets[:100]\n", + "classifier = nltk.NaiveBayesClassifier.train(train_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.64\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Define a feature extractor\n", - "def gender_features(word):\n", - " return {'last_letter': word[-1]}\n", - "gender_features('Shrek')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 56, - "text": [ - "{'last_letter': 'k'}" - ] - } - ], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Prepare a list of examples\n", - "from nltk.corpus import names\n", - "labeled_names = ([(name, 'male') for name in names.words('male.txt')] +\n", - " [(name, 'female') for name in names.words('female.txt')])\n", - "import random\n", - "random.shuffle(labeled_names)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Process the names data\n", - "featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]\n", - "train_set, test_set = featuresets[500:], featuresets[:500]\n", - "classifier = nltk.NaiveBayesClassifier.train(train_set)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 58 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.classify(gender_features('Neo'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 59, - "text": [ - "'male'" - ] - } - ], - "prompt_number": 59 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.classify(gender_features('Trinity'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 60, - "text": [ - "'female'" - ] - } - ], - "prompt_number": 60 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(nltk.classify.accuracy(classifier, test_set))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.752\n" - ] - } - ], - "prompt_number": 61 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.show_most_informative_features(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Most Informative Features\n", - " last_letter = u'a' female : male = 35.4 : 1.0\n", - " last_letter = u'k' male : female = 31.9 : 1.0\n", - " last_letter = u'f' male : female = 17.4 : 1.0\n", - " last_letter = u'p' male : female = 11.3 : 1.0\n", - " last_letter = u'm' male : female = 10.2 : 1.0\n" - ] - } - ], - "prompt_number": 62 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Document classification\n", - "from nltk.corpus import movie_reviews\n", - "documents = [(list(movie_reviews.words(fileid)), category)\n", - " for category in movie_reviews.categories()\n", - " for fileid in movie_reviews.fileids(category)]\n", - "random.shuffle(documents)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 63 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())\n", - "word_features = all_words.keys()[:2000]\n", - "\n", - "def document_features(document):\n", - " document_words = set(document)\n", - " features = {}\n", - " for word in word_features:\n", - " features['contains(%s)' % word] = (word in document_words)\n", - " return features" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 64 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "featuresets = [(document_features(d), c) for (d,c) in documents]\n", - "train_set, test_set = featuresets[100:], featuresets[:100]\n", - "classifier = nltk.NaiveBayesClassifier.train(train_set)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 65 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(nltk.classify.accuracy(classifier, test_set))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.64\n" - ] - } - ], - "prompt_number": 66 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.show_most_informative_features(5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Most Informative Features\n", - " contains(sans) = True neg : pos = 8.4 : 1.0\n", - " contains(uplifting) = True pos : neg = 8.2 : 1.0\n", - " contains(mediocrity) = True neg : pos = 7.7 : 1.0\n", - " contains(dismissed) = True pos : neg = 7.0 : 1.0\n", - " contains(overwhelmed) = True pos : neg = 6.3 : 1.0\n" - ] - } - ], - "prompt_number": 67 } ], - "metadata": {} + "source": [ + "print(nltk.classify.accuracy(classifier, test_set))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Informative Features\n", + " contains(sans) = True neg : pos = 8.4 : 1.0\n", + " contains(uplifting) = True pos : neg = 8.2 : 1.0\n", + " contains(mediocrity) = True neg : pos = 7.7 : 1.0\n", + " contains(dismissed) = True pos : neg = 7.0 : 1.0\n", + " contains(overwhelmed) = True pos : neg = 6.3 : 1.0\n" + ] + } + ], + "source": [ + "classifier.show_most_informative_features(5)" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/NetworkX.ipynb b/NetworkX.ipynb index 1243888..8c93160 100644 --- a/NetworkX.ipynb +++ b/NetworkX.ipynb @@ -1,431 +1,599 @@ { - "metadata": { - "name": "", - "signature": "sha256:06f6d2e9bde315dd9e32038adfb442db10a29fba982bf14484e8eccb255cef42" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "NetworkX" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks.\n", - "\n", - "With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import networkx as nx\n", - "G = nx.Graph()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# basic add nodes\n", - "G.add_node(1)\n", - "G.add_nodes_from([2, 3])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add a group of nodes at once\n", - "H = nx.path_graph(10)\n", - "G.add_nodes_from(H)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add another graph itself as a node\n", - "G.add_node(H)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add edges using similar methods\n", - "G.add_edge(1, 2)\n", - "e = (2, 3)\n", - "G.add_edge(*e)\n", - "G.add_edges_from([(1, 2), (1, 3)])\n", - "G.add_edges_from(H.edges())" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# can also remove or clear\n", - "G.remove_node(H)\n", - "G.clear()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# repeats are ignored\n", - "G.add_edges_from([(1,2),(1,3)])\n", - "G.add_node(1)\n", - "G.add_edge(1,2)\n", - "G.add_node('spam') # adds node \"spam\"\n", - "G.add_nodes_from('spam') # adds 4 nodes: 's', 'p', 'a', 'm'" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# get the number of nodes and edges\n", - "G.number_of_nodes(), G.number_of_edges()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "(8, 2)" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# access graph edges\n", - "G[1]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "{2: {}, 3: {}}" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G[1][2]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "{}" - ] - } - ], - "prompt_number": 10 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NetworkX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks.\n", + "\n", + "With NetworkX you can load and store networks in standard and nonstandard data formats, generate many types of random and classic networks, analyze network structure, build network models, design new network algorithms, draw networks, and much more." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import networkx as nx\n", + "G = nx.Graph()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# basic add nodes\n", + "G.add_node(1)\n", + "G.add_nodes_from([2, 3])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add a group of nodes at once\n", + "H = nx.path_graph(10)\n", + "G.add_nodes_from(H)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add another graph itself as a node\n", + "G.add_node(H)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add edges using similar methods\n", + "G.add_edge(1, 2)\n", + "e = (2, 3)\n", + "G.add_edge(*e)\n", + "G.add_edges_from([(1, 2), (1, 3)])\n", + "G.add_edges_from(H.edges())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# can also remove or clear\n", + "G.remove_node(H)\n", + "G.clear()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# repeats are ignored\n", + "G.add_edges_from([(1,2),(1,3)])\n", + "G.add_node(1)\n", + "G.add_edge(1,2)\n", + "G.add_node('spam') # adds node \"spam\"\n", + "G.add_nodes_from('spam') # adds 4 nodes: 's', 'p', 'a', 'm'" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# set an attribute of an edge\n", - "G.add_edge(1,3)\n", - "G[1][3]['color'] = 'blue'" - ], - "language": "python", + "data": { + "text/plain": [ + "(8, 2)" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# get the number of nodes and edges\n", + "G.number_of_nodes(), G.number_of_edges()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "FG = nx.Graph()\n", - "FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2), (3, 4, 0.375)])\n", - "for n, nbrs in FG.adjacency_iter():\n", - " for nbr, eattr in nbrs.items():\n", - " data = eattr['weight']\n", - " if data < 0.5: print('(%d, %d, %.3f)' % (n, nbr, data))" - ], - "language": "python", + "data": { + "text/plain": [ + "{2: {}, 3: {}}" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(1, 2, 0.125)\n", - "(2, 1, 0.125)\n", - "(3, 4, 0.375)\n", - "(4, 3, 0.375)\n" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# access graph edges\n", + "G[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# graph attribte\n", - "G = nx.Graph(day='Friday')\n", - "G.graph" - ], - "language": "python", + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "{'day': 'Friday'}" - ] - } - ], - "prompt_number": 13 - }, + "output_type": "execute_result" + } + ], + "source": [ + "G[1][2]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set an attribute of an edge\n", + "G.add_edge(1,3)\n", + "G[1][3]['color'] = 'blue'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# modifying an attribute\n", - "G.graph['day'] = 'Monday'\n", - "G.graph" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "{'day': 'Monday'}" - ] - } - ], - "prompt_number": 14 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 0.125)\n", + "(2, 1, 0.125)\n", + "(3, 4, 0.375)\n", + "(4, 3, 0.375)\n" + ] + } + ], + "source": [ + "FG = nx.Graph()\n", + "FG.add_weighted_edges_from([(1, 2, 0.125), (1, 3, 0.75), (2, 4, 1.2), (3, 4, 0.375)])\n", + "for n, nbrs in FG.adjacency_iter():\n", + " for nbr, eattr in nbrs.items():\n", + " data = eattr['weight']\n", + " if data < 0.5: print('(%d, %d, %.3f)' % (n, nbr, data))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# node attributes\n", - "G.add_node(1, time='5pm')\n", - "G.add_nodes_from([3], time='2pm')\n", - "G.node[1]['room'] = 714\n", - "G.nodes(data=True)" - ], - "language": "python", + "data": { + "text/plain": [ + "{'day': 'Friday'}" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "[(1, {'room': 714, 'time': '5pm'}), (3, {'time': '2pm'})]" - ] - } - ], - "prompt_number": 15 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# graph attribte\n", + "G = nx.Graph(day='Friday')\n", + "G.graph" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# edge attributes (weight is a special numeric attribute)\n", - "G.add_edge(1, 2, weight=4.7)\n", - "G.add_edges_from([(3, 4), (4, 5)], color='red')\n", - "G.add_edges_from([(1, 2 ,{'color': 'blue'}), (2, 3, {'weight' :8})])\n", - "G[1][2]['weight'] = 4.7\n", - "G.edge[1][2]['weight'] = 4" - ], - "language": "python", + "data": { + "text/plain": [ + "{'day': 'Monday'}" + ] + }, + "execution_count": 14, "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# modifying an attribute\n", + "G.graph['day'] = 'Monday'\n", + "G.graph" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# directed graph\n", - "DG = nx.DiGraph()\n", - "DG.add_weighted_edges_from([(1, 2 ,0.5), (3, 1, 0.75)])\n", - "DG.out_degree(1, weight='weight')" - ], - "language": "python", + "data": { + "text/plain": [ + "[(1, {'room': 714, 'time': '5pm'}), (3, {'time': '2pm'})]" + ] + }, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "0.5" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# node attributes\n", + "G.add_node(1, time='5pm')\n", + "G.add_nodes_from([3], time='2pm')\n", + "G.node[1]['room'] = 714\n", + "G.nodes(data=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# edge attributes (weight is a special numeric attribute)\n", + "G.add_edge(1, 2, weight=4.7)\n", + "G.add_edges_from([(3, 4), (4, 5)], color='red')\n", + "G.add_edges_from([(1, 2 ,{'color': 'blue'}), (2, 3, {'weight' :8})])\n", + "G[1][2]['weight'] = 4.7\n", + "G.edge[1][2]['weight'] = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "DG.degree(1, weight='weight')" - ], - "language": "python", + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "1.25" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# directed graph\n", + "DG = nx.DiGraph()\n", + "DG.add_weighted_edges_from([(1, 2 ,0.5), (3, 1, 0.75)])\n", + "DG.out_degree(1, weight='weight')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "DG.successors(1)" - ], - "language": "python", + "data": { + "text/plain": [ + "1.25" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "[2]" - ] - } - ], - "prompt_number": 19 - }, + "output_type": "execute_result" + } + ], + "source": [ + "DG.degree(1, weight='weight')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "DG.predecessors(1)" - ], - "language": "python", + "data": { + "text/plain": [ + "[2]" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "[3]" - ] - } - ], - "prompt_number": 20 - }, + "output_type": "execute_result" + } + ], + "source": [ + "DG.successors(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# convert to undirected graph\n", - "H = nx.Graph(G)" - ], - "language": "python", + "data": { + "text/plain": [ + "[3]" + ] + }, + "execution_count": 20, "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, + "output_type": "execute_result" + } + ], + "source": [ + "DG.predecessors(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# convert to undirected graph\n", + "H = nx.Graph(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ { - 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- "text": [ - "" - ] - } - ], - "prompt_number": 22 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# basic graph drawing capability\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "nx.draw(G)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/NumPy.ipynb b/NumPy.ipynb index d099a5d..a84e2d7 100644 --- a/NumPy.ipynb +++ b/NumPy.ipynb @@ -1,1228 +1,1337 @@ { - "metadata": { - "name": "", - "signature": "sha256:c267ffc8b9b25d0866a5f426cb63d858dbe885875bd740cf4ed9045f08280b06" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "NumPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", - "\n", - "- a powerful N-dimensional array object\n", - "- sophisticated (broadcasting) functions\n", - "- tools for integrating C/C++ and Fortran code\n", - "- useful linear algebra, Fourier transform, and random number capabilities\n", - "\n", - "Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.\n", - "\n", - "Numpy is licensed under the BSD license, enabling reuse with few restrictions." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numpy import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# declare a vector using a list as the argument\n", - "v = array([1,2,3,4])\n", - "v" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "array([1, 2, 3, 4])" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# declare a matrix using a nested list as the argument\n", - "M = array([[1,2],[3,4]])\n", - "M" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "array([[1, 2],\n", - " [3, 4]])" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# still the same core type with different shapes\n", - "type(v), type(M)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "(numpy.ndarray, numpy.ndarray)" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.size" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "4" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# arguments: start, stop, step\n", - "x = arange(0, 10, 1)\n", - "x" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "linspace(0, 10, 25)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "array([ 0. , 0.41666667, 0.83333333, 1.25 ,\n", - " 1.66666667, 2.08333333, 2.5 , 2.91666667,\n", - " 3.33333333, 3.75 , 4.16666667, 4.58333333,\n", - " 5. , 5.41666667, 5.83333333, 6.25 ,\n", - " 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", - " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" - ] - } - ], - "prompt_number": 7 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n", + "\n", + "- a powerful N-dimensional array object\n", + "- sophisticated (broadcasting) functions\n", + "- tools for integrating C/C++ and Fortran code\n", + "- useful linear algebra, Fourier transform, and random number capabilities\n", + "\n", + "Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.\n", + "\n", + "Numpy is licensed under the BSD license, enabling reuse with few restrictions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "logspace(0, 10, 10, base=e)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([1, 2, 3, 4])" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "array([ 1.00000000e+00, 3.03773178e+00, 9.22781435e+00,\n", - " 2.80316249e+01, 8.51525577e+01, 2.58670631e+02,\n", - " 7.85771994e+02, 2.38696456e+03, 7.25095809e+03,\n", - " 2.20264658e+04])" - ] - } - ], - "prompt_number": 8 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# declare a vector using a list as the argument\n", + "v = array([1,2,3,4])\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "x, y = mgrid[0:5, 0:5]\n", - "x" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "array([[0, 0, 0, 0, 0],\n", - " [1, 1, 1, 1, 1],\n", - " [2, 2, 2, 2, 2],\n", - " [3, 3, 3, 3, 3],\n", - " [4, 4, 4, 4, 4]])" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# declare a matrix using a nested list as the argument\n", + "M = array([[1,2],[3,4]])\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "y" - ], - "language": "python", + "data": { + "text/plain": [ + "(numpy.ndarray, numpy.ndarray)" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "array([[0, 1, 2, 3, 4],\n", - " [0, 1, 2, 3, 4],\n", - " [0, 1, 2, 3, 4],\n", - " [0, 1, 2, 3, 4],\n", - " [0, 1, 2, 3, 4]])" - ] - } - ], - "prompt_number": 10 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# still the same core type with different shapes\n", + "type(v), type(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numpy import random" - ], - "language": "python", + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 5, "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, + "output_type": "execute_result" + } + ], + "source": [ + "M.size" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "random.rand(5,5)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "array([[ 0.88096372, 0.53238822, 0.17775764, 0.76591586, 0.6127709 ],\n", - " [ 0.51258827, 0.05731522, 0.05610599, 0.36338405, 0.29548536],\n", - " [ 0.54649788, 0.60544106, 0.38081415, 0.5717322 , 0.2426889 ],\n", - " [ 0.96448533, 0.22105112, 0.41292727, 0.40652867, 0.57179488],\n", - " [ 0.55815745, 0.22049273, 0.30680923, 0.82881023, 0.36665264]])" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# arguments: start, stop, step\n", + "x = arange(0, 10, 1)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# normal distribution\n", - "random.randn(5,5)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 0. , 0.41666667, 0.83333333, 1.25 ,\n", + " 1.66666667, 2.08333333, 2.5 , 2.91666667,\n", + " 3.33333333, 3.75 , 4.16666667, 4.58333333,\n", + " 5. , 5.41666667, 5.83333333, 6.25 ,\n", + " 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", + " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "array([[ 0.40801047, -0.36738023, 0.0654462 , 0.16108406, 0.08391533],\n", - " [-1.31495404, -1.31773965, 1.01225524, 0.28113264, -1.32523908],\n", - " [ 1.09106398, -0.37571802, 2.01780085, 0.16072945, 1.0688331 ],\n", - " [ 0.54306468, 0.9436181 , -2.60779314, 0.27348637, 0.60950091],\n", - " [-1.0055051 , 1.77771874, 0.33209667, -0.10772336, -0.66501805]])" - ] - } - ], - "prompt_number": 13 - }, + "output_type": "execute_result" + } + ], + "source": [ + "linspace(0, 10, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "diag([1,2,3])" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 1.00000000e+00, 3.03773178e+00, 9.22781435e+00,\n", + " 2.80316249e+01, 8.51525577e+01, 2.58670631e+02,\n", + " 7.85771994e+02, 2.38696456e+03, 7.25095809e+03,\n", + " 2.20264658e+04])" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "array([[1, 0, 0],\n", - " [0, 2, 0],\n", - " [0, 0, 3]])" - ] - } - ], - "prompt_number": 14 - }, + "output_type": "execute_result" + } + ], + "source": [ + "logspace(0, 10, 10, base=e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.itemsize" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 1],\n", + " [2, 2, 2, 2, 2],\n", + " [3, 3, 3, 3, 3],\n", + " [4, 4, 4, 4, 4]])" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "4" - ] - } - ], - "prompt_number": 15 - }, + "output_type": "execute_result" + } + ], + "source": [ + "x, y = mgrid[0:5, 0:5]\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.nbytes" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4]])" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "16" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import random" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "M.ndim" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0.88096372, 0.53238822, 0.17775764, 0.76591586, 0.6127709 ],\n", + " [ 0.51258827, 0.05731522, 0.05610599, 0.36338405, 0.29548536],\n", + " [ 0.54649788, 0.60544106, 0.38081415, 0.5717322 , 0.2426889 ],\n", + " [ 0.96448533, 0.22105112, 0.41292727, 0.40652867, 0.57179488],\n", + " [ 0.55815745, 0.22049273, 0.30680923, 0.82881023, 0.36665264]])" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "2" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "execute_result" + } + ], + "source": [ + "random.rand(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "v[0], M[1,1]" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0.40801047, -0.36738023, 0.0654462 , 0.16108406, 0.08391533],\n", + " [-1.31495404, -1.31773965, 1.01225524, 0.28113264, -1.32523908],\n", + " [ 1.09106398, -0.37571802, 2.01780085, 0.16072945, 1.0688331 ],\n", + " [ 0.54306468, 0.9436181 , -2.60779314, 0.27348637, 0.60950091],\n", + " [-1.0055051 , 1.77771874, 0.33209667, -0.10772336, -0.66501805]])" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "(1, 4)" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# normal distribution\n", + "random.randn(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "M[1]" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[1, 0, 0],\n", + " [0, 2, 0],\n", + " [0, 0, 3]])" + ] + }, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "array([3, 4])" - ] - } - ], - "prompt_number": 19 - }, + "output_type": "execute_result" + } + ], + "source": [ + "diag([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# assign new value\n", - "M[0,0] = 7\n", - "M" - ], - "language": "python", + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "array([[7, 2],\n", - " [3, 4]])" - ] - } - ], - "prompt_number": 20 - }, + "output_type": "execute_result" + } + ], + "source": [ + "M.itemsize" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "M[0,:] = 0\n", - "M" - ], - "language": "python", + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "array([[0, 0],\n", - " [3, 4]])" - ] - } - ], - "prompt_number": 21 - }, + "output_type": "execute_result" + } + ], + "source": [ + "M.nbytes" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# slicing works just like with lists\n", - "A = array([1,2,3,4,5])\n", - "A[1:3]" - ], - "language": "python", + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "array([2, 3])" - ] - } - ], - "prompt_number": 22 - }, + "output_type": "execute_result" + } + ], + "source": [ + "M.ndim" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "A = array([[n+m*10 for n in range(5)] for m in range(5)])\n", - "A" - ], - "language": "python", + "data": { + "text/plain": [ + "(1, 4)" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, - "text": [ - "array([[ 0, 1, 2, 3, 4],\n", - " [10, 11, 12, 13, 14],\n", - " [20, 21, 22, 23, 24],\n", - " [30, 31, 32, 33, 34],\n", - " [40, 41, 42, 43, 44]])" - ] - } - ], - "prompt_number": 23 - }, + "output_type": "execute_result" + } + ], + "source": [ + "v[0], M[1,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "row_indices = [1, 2, 3]\n", - "A[row_indices]" - ], - "language": "python", + "data": { + "text/plain": [ + "array([3, 4])" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "array([[10, 11, 12, 13, 14],\n", - " [20, 21, 22, 23, 24],\n", - " [30, 31, 32, 33, 34]])" - ] - } - ], - "prompt_number": 24 - }, + "output_type": "execute_result" + } + ], + "source": [ + "M[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# index masking\n", - "B = array([n for n in range(5)])\n", - "row_mask = array([True, False, True, False, False])\n", - "B[row_mask]" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[7, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "array([0, 2])" - ] - } - ], - "prompt_number": 25 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# assign new value\n", + "M[0,0] = 7\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/plain": [ + "array([[0, 0],\n", + " [3, 4]])" + ] + }, + "execution_count": 21, "metadata": {}, - "source": [ - "Linear Algebra" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "M[0,:] = 0\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "v1 = arange(0, 5)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([2, 3])" + ] + }, + "execution_count": 22, "metadata": {}, - "outputs": [], - "prompt_number": 26 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# slicing works just like with lists\n", + "A = array([1,2,3,4,5])\n", + "A[1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "v1 + 2" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 27, - "text": [ - "array([2, 3, 4, 5, 6])" - ] - } - ], - "prompt_number": 27 - }, + "output_type": "execute_result" + } + ], + "source": [ + "A = array([[n+m*10 for n in range(5)] for m in range(5)])\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "v1 * 2" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34]])" + ] + }, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "array([0, 2, 4, 6, 8])" - ] - } - ], - "prompt_number": 28 - }, + "output_type": "execute_result" + } + ], + "source": [ + "row_indices = [1, 2, 3]\n", + "A[row_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "v1 * v1" - ], - "language": "python", + "data": { + "text/plain": [ + "array([0, 2])" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "array([ 0, 1, 4, 9, 16])" - ] - } - ], - "prompt_number": 29 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# index masking\n", + "B = array([n for n in range(5)])\n", + "row_mask = array([True, False, True, False, False])\n", + "B[row_mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Algebra" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "v1 = arange(0, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "dot(v1, v1)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([2, 3, 4, 5, 6])" + ] + }, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "30" - ] - } - ], - "prompt_number": 30 - }, + "output_type": "execute_result" + } + ], + "source": [ + "v1 + 2" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "dot(A, v1)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([0, 2, 4, 6, 8])" + ] + }, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "array([ 30, 130, 230, 330, 430])" - ] - } - ], - "prompt_number": 31 - }, + "output_type": "execute_result" + } + ], + "source": [ + "v1 * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# cast changes behavior of + - * etc. to use matrix algebra\n", - "M = matrix(A)\n", - "M * M" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 0, 1, 4, 9, 16])" + ] + }, + "execution_count": 29, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "matrix([[ 300, 310, 320, 330, 340],\n", - " [1300, 1360, 1420, 1480, 1540],\n", - " [2300, 2410, 2520, 2630, 2740],\n", - " [3300, 3460, 3620, 3780, 3940],\n", - " [4300, 4510, 4720, 4930, 5140]])" - ] - } - ], - "prompt_number": 32 - }, + "output_type": "execute_result" + } + ], + "source": [ + "v1 * v1" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# inner product\n", - "v.T * v" - ], - "language": "python", + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 30, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "array([ 1, 4, 9, 16])" - ] - } - ], - "prompt_number": 33 - }, + "output_type": "execute_result" + } + ], + "source": [ + "dot(v1, v1)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "C = matrix([[1j, 2j], [3j, 4j]])\n", - "C" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 30, 130, 230, 330, 430])" + ] + }, + "execution_count": 31, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 34, - "text": [ - "matrix([[ 0.+1.j, 0.+2.j],\n", - " [ 0.+3.j, 0.+4.j]])" - ] - } - ], - "prompt_number": 34 - }, + "output_type": "execute_result" + } + ], + "source": [ + "dot(A, v1)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "conjugate(C)" - ], - "language": "python", + "data": { + "text/plain": [ + "matrix([[ 300, 310, 320, 330, 340],\n", + " [1300, 1360, 1420, 1480, 1540],\n", + " [2300, 2410, 2520, 2630, 2740],\n", + " [3300, 3460, 3620, 3780, 3940],\n", + " [4300, 4510, 4720, 4930, 5140]])" + ] + }, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "matrix([[ 0.-1.j, 0.-2.j],\n", - " [ 0.-3.j, 0.-4.j]])" - ] - } - ], - "prompt_number": 35 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# cast changes behavior of + - * etc. to use matrix algebra\n", + "M = matrix(A)\n", + "M * M" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# inverse\n", - "C.I" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 1, 4, 9, 16])" + ] + }, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "matrix([[ 0.+2.j , 0.-1.j ],\n", - " [ 0.-1.5j, 0.+0.5j]])" - ] - } - ], - "prompt_number": 36 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# inner product\n", + "v.T * v" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/plain": [ + "matrix([[ 0.+1.j, 0.+2.j],\n", + " [ 0.+3.j, 0.+4.j]])" + ] + }, + "execution_count": 34, "metadata": {}, - "source": [ - "Statistics" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "C = matrix([[1j, 2j], [3j, 4j]])\n", + "C" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "mean(A[:,3])" - ], - "language": "python", + "data": { + "text/plain": [ + "matrix([[ 0.-1.j, 0.-2.j],\n", + " [ 0.-3.j, 0.-4.j]])" + ] + }, + "execution_count": 35, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 37, - "text": [ - "23.0" - ] - } - ], - "prompt_number": 37 - }, + "output_type": "execute_result" + } + ], + "source": [ + "conjugate(C)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "std(A[:,3]), var(A[:,3])" - ], - "language": "python", + "data": { + "text/plain": [ + "matrix([[ 0.+2.j , 0.-1.j ],\n", + " [ 0.-1.5j, 0.+0.5j]])" + ] + }, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "(14.142135623730951, 200.0)" - ] - } - ], - "prompt_number": 38 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# inverse\n", + "C.I" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "A[:,3].min(), A[:,3].max()" - ], - "language": "python", + "data": { + "text/plain": [ + "23.0" + ] + }, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 39, - "text": [ - "(3, 43)" - ] - } - ], - "prompt_number": 39 - }, + "output_type": "execute_result" + } + ], + "source": [ + "mean(A[:,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "d = arange(1, 10)\n", - "sum(d), prod(d)" - ], - "language": "python", + "data": { + "text/plain": [ + "(14.142135623730951, 200.0)" + ] + }, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 40, - "text": [ - "(45, 362880)" - ] - } - ], - "prompt_number": 40 - }, + "output_type": "execute_result" + } + ], + "source": [ + "std(A[:,3]), var(A[:,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "cumsum(d)" - ], - "language": "python", + "data": { + "text/plain": [ + "(3, 43)" + ] + }, + "execution_count": 39, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "array([ 1, 3, 6, 10, 15, 21, 28, 36, 45])" - ] - } - ], - "prompt_number": 41 - }, + "output_type": "execute_result" + } + ], + "source": [ + "A[:,3].min(), A[:,3].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "cumprod(d)" - ], - "language": "python", + "data": { + "text/plain": [ + "(45, 362880)" + ] + }, + "execution_count": 40, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 42, - "text": [ - "array([ 1, 2, 6, 24, 120, 720, 5040, 40320,\n", - " 362880])" - ] - } - ], - "prompt_number": 42 - }, + "output_type": "execute_result" + } + ], + "source": [ + "d = arange(1, 10)\n", + "sum(d), prod(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# sum of diagonal\n", - "trace(A)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 1, 3, 6, 10, 15, 21, 28, 36, 45])" + ] + }, + "execution_count": 41, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 43, - "text": [ - "110" - ] - } - ], - "prompt_number": 43 - }, + "output_type": "execute_result" + } + ], + "source": [ + "cumsum(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "m = random.rand(3, 3)\n", - "m" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 1, 2, 6, 24, 120, 720, 5040, 40320,\n", + " 362880])" + ] + }, + "execution_count": 42, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 44, - "text": [ - "array([[ 0.37938474, 0.93337301, 0.10864521],\n", - " [ 0.144712 , 0.12270014, 0.622434 ],\n", - " [ 0.16307745, 0.4850791 , 0.59703797]])" - ] - } - ], - "prompt_number": 44 - }, + "output_type": "execute_result" + } + ], + "source": [ + "cumprod(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# use axis parameter to specify how function behaves\n", - "m.max(), m.max(axis=0)" - ], - "language": "python", + "data": { + "text/plain": [ + "110" + ] + }, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 45, - "text": [ - "(0.93337300979654614, array([ 0.37938474, 0.93337301, 0.622434 ]))" - ] - } - ], - "prompt_number": 45 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# sum of diagonal\n", + "trace(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "A" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0.37938474, 0.93337301, 0.10864521],\n", + " [ 0.144712 , 0.12270014, 0.622434 ],\n", + " [ 0.16307745, 0.4850791 , 0.59703797]])" + ] + }, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 46, - "text": [ - "array([[ 0, 1, 2, 3, 4],\n", - " [10, 11, 12, 13, 14],\n", - " [20, 21, 22, 23, 24],\n", - " [30, 31, 32, 33, 34],\n", - " [40, 41, 42, 43, 44]])" - ] - } - ], - "prompt_number": 46 - }, + "output_type": "execute_result" + } + ], + "source": [ + "m = random.rand(3, 3)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reshape without copying underlying data\n", - "n, m = A.shape\n", - "B = A.reshape((1,n*m))\n", - "\n", - "B" - ], - "language": "python", + "data": { + "text/plain": [ + "(0.93337300979654614, array([ 0.37938474, 0.93337301, 0.622434 ]))" + ] + }, + "execution_count": 45, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 47, - "text": [ - "array([[ 0, 1, 2, 3, 4, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", - " 32, 33, 34, 40, 41, 42, 43, 44]])" - ] - } - ], - "prompt_number": 47 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# use axis parameter to specify how function behaves\n", + "m.max(), m.max(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# modify the array\n", - "B[0,0:5] = 5\n", - "B" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 46, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 48, - "text": [ - "array([[ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", - " 32, 33, 34, 40, 41, 42, 43, 44]])" - ] - } - ], - "prompt_number": 48 - }, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# also changed\n", - "A" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", + " 32, 33, 34, 40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 47, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 49, - "text": [ - "array([[ 5, 5, 5, 5, 5],\n", - " [10, 11, 12, 13, 14],\n", - " [20, 21, 22, 23, 24],\n", - " [30, 31, 32, 33, 34],\n", - " [40, 41, 42, 43, 44]])" - ] - } - ], - "prompt_number": 49 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# reshape without copying underlying data\n", + "n, m = A.shape\n", + "B = A.reshape((1,n*m))\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# creates a copy\n", - "B = A.flatten()\n", - "B" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", + " 32, 33, 34, 40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 50, - "text": [ - "array([ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", - " 32, 33, 34, 40, 41, 42, 43, 44])" - ] - } - ], - "prompt_number": 50 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# modify the array\n", + "B[0,0:5] = 5\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# can insert a dimension in an array\n", - "v = array([1,2,3])\n", - "v[:, newaxis], v[:,newaxis].shape, v[newaxis,:].shape" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 5, 5, 5, 5, 5],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 49, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 51, - "text": [ - "(array([[1],\n", - " [2],\n", - " [3]]), (3L, 1L), (1L, 3L))" - ] - } - ], - "prompt_number": 51 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# also changed\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "repeat(v, 3)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31,\n", + " 32, 33, 34, 40, 41, 42, 43, 44])" + ] + }, + "execution_count": 50, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 52, - "text": [ - "array([1, 1, 1, 2, 2, 2, 3, 3, 3])" - ] - } - ], - "prompt_number": 52 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# creates a copy\n", + "B = A.flatten()\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "tile(v, 3)" - ], - "language": "python", + "data": { + "text/plain": [ + "(array([[1],\n", + " [2],\n", + " [3]]), (3L, 1L), (1L, 3L))" + ] + }, + "execution_count": 51, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 53, - "text": [ - "array([1, 2, 3, 1, 2, 3, 1, 2, 3])" - ] - } - ], - "prompt_number": 53 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# can insert a dimension in an array\n", + "v = array([1,2,3])\n", + "v[:, newaxis], v[:,newaxis].shape, v[newaxis,:].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "w = array([5, 6])" - ], - "language": "python", + "data": { + "text/plain": [ + "array([1, 1, 1, 2, 2, 2, 3, 3, 3])" + ] + }, + "execution_count": 52, "metadata": {}, - "outputs": [], - "prompt_number": 54 - }, + "output_type": "execute_result" + } + ], + "source": [ + "repeat(v, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "concatenate((v, w), axis=0)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([1, 2, 3, 1, 2, 3, 1, 2, 3])" + ] + }, + "execution_count": 53, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 55, - "text": [ - "array([1, 2, 3, 5, 6])" - ] - } - ], - "prompt_number": 55 - }, + "output_type": "execute_result" + } + ], + "source": [ + "tile(v, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "w = array([5, 6])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# deep copy\n", - "B = copy(A)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([1, 2, 3, 5, 6])" + ] + }, + "execution_count": 55, "metadata": {}, - "outputs": [], - "prompt_number": 56 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "concatenate((v, w), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# deep copy\n", + "B = copy(A)" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Pandas.ipynb b/Pandas.ipynb index c04736c..52dc310 100644 --- a/Pandas.ipynb +++ b/Pandas.ipynb @@ -1,3229 +1,4206 @@ { - "metadata": { - "name": "", - "signature": "sha256:558b9d5aa827b14ba9d7a2ace9308e6784ddbab52522d8eda3a4ff07c2531967" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "General" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create a series\n", - "s = pd.Series([1,3,5,np.nan,6,8])\n", - "s" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "0 1\n", - "1 3\n", - "2 5\n", - "3 NaN\n", - "4 6\n", - "5 8\n", - "dtype: float64" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create a data frame\n", - "dates = pd.date_range('20130101',periods=6)\n", - "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n", - "df" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
2013-01-06 0.000782 0.146544 0.033628-0.419772
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - " A B C D\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", - "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", - "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422\n", - "2013-01-06 0.000782 0.146544 0.033628 -0.419772" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# another way to create a data frame\n", - "df2 = pd.DataFrame(\n", - " { 'A' : 1.,\n", - " 'B' : pd.Timestamp('20130102'),\n", - " 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n", - " 'D' : np.array([3] * 4,dtype='int32'),\n", - " 'E' : 'foo' })\n", - "df2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCDE
0 12013-01-02 1 3 foo
1 12013-01-02 1 3 foo
2 12013-01-02 1 3 foo
3 12013-01-02 1 3 foo
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - " A B C D E\n", - "0 1 2013-01-02 1 3 foo\n", - "1 1 2013-01-02 1 3 foo\n", - "2 1 2013-01-02 1 3 foo\n", - "3 1 2013-01-02 1 3 foo" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "df2.dtypes" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "A float64\n", - "B datetime64[ns]\n", - "C float32\n", - "D int32\n", - "E object\n", - "dtype: object" - ] - } - ], - "prompt_number": 5 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.head()" - ], - "language": "python", + "data": { + "text/plain": [ + "0 1\n", + "1 3\n", + "2 5\n", + "3 NaN\n", + "4 6\n", + "5 8\n", + "dtype: float64" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - " A B C D\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", - "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", - "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422" - ] - } - ], - "prompt_number": 6 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# create a series\n", + "s = pd.Series([1,3,5,np.nan,6,8])\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.index" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
2013-01-06 0.000782 0.146544 0.033628-0.419772
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", + "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", + "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422\n", + "2013-01-06 0.000782 0.146544 0.033628 -0.419772" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "\n", - "[2013-01-01, ..., 2013-01-06]\n", - "Length: 6, Freq: D, Timezone: None" - ] - } - ], - "prompt_number": 7 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# create a data frame\n", + "dates = pd.date_range('20130101',periods=6)\n", + "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.columns" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCDE
0 12013-01-02 1 3 foo
1 12013-01-02 1 3 foo
2 12013-01-02 1 3 foo
3 12013-01-02 1 3 foo
\n", + "
" + ], + "text/plain": [ + " A B C D E\n", + "0 1 2013-01-02 1 3 foo\n", + "1 1 2013-01-02 1 3 foo\n", + "2 1 2013-01-02 1 3 foo\n", + "3 1 2013-01-02 1 3 foo" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "Index([u'A', u'B', u'C', u'D'], dtype='object')" - ] - } - ], - "prompt_number": 8 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# another way to create a data frame\n", + "df2 = pd.DataFrame(\n", + " { 'A' : 1.,\n", + " 'B' : pd.Timestamp('20130102'),\n", + " 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n", + " 'D' : np.array([3] * 4,dtype='int32'),\n", + " 'E' : 'foo' })\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.values" - ], - "language": "python", + "data": { + "text/plain": [ + "A float64\n", + "B datetime64[ns]\n", + "C float32\n", + "D int32\n", + "E object\n", + "dtype: object" + ] + }, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "array([[ 2.05240362e-01, 5.27602841e-01, 6.10052272e-01,\n", - " 4.69292270e-01],\n", - " [ 8.18112883e-01, -8.94389618e-01, -1.60283098e+00,\n", - " 8.62169894e-01],\n", - " [ -1.46210940e+00, 4.83201108e-01, -1.04497297e+00,\n", - " -5.34226832e-01],\n", - " [ 7.19196807e-01, -4.99809344e-01, 1.14578824e+00,\n", - " -8.09525609e-01],\n", - " [ -1.16105080e+00, -1.15774007e-01, -6.24412514e-01,\n", - " 4.74421893e-01],\n", - " [ 7.82298420e-04, 1.46543576e-01, 3.36282758e-02,\n", - " -4.19771560e-01]])" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df2.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# quick data summary\n", - "df.describe()" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", + "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", + "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
count 6.000000 6.000000 6.000000 6.000000
mean-0.146638-0.058771-0.247125 0.007060
std 0.957650 0.561381 1.036400 0.679012
min-1.462109-0.894390-1.602831-0.809526
25%-0.870593-0.403801-0.939833-0.505613
50% 0.103011 0.015385-0.295392 0.024760
75% 0.590708 0.399037 0.465946 0.473139
max 0.818113 0.527603 1.145788 0.862170
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - " A B C D\n", - "count 6.000000 6.000000 6.000000 6.000000\n", - "mean -0.146638 -0.058771 -0.247125 0.007060\n", - "std 0.957650 0.561381 1.036400 0.679012\n", - "min -1.462109 -0.894390 -1.602831 -0.809526\n", - "25% -0.870593 -0.403801 -0.939833 -0.505613\n", - "50% 0.103011 0.015385 -0.295392 0.024760\n", - "75% 0.590708 0.399037 0.465946 0.473139\n", - "max 0.818113 0.527603 1.145788 0.862170" - ] - } - ], - "prompt_number": 10 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.T" - ], - "language": "python", + "data": { + "text/plain": [ + "\n", + "[2013-01-01, ..., 2013-01-06]\n", + "Length: 6, Freq: D, Timezone: None" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "html": [ - "
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2013-01-01 00:00:002013-01-02 00:00:002013-01-03 00:00:002013-01-04 00:00:002013-01-05 00:00:002013-01-06 00:00:00
A 0.205240 0.818113-1.462109 0.719197-1.161051 0.000782
B 0.527603-0.894390 0.483201-0.499809-0.115774 0.146544
C 0.610052-1.602831-1.044973 1.145788-0.624413 0.033628
D 0.469292 0.862170-0.534227-0.809526 0.474422-0.419772
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - " 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06\n", - "A 0.205240 0.818113 -1.462109 0.719197 -1.161051 0.000782\n", - "B 0.527603 -0.894390 0.483201 -0.499809 -0.115774 0.146544\n", - "C 0.610052 -1.602831 -1.044973 1.145788 -0.624413 0.033628\n", - "D 0.469292 0.862170 -0.534227 -0.809526 0.474422 -0.419772" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.index" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# axis 0 is index, axis 1 is columns\n", - "df.sort_index(axis=1, ascending=False)" - ], - "language": "python", + "data": { + "text/plain": [ + "Index([u'A', u'B', u'C', u'D'], dtype='object')" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "html": [ - "
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DCBA
2013-01-01 0.469292 0.610052 0.527603 0.205240
2013-01-02 0.862170-1.602831-0.894390 0.818113
2013-01-03-0.534227-1.044973 0.483201-1.462109
2013-01-04-0.809526 1.145788-0.499809 0.719197
2013-01-05 0.474422-0.624413-0.115774-1.161051
2013-01-06-0.419772 0.033628 0.146544 0.000782
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - " D C B A\n", - "2013-01-01 0.469292 0.610052 0.527603 0.205240\n", - "2013-01-02 0.862170 -1.602831 -0.894390 0.818113\n", - "2013-01-03 -0.534227 -1.044973 0.483201 -1.462109\n", - "2013-01-04 -0.809526 1.145788 -0.499809 0.719197\n", - "2013-01-05 0.474422 -0.624413 -0.115774 -1.161051\n", - "2013-01-06 -0.419772 0.033628 0.146544 0.000782" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# can sort by values too\n", - "df.sort(columns='B')" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[ 2.05240362e-01, 5.27602841e-01, 6.10052272e-01,\n", + " 4.69292270e-01],\n", + " [ 8.18112883e-01, -8.94389618e-01, -1.60283098e+00,\n", + " 8.62169894e-01],\n", + " [ -1.46210940e+00, 4.83201108e-01, -1.04497297e+00,\n", + " -5.34226832e-01],\n", + " [ 7.19196807e-01, -4.99809344e-01, 1.14578824e+00,\n", + " -8.09525609e-01],\n", + " [ -1.16105080e+00, -1.15774007e-01, -6.24412514e-01,\n", + " 4.74421893e-01],\n", + " [ 7.82298420e-04, 1.46543576e-01, 3.36282758e-02,\n", + " -4.19771560e-01]])" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCD
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
2013-01-06 0.000782 0.146544 0.033628-0.419772
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-01 0.205240 0.527603 0.610052 0.469292
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - " A B C D\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", - "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422\n", - "2013-01-06 0.000782 0.146544 0.033628 -0.419772\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292" - ] - } - ], - "prompt_number": 13 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.values" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/html": [ + "
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ABCD
count 6.000000 6.000000 6.000000 6.000000
mean-0.146638-0.058771-0.247125 0.007060
std 0.957650 0.561381 1.036400 0.679012
min-1.462109-0.894390-1.602831-0.809526
25%-0.870593-0.403801-0.939833-0.505613
50% 0.103011 0.015385-0.295392 0.024760
75% 0.590708 0.399037 0.465946 0.473139
max 0.818113 0.527603 1.145788 0.862170
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "count 6.000000 6.000000 6.000000 6.000000\n", + "mean -0.146638 -0.058771 -0.247125 0.007060\n", + "std 0.957650 0.561381 1.036400 0.679012\n", + "min -1.462109 -0.894390 -1.602831 -0.809526\n", + "25% -0.870593 -0.403801 -0.939833 -0.505613\n", + "50% 0.103011 0.015385 -0.295392 0.024760\n", + "75% 0.590708 0.399037 0.465946 0.473139\n", + "max 0.818113 0.527603 1.145788 0.862170" + ] + }, + "execution_count": 10, "metadata": {}, - "source": [ - "Selection" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "# quick data summary\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# select a column (yields a series)\n", - "df['A']" - ], - "language": "python", + "data": { + "text/html": [ + "
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2013-01-01 00:00:002013-01-02 00:00:002013-01-03 00:00:002013-01-04 00:00:002013-01-05 00:00:002013-01-06 00:00:00
A 0.205240 0.818113-1.462109 0.719197-1.161051 0.000782
B 0.527603-0.894390 0.483201-0.499809-0.115774 0.146544
C 0.610052-1.602831-1.044973 1.145788-0.624413 0.033628
D 0.469292 0.862170-0.534227-0.809526 0.474422-0.419772
\n", + "
" + ], + "text/plain": [ + " 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06\n", + "A 0.205240 0.818113 -1.462109 0.719197 -1.161051 0.000782\n", + "B 0.527603 -0.894390 0.483201 -0.499809 -0.115774 0.146544\n", + "C 0.610052 -1.602831 -1.044973 1.145788 -0.624413 0.033628\n", + "D 0.469292 0.862170 -0.534227 -0.809526 0.474422 -0.419772" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "2013-01-01 0.205240\n", - "2013-01-02 0.818113\n", - "2013-01-03 -1.462109\n", - "2013-01-04 0.719197\n", - "2013-01-05 -1.161051\n", - "2013-01-06 0.000782\n", - "Freq: D, Name: A, dtype: float64" - ] - } - ], - "prompt_number": 14 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.T" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# column names also attached to the object\n", - "df.A" - ], - "language": "python", + "data": { + "text/html": [ + "
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DCBA
2013-01-01 0.469292 0.610052 0.527603 0.205240
2013-01-02 0.862170-1.602831-0.894390 0.818113
2013-01-03-0.534227-1.044973 0.483201-1.462109
2013-01-04-0.809526 1.145788-0.499809 0.719197
2013-01-05 0.474422-0.624413-0.115774-1.161051
2013-01-06-0.419772 0.033628 0.146544 0.000782
\n", + "
" + ], + "text/plain": [ + " D C B A\n", + "2013-01-01 0.469292 0.610052 0.527603 0.205240\n", + "2013-01-02 0.862170 -1.602831 -0.894390 0.818113\n", + "2013-01-03 -0.534227 -1.044973 0.483201 -1.462109\n", + "2013-01-04 -0.809526 1.145788 -0.499809 0.719197\n", + "2013-01-05 0.474422 -0.624413 -0.115774 -1.161051\n", + "2013-01-06 -0.419772 0.033628 0.146544 0.000782" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "2013-01-01 0.205240\n", - "2013-01-02 0.818113\n", - "2013-01-03 -1.462109\n", - "2013-01-04 0.719197\n", - "2013-01-05 -1.161051\n", - "2013-01-06 0.000782\n", - "Freq: D, Name: A, dtype: float64" - ] - } - ], - "prompt_number": 15 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# axis 0 is index, axis 1 is columns\n", + "df.sort_index(axis=1, ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# slicing works\n", - "df[0:3]" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-05-1.161051-0.115774-0.624413 0.474422
2013-01-06 0.000782 0.146544 0.033628-0.419772
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-01 0.205240 0.527603 0.610052 0.469292
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", + "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422\n", + "2013-01-06 0.000782 0.146544 0.033628 -0.419772\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - " A B C D\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# can sort by values too\n", + "df.sort(columns='B')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df['20130102':'20130104']" - ], - "language": "python", + "data": { + "text/plain": [ + "2013-01-01 0.205240\n", + "2013-01-02 0.818113\n", + "2013-01-03 -1.462109\n", + "2013-01-04 0.719197\n", + "2013-01-05 -1.161051\n", + "2013-01-06 0.000782\n", + "Freq: D, Name: A, dtype: float64" + ] + }, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - " A B C D\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", - "2013-01-04 0.719197 -0.499809 1.145788 -0.809526" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# select a column (yields a series)\n", + "df['A']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# cross-section using a label\n", - "df.loc[dates[0]]" - ], - "language": "python", + "data": { + "text/plain": [ + "2013-01-01 0.205240\n", + "2013-01-02 0.818113\n", + "2013-01-03 -1.462109\n", + "2013-01-04 0.719197\n", + "2013-01-05 -1.161051\n", + "2013-01-06 0.000782\n", + "Freq: D, Name: A, dtype: float64" + ] + }, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "A 0.205240\n", - "B 0.527603\n", - "C 0.610052\n", - "D 0.469292\n", - "Name: 2013-01-01 00:00:00, dtype: float64" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# column names also attached to the object\n", + "df.A" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# getting a scalar value\n", - "df.loc[dates[0], 'A']" - ], - "language": "python", + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227" + ] + }, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "0.20524036189008577" - ] - } - ], - "prompt_number": 19 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# slicing works\n", + "df[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# select via position\n", - "df.iloc[3]" - ], - "language": "python", + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ABCD
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-03-1.462109 0.483201-1.044973-0.534227
2013-01-04 0.719197-0.499809 1.145788-0.809526
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227\n", + "2013-01-04 0.719197 -0.499809 1.145788 -0.809526" + ] + }, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "A 0.719197\n", - "B -0.499809\n", - "C 1.145788\n", - "D -0.809526\n", - "Name: 2013-01-04 00:00:00, dtype: float64" - ] - } - ], - "prompt_number": 20 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df['20130102':'20130104']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df.iloc[3:5,0:2]" - ], - "language": "python", + "data": { + "text/plain": [ + "A 0.205240\n", + "B 0.527603\n", + "C 0.610052\n", + "D 0.469292\n", + "Name: 2013-01-01 00:00:00, dtype: float64" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
AB
2013-01-04 0.719197-0.499809
2013-01-05-1.161051-0.115774
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - " A B\n", - "2013-01-04 0.719197 -0.499809\n", - "2013-01-05 -1.161051 -0.115774" - ] - } - ], - "prompt_number": 21 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# cross-section using a label\n", + "df.loc[dates[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# column slicing\n", - "df.iloc[:,1:3]" - ], - "language": "python", + "data": { + "text/plain": [ + "0.20524036189008577" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BC
2013-01-01 0.527603 0.610052
2013-01-02-0.894390-1.602831
2013-01-03 0.483201-1.044973
2013-01-04-0.499809 1.145788
2013-01-05-0.115774-0.624413
2013-01-06 0.146544 0.033628
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - " B C\n", - "2013-01-01 0.527603 0.610052\n", - "2013-01-02 -0.894390 -1.602831\n", - "2013-01-03 0.483201 -1.044973\n", - "2013-01-04 -0.499809 1.145788\n", - "2013-01-05 -0.115774 -0.624413\n", - "2013-01-06 0.146544 0.033628" - ] - } - ], - "prompt_number": 22 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# getting a scalar value\n", + "df.loc[dates[0], 'A']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# get a value by index\n", - "df.iloc[1,1]" - ], - "language": "python", + "data": { + "text/plain": [ + "A 0.719197\n", + "B -0.499809\n", + "C 1.145788\n", + "D -0.809526\n", + "Name: 2013-01-04 00:00:00, dtype: float64" + ] + }, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, - "text": [ - "-0.89438961765370562" - ] - } - ], - "prompt_number": 23 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# select via position\n", + "df.iloc[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# boolean indexing\n", - "df[df.A > 0]" - ], - "language": "python", + "data": { + "text/html": [ + "
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AB
2013-01-04 0.719197-0.499809
2013-01-05-1.161051-0.115774
\n", + "
" + ], + "text/plain": [ + " A B\n", + "2013-01-04 0.719197 -0.499809\n", + "2013-01-05 -1.161051 -0.115774" + ] + }, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-06 0.000782 0.146544 0.033628-0.419772
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - " A B C D\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", - "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", - "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", - "2013-01-06 0.000782 0.146544 0.033628 -0.419772" - ] - } - ], - "prompt_number": 24 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[3:5,0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df[df > 0]" - ], - "language": "python", + "data": { + "text/html": [ + "
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BC
2013-01-01 0.527603 0.610052
2013-01-02-0.894390-1.602831
2013-01-03 0.483201-1.044973
2013-01-04-0.499809 1.145788
2013-01-05-0.115774-0.624413
2013-01-06 0.146544 0.033628
\n", + "
" + ], + "text/plain": [ + " B C\n", + "2013-01-01 0.527603 0.610052\n", + "2013-01-02 -0.894390 -1.602831\n", + "2013-01-03 0.483201 -1.044973\n", + "2013-01-04 -0.499809 1.145788\n", + "2013-01-05 -0.115774 -0.624413\n", + "2013-01-06 0.146544 0.033628" + ] + }, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113 NaN NaN 0.862170
2013-01-03 NaN 0.483201 NaN NaN
2013-01-04 0.719197 NaN 1.145788 NaN
2013-01-05 NaN NaN NaN 0.474422
2013-01-06 0.000782 0.146544 0.033628 NaN
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - " A B C D\n", - "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", - "2013-01-02 0.818113 NaN NaN 0.862170\n", - "2013-01-03 NaN 0.483201 NaN NaN\n", - "2013-01-04 0.719197 NaN 1.145788 NaN\n", - "2013-01-05 NaN NaN NaN 0.474422\n", - "2013-01-06 0.000782 0.146544 0.033628 NaN" - ] - } - ], - "prompt_number": 25 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# column slicing\n", + "df.iloc[:,1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# filtering\n", - "df3 = df.copy()\n", - "df3['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n", - "df3[df3['E'].isin(['two', 'four'])]" - ], - "language": "python", + "data": { + "text/plain": [ + "-0.89438961765370562" + ] + }, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCDE
2013-01-03-1.462109 0.483201-1.044973-0.534227 two
2013-01-05-1.161051-0.115774-0.624413 0.474422 four
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - " A B C D E\n", - "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227 two\n", - "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422 four" - ] - } - ], - "prompt_number": 26 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# get a value by index\n", + "df.iloc[1,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# setting examples\n", - "df.at[dates[0],'A'] = 0\n", - "df.iat[0,1] = 0\n", - "df.loc[:, 'D'] = np.array([5] * len(df))\n", - "df" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113-0.894390-1.602831 0.862170
2013-01-04 0.719197-0.499809 1.145788-0.809526
2013-01-06 0.000782 0.146544 0.033628-0.419772
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", + "2013-01-02 0.818113 -0.894390 -1.602831 0.862170\n", + "2013-01-04 0.719197 -0.499809 1.145788 -0.809526\n", + "2013-01-06 0.000782 0.146544 0.033628 -0.419772" + ] + }, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCD
2013-01-01 0.000000 0.000000 0.610052 5
2013-01-02 0.818113-0.894390-1.602831 5
2013-01-03-1.462109 0.483201-1.044973 5
2013-01-04 0.719197-0.499809 1.145788 5
2013-01-05-1.161051-0.115774-0.624413 5
2013-01-06 0.000782 0.146544 0.033628 5
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 27, - "text": [ - " A B C D\n", - "2013-01-01 0.000000 0.000000 0.610052 5\n", - "2013-01-02 0.818113 -0.894390 -1.602831 5\n", - "2013-01-03 -1.462109 0.483201 -1.044973 5\n", - "2013-01-04 0.719197 -0.499809 1.145788 5\n", - "2013-01-05 -1.161051 -0.115774 -0.624413 5\n", - "2013-01-06 0.000782 0.146544 0.033628 5" - ] - } - ], - "prompt_number": 27 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# boolean indexing\n", + "df[df.A > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# dealing with missing data\n", - "df4 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])\n", - "df4.loc[dates[0]:dates[1],'E'] = 1\n", - "df4" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2013-01-01 0.205240 0.527603 0.610052 0.469292
2013-01-02 0.818113 NaN NaN 0.862170
2013-01-03 NaN 0.483201 NaN NaN
2013-01-04 0.719197 NaN 1.145788 NaN
2013-01-05 NaN NaN NaN 0.474422
2013-01-06 0.000782 0.146544 0.033628 NaN
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.205240 0.527603 0.610052 0.469292\n", + "2013-01-02 0.818113 NaN NaN 0.862170\n", + "2013-01-03 NaN 0.483201 NaN NaN\n", + "2013-01-04 0.719197 NaN 1.145788 NaN\n", + "2013-01-05 NaN NaN NaN 0.474422\n", + "2013-01-06 0.000782 0.146544 0.033628 NaN" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCDE
2013-01-01 0.000000 0.000000 0.610052 5 1
2013-01-02 0.818113-0.894390-1.602831 5 1
2013-01-03-1.462109 0.483201-1.044973 5NaN
2013-01-04 0.719197-0.499809 1.145788 5NaN
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - " A B C D E\n", - "2013-01-01 0.000000 0.000000 0.610052 5 1\n", - "2013-01-02 0.818113 -0.894390 -1.602831 5 1\n", - "2013-01-03 -1.462109 0.483201 -1.044973 5 NaN\n", - "2013-01-04 0.719197 -0.499809 1.145788 5 NaN" - ] - } - ], - "prompt_number": 28 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df[df > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# drop rows with missing data\n", - "df4.dropna(how='any')" - ], - "language": "python", + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ABCDE
2013-01-03-1.462109 0.483201-1.044973-0.534227 two
2013-01-05-1.161051-0.115774-0.624413 0.474422 four
\n", + "
" + ], + "text/plain": [ + " A B C D E\n", + "2013-01-03 -1.462109 0.483201 -1.044973 -0.534227 two\n", + "2013-01-05 -1.161051 -0.115774 -0.624413 0.474422 four" + ] + }, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCDE
2013-01-01 0.000000 0.00000 0.610052 5 1
2013-01-02 0.818113-0.89439-1.602831 5 1
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - " A B C D E\n", - "2013-01-01 0.000000 0.00000 0.610052 5 1\n", - "2013-01-02 0.818113 -0.89439 -1.602831 5 1" - ] - } - ], - "prompt_number": 29 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# filtering\n", + "df3 = df.copy()\n", + "df3['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n", + "df3[df3['E'].isin(['two', 'four'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# fill missing data\n", - "df4.fillna(value=5)" - ], - "language": "python", + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ABCD
2013-01-01 0.000000 0.000000 0.610052 5
2013-01-02 0.818113-0.894390-1.602831 5
2013-01-03-1.462109 0.483201-1.044973 5
2013-01-04 0.719197-0.499809 1.145788 5
2013-01-05-1.161051-0.115774-0.624413 5
2013-01-06 0.000782 0.146544 0.033628 5
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "2013-01-01 0.000000 0.000000 0.610052 5\n", + "2013-01-02 0.818113 -0.894390 -1.602831 5\n", + "2013-01-03 -1.462109 0.483201 -1.044973 5\n", + "2013-01-04 0.719197 -0.499809 1.145788 5\n", + "2013-01-05 -1.161051 -0.115774 -0.624413 5\n", + "2013-01-06 0.000782 0.146544 0.033628 5" + ] + }, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCDE
2013-01-01 0.000000 0.000000 0.610052 5 1
2013-01-02 0.818113-0.894390-1.602831 5 1
2013-01-03-1.462109 0.483201-1.044973 5 5
2013-01-04 0.719197-0.499809 1.145788 5 5
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - " A B C D E\n", - "2013-01-01 0.000000 0.000000 0.610052 5 1\n", - "2013-01-02 0.818113 -0.894390 -1.602831 5 1\n", - "2013-01-03 -1.462109 0.483201 -1.044973 5 5\n", - "2013-01-04 0.719197 -0.499809 1.145788 5 5" - ] - } - ], - "prompt_number": 30 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# setting examples\n", + "df.at[dates[0],'A'] = 0\n", + "df.iat[0,1] = 0\n", + "df.loc[:, 'D'] = np.array([5] * len(df))\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# boolean mask for nan values\n", - "pd.isnull(df4)" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCDE
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ABCDE
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ABCD
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ABCD
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ABCD
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ABCD
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2-0.159224-1.226735-0.141689-1.450920
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6 0.380308-0.397791-0.322955 0.074480
7-0.623134-0.205967-0.367622 1.437279
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9-0.345859-0.759047-0.927940 1.487916
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - " 0 1 2 3\n", - "0 -0.006589 -1.232048 -0.147323 0.709050\n", - "1 -1.201048 0.675688 1.110037 0.553489\n", - "2 -0.159224 -1.226735 -0.141689 -1.450920\n", - "3 -0.049450 -0.438565 0.670832 1.089032\n", - "4 -0.105969 -0.891644 0.626482 0.416679\n", - "5 -1.103222 -1.983806 0.282366 0.031730\n", - "6 0.380308 -0.397791 -0.322955 0.074480\n", - "7 -0.623134 -0.205967 -0.367622 1.437279\n", - "8 -0.481202 1.242607 -2.107715 1.020051\n", - "9 -0.345859 -0.759047 -0.927940 1.487916" - ] - } - ], - "prompt_number": 38 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# simple count aggregation\n", + "s = pd.Series(np.random.randint(0,7,size=10))\n", + "s.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merging / Grouping / Shaping" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# SQL-style join\n", - "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n", - "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n", - "pd.merge(left, right, on='key')" - ], - "language": "python", + "data": { + "text/html": [ + "
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0123
0-0.006589-1.232048-0.147323 0.709050
1-1.201048 0.675688 1.110037 0.553489
2-0.159224-1.226735-0.141689-1.450920
3-0.049450-0.438565 0.670832 1.089032
4-0.105969-0.891644 0.626482 0.416679
5-1.103222-1.983806 0.282366 0.031730
6 0.380308-0.397791-0.322955 0.074480
7-0.623134-0.205967-0.367622 1.437279
8-0.481202 1.242607-2.107715 1.020051
9-0.345859-0.759047-0.927940 1.487916
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keylvalrval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 39, - "text": [ - " key lval rval\n", - "0 foo 1 4\n", - "1 foo 1 5\n", - "2 foo 2 4\n", - "3 foo 2 5" - ] - } - ], - "prompt_number": 39 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# concatenation\n", + "df = pd.DataFrame(np.random.randn(10, 4))\n", + "pieces = [df[:3], df[3:7], df[7:]]\n", + "pd.concat(pieces)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# append\n", - "df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\n", - "s = df.iloc[3]\n", - "df.append(s, ignore_index=True)" - ], - "language": "python", + "data": { + "text/html": [ + "
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keylvalrval
0 foo 1 4
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2 foo 2 4
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ABCD
0-0.992219 1.298979 0.998799-0.164381
1 0.902147 1.118289-0.169358 0.117833
2 1.201061-1.699020-2.112810-1.412482
3 1.084910 1.171135 0.384876 0.535239
4-0.922543-0.018670-1.506012 0.293739
5 0.481017 0.639182-0.090676 0.951261
6 1.201241 2.528836-0.530795 0.901950
7 0.899290 0.562738 1.566468-0.846827
8 1.084910 1.171135 0.384876 0.535239
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 40, - "text": [ - " A B C D\n", - "0 -0.992219 1.298979 0.998799 -0.164381\n", - "1 0.902147 1.118289 -0.169358 0.117833\n", - "2 1.201061 -1.699020 -2.112810 -1.412482\n", - "3 1.084910 1.171135 0.384876 0.535239\n", - "4 -0.922543 -0.018670 -1.506012 0.293739\n", - "5 0.481017 0.639182 -0.090676 0.951261\n", - "6 1.201241 2.528836 -0.530795 0.901950\n", - "7 0.899290 0.562738 1.566468 -0.846827\n", - "8 1.084910 1.171135 0.384876 0.535239" - ] - } - ], - "prompt_number": 40 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# SQL-style join\n", + "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n", + "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n", + "pd.merge(left, right, on='key')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df = pd.DataFrame(\n", - " { 'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n", - " 'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],\n", - " 'C' : np.random.randn(8),\n", - " 'D' : np.random.randn(8) })\n", - "df" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
0-0.992219 1.298979 0.998799-0.164381
1 0.902147 1.118289-0.169358 0.117833
2 1.201061-1.699020-2.112810-1.412482
3 1.084910 1.171135 0.384876 0.535239
4-0.922543-0.018670-1.506012 0.293739
5 0.481017 0.639182-0.090676 0.951261
6 1.201241 2.528836-0.530795 0.901950
7 0.899290 0.562738 1.566468-0.846827
8 1.084910 1.171135 0.384876 0.535239
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "0 -0.992219 1.298979 0.998799 -0.164381\n", + "1 0.902147 1.118289 -0.169358 0.117833\n", + "2 1.201061 -1.699020 -2.112810 -1.412482\n", + "3 1.084910 1.171135 0.384876 0.535239\n", + "4 -0.922543 -0.018670 -1.506012 0.293739\n", + "5 0.481017 0.639182 -0.090676 0.951261\n", + "6 1.201241 2.528836 -0.530795 0.901950\n", + "7 0.899290 0.562738 1.566468 -0.846827\n", + "8 1.084910 1.171135 0.384876 0.535239" + ] + }, + "execution_count": 40, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCD
0 foo one 0.193948-1.385614
1 bar one-0.257859 2.127808
2 foo two-0.944848-0.760487
3 bar three-0.872161-1.707254
4 foo two-0.658552 0.175699
5 bar two-1.887614 0.627801
6 foo one 0.439001-2.264125
7 foo three-0.829368-1.229315
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 41, - "text": [ - " A B C D\n", - "0 foo one 0.193948 -1.385614\n", - "1 bar one -0.257859 2.127808\n", - "2 foo two -0.944848 -0.760487\n", - "3 bar three -0.872161 -1.707254\n", - "4 foo two -0.658552 0.175699\n", - "5 bar two -1.887614 0.627801\n", - "6 foo one 0.439001 -2.264125\n", - "7 foo three -0.829368 -1.229315" - ] - } - ], - "prompt_number": 41 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# append\n", + "df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\n", + "s = df.iloc[3]\n", + "df.append(s, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# group by\n", - "df.groupby('A').sum()" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
0 foo one 0.193948-1.385614
1 bar one-0.257859 2.127808
2 foo two-0.944848-0.760487
3 bar three-0.872161-1.707254
4 foo two-0.658552 0.175699
5 bar two-1.887614 0.627801
6 foo one 0.439001-2.264125
7 foo three-0.829368-1.229315
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "0 foo one 0.193948 -1.385614\n", + "1 bar one -0.257859 2.127808\n", + "2 foo two -0.944848 -0.760487\n", + "3 bar three -0.872161 -1.707254\n", + "4 foo two -0.658552 0.175699\n", + "5 bar two -1.887614 0.627801\n", + "6 foo one 0.439001 -2.264125\n", + "7 foo three -0.829368 -1.229315" + ] + }, + "execution_count": 41, "metadata": {}, - "outputs": [ - { - "html": [ - "
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CD
A
bar-3.017634 1.048355
foo-1.799818-5.463842
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 42, - "text": [ - " C D\n", - "A \n", - "bar -3.017634 1.048355\n", - "foo -1.799818 -5.463842" - ] - } - ], - "prompt_number": 42 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " { 'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n", + " 'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],\n", + " 'C' : np.random.randn(8),\n", + " 'D' : np.random.randn(8) })\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# group by multiple columns\n", - "df.groupby(['A','B']).sum()" - ], - "language": "python", + "data": { + "text/html": [ + "
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CD
A
bar-3.017634 1.048355
foo-1.799818-5.463842
\n", + "
" + ], + "text/plain": [ + " C D\n", + "A \n", + "bar -3.017634 1.048355\n", + "foo -1.799818 -5.463842" + ] + }, + "execution_count": 42, "metadata": {}, - "outputs": [ - { - "html": [ - "
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CD
AB
barone-0.257859 2.127808
three-0.872161-1.707254
two-1.887614 0.627801
fooone 0.632949-3.649739
three-0.829368-1.229315
two-1.603400-0.584788
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 43, - "text": [ - " C D\n", - "A B \n", - "bar one -0.257859 2.127808\n", - " three -0.872161 -1.707254\n", - " two -1.887614 0.627801\n", - "foo one 0.632949 -3.649739\n", - " three -0.829368 -1.229315\n", - " two -1.603400 -0.584788" - ] - } - ], - "prompt_number": 43 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# group by\n", + "df.groupby('A').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "df = pd.DataFrame(\n", - " { 'A' : ['one', 'one', 'two', 'three'] * 3,\n", - " 'B' : ['A', 'B', 'C'] * 4,\n", - " 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n", - " 'D' : np.random.randn(12),\n", - " 'E' : np.random.randn(12)} )\n", - "df" - ], - "language": "python", + "data": { + "text/html": [ + "
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CD
AB
barone-0.257859 2.127808
three-0.872161-1.707254
two-1.887614 0.627801
fooone 0.632949-3.649739
three-0.829368-1.229315
two-1.603400-0.584788
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" + ], + "text/plain": [ + " C D\n", + "A B \n", + "bar one -0.257859 2.127808\n", + " three -0.872161 -1.707254\n", + " two -1.887614 0.627801\n", + "foo one 0.632949 -3.649739\n", + " three -0.829368 -1.229315\n", + " two -1.603400 -0.584788" + ] + }, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "html": [ - "
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ABCDE
0 one A foo-0.853288 2.549878
1 one B foo 0.552557 0.865465
2 two C foo 0.700943 0.800563
3 three A bar-0.466072 0.011508
4 one B bar 0.465724 1.087874
5 one C bar 1.105949-0.118134
6 two A foo-0.666630-0.143474
7 three B foo 0.644902 1.731818
8 one C foo 0.819170-1.153036
9 one A bar-1.849893 0.733137
10 two B bar 0.684170-0.276237
11 three C bar 0.592939-0.830433
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 44, - "text": [ - " A B C D E\n", - "0 one A foo -0.853288 2.549878\n", - "1 one B foo 0.552557 0.865465\n", - "2 two C foo 0.700943 0.800563\n", - "3 three A bar -0.466072 0.011508\n", - "4 one B bar 0.465724 1.087874\n", - "5 one C bar 1.105949 -0.118134\n", - "6 two A foo -0.666630 -0.143474\n", - "7 three B foo 0.644902 1.731818\n", - "8 one C foo 0.819170 -1.153036\n", - "9 one A bar -1.849893 0.733137\n", - "10 two B bar 0.684170 -0.276237\n", - "11 three C bar 0.592939 -0.830433" - ] - } - ], - "prompt_number": 44 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# group by multiple columns\n", + "df.groupby(['A','B']).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# pivot table\n", - "pd.pivot_table(df, values='D', rows=['A', 'B'], columns=['C'])" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCDE
0 one A foo-0.853288 2.549878
1 one B foo 0.552557 0.865465
2 two C foo 0.700943 0.800563
3 three A bar-0.466072 0.011508
4 one B bar 0.465724 1.087874
5 one C bar 1.105949-0.118134
6 two A foo-0.666630-0.143474
7 three B foo 0.644902 1.731818
8 one C foo 0.819170-1.153036
9 one A bar-1.849893 0.733137
10 two B bar 0.684170-0.276237
11 three C bar 0.592939-0.830433
\n", + "
" + ], + "text/plain": [ + " A B C D E\n", + "0 one A foo -0.853288 2.549878\n", + "1 one B foo 0.552557 0.865465\n", + "2 two C foo 0.700943 0.800563\n", + "3 three A bar -0.466072 0.011508\n", + "4 one B bar 0.465724 1.087874\n", + "5 one C bar 1.105949 -0.118134\n", + "6 two A foo -0.666630 -0.143474\n", + "7 three B foo 0.644902 1.731818\n", + "8 one C foo 0.819170 -1.153036\n", + "9 one A bar -1.849893 0.733137\n", + "10 two B bar 0.684170 -0.276237\n", + "11 three C bar 0.592939 -0.830433" + ] + }, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "C:\\Program Files\\Anaconda\\lib\\site-packages\\pandas\\util\\decorators.py:53: FutureWarning: rows is deprecated, use index instead\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "html": [ - "
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Cbarfoo
AB
oneA-1.849893-0.853288
B 0.465724 0.552557
C 1.105949 0.819170
threeA-0.466072 NaN
B NaN 0.644902
C 0.592939 NaN
twoA NaN-0.666630
B 0.684170 NaN
C NaN 0.700943
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 45, - "text": [ - "C bar foo\n", - "A B \n", - "one A -1.849893 -0.853288\n", - " B 0.465724 0.552557\n", - " C 1.105949 0.819170\n", - "three A -0.466072 NaN\n", - " B NaN 0.644902\n", - " C 0.592939 NaN\n", - "two A NaN -0.666630\n", - " B 0.684170 NaN\n", - " C NaN 0.700943" - ] - } - ], - "prompt_number": 45 - }, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " { 'A' : ['one', 'one', 'two', 'three'] * 3,\n", + " 'B' : ['A', 'B', 'C'] * 4,\n", + " 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n", + " 'D' : np.random.randn(12),\n", + " 'E' : np.random.randn(12)} )\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Time Series" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Program Files\\Anaconda\\lib\\site-packages\\pandas\\util\\decorators.py:53: FutureWarning: rows is deprecated, use index instead\n", + " warnings.warn(msg, FutureWarning)\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# time period resampling\n", - "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", - "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n", - "ts.resample('5Min', how='sum')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 46, - "text": [ - "2012-01-01 24406\n", - "Freq: 5T, dtype: int32" - ] - } - ], - "prompt_number": 46 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n", - "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n", - "ts" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 47, - "text": [ - "2012-01-31 -0.624893\n", - "2012-02-29 -0.176292\n", - "2012-03-31 1.673556\n", - "2012-04-30 0.707903\n", - "2012-05-31 0.533647\n", - "Freq: M, dtype: float64" - ] - } - ], - "prompt_number": 47 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ps = ts.to_period()\n", - "ps.to_timestamp()" - ], - "language": "python", + "data": { + "text/html": [ + "
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Cbarfoo
AB
oneA-1.849893-0.853288
B 0.465724 0.552557
C 1.105949 0.819170
threeA-0.466072 NaN
B NaN 0.644902
C 0.592939 NaN
twoA NaN-0.666630
B 0.684170 NaN
C NaN 0.700943
\n", + "
" + ], + "text/plain": [ + "C bar foo\n", + "A B \n", + "one A -1.849893 -0.853288\n", + " B 0.465724 0.552557\n", + " C 1.105949 0.819170\n", + "three A -0.466072 NaN\n", + " B NaN 0.644902\n", + " C 0.592939 NaN\n", + "two A NaN -0.666630\n", + " B 0.684170 NaN\n", + " C NaN 0.700943" + ] + }, + "execution_count": 45, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 48, - "text": [ - "2012-01-01 -0.624893\n", - "2012-02-01 -0.176292\n", - "2012-03-01 1.673556\n", - "2012-04-01 0.707903\n", - "2012-05-01 0.533647\n", - "Freq: MS, dtype: float64" - ] - } - ], - "prompt_number": 48 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# pivot table\n", + "pd.pivot_table(df, values='D', rows=['A', 'B'], columns=['C'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Series" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/plain": [ + "2012-01-01 24406\n", + "Freq: 5T, dtype: int32" + ] + }, + "execution_count": 46, "metadata": {}, - "source": [ - "Plotting" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "# time period resampling\n", + "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", + "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n", + "ts.resample('5Min', how='sum')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# time series plot\n", - "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", - "ts = ts.cumsum()\n", - "ts.plot()" - ], - "language": "python", + "data": { + "text/plain": [ + "2012-01-31 -0.624893\n", + "2012-02-29 -0.176292\n", + "2012-03-31 1.673556\n", + "2012-04-30 0.707903\n", + "2012-05-31 0.533647\n", + "Freq: M, dtype: float64" + ] + }, + "execution_count": 47, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 49, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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HD7P7mBUqK0Pd73r1YtNPqgp0gM0n5gApjRbo27aFbtcJpJYsYc8e88SeNt1s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\nCAAff8ztVBGWqdJPbRcU0hs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- "text": [ - "" - ] - } - ], - "prompt_number": 49 - }, + "output_type": "execute_result" + } + ], + "source": [ + "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n", + "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# plot with a data frame\n", - "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])\n", - "df = df.cumsum()\n", - "plt.figure(); df.plot(); plt.legend(loc='best')" - ], - "language": "python", + "data": { + "text/plain": [ + "2012-01-01 -0.624893\n", + "2012-02-01 -0.176292\n", + "2012-03-01 1.673556\n", + "2012-04-01 0.707903\n", + "2012-05-01 0.533647\n", + "Freq: MS, dtype: float64" + ] + }, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 50, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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ETkNyMnvnAwM56qS5mZZjxuiGHUp/BMmRtnev7CQyxNy5rO0D7AgrLuYKioCu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- "text": [ - "" - ] - } - ], - "prompt_number": 50 - }, + "output_type": "execute_result" + } + ], + "source": [ + "ps = ts.to_period()\n", + "ps.to_timestamp()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, "metadata": {}, - "source": [ - "Input / Output" - ] + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# write to a csv file\n", - "df.to_csv('foo.csv', index=False)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEMCAYAAADUEk3/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXl4FGW2/7+HXQmQhX2NIOu4BBBEQQngOl5xv+O4YMar\n", + 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= ts.cumsum()\n", + "ts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# read file back in\n", - "path = r'C:\\Users\\John\\Documents\\IPython Notebooks\\foo.csv'\n", - "newDf = pd.read_csv(path)\n", - "newDf.head()" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, "metadata": {}, - "outputs": [ - { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ABCD
0-0.914956 0.294759 0.143332 0.174706
1-0.297442 1.640208 0.425301-0.075666
2-0.762292 0.741179 0.505002-0.128560
3-1.577471-0.495294 1.803332 0.188178
4-0.137486-0.676985 1.435308 0.181047
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 52, - "text": [ - " A B C D\n", - "0 -0.914956 0.294759 0.143332 0.174706\n", - "1 -0.297442 1.640208 0.425301 -0.075666\n", - "2 -0.762292 0.741179 0.505002 -0.128560\n", - "3 -1.577471 -0.495294 1.803332 0.188178\n", - "4 -0.137486 -0.676985 1.435308 0.181047" - ] - } - ], - "prompt_number": 52 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# remove the file\n", - "import os\n", - "os.remove(path)" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [], - "prompt_number": 53 + "output_type": "display_data" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# can also do Excel\n", - "df.to_excel('foo.xlsx', sheet_name='Sheet1')" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEMCAYAAADUEk3/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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"metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# write to a csv file\n", + "df.to_csv('foo.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "newDf2 = pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])\n", - "newDf2.head()" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
0-0.914956 0.294759 0.143332 0.174706
1-0.297442 1.640208 0.425301-0.075666
2-0.762292 0.741179 0.505002-0.128560
3-1.577471-0.495294 1.803332 0.188178
4-0.137486-0.676985 1.435308 0.181047
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 55, - "text": [ - " A B C D\n", - "2000-01-01 -0.914956 0.294759 0.143332 0.174706\n", - "2000-01-02 -0.297442 1.640208 0.425301 -0.075666\n", - "2000-01-03 -0.762292 0.741179 0.505002 -0.128560\n", - "2000-01-04 -1.577471 -0.495294 1.803332 0.188178\n", - "2000-01-05 -0.137486 -0.676985 1.435308 0.181047" - ] - } - ], - "prompt_number": 55 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# read file back in\n", + "path = r'C:\\Users\\John\\Documents\\IPython Notebooks\\foo.csv'\n", + "newDf = pd.read_csv(path)\n", + "newDf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# remove the file\n", + "import os\n", + "os.remove(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# can also do Excel\n", + "df.to_excel('foo.xlsx', sheet_name='Sheet1')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "os.remove('foo.xlsx')" - ], - "language": "python", + "data": { + "text/html": [ + "
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ABCD
2000-01-01-0.914956 0.294759 0.143332 0.174706
2000-01-02-0.297442 1.640208 0.425301-0.075666
2000-01-03-0.762292 0.741179 0.505002-0.128560
2000-01-04-1.577471-0.495294 1.803332 0.188178
2000-01-05-0.137486-0.676985 1.435308 0.181047
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" + ], + "text/plain": [ + " A B C D\n", + "2000-01-01 -0.914956 0.294759 0.143332 0.174706\n", + "2000-01-02 -0.297442 1.640208 0.425301 -0.075666\n", + "2000-01-03 -0.762292 0.741179 0.505002 -0.128560\n", + "2000-01-04 -1.577471 -0.495294 1.803332 0.188178\n", + "2000-01-05 -0.137486 -0.676985 1.435308 0.181047" + ] + }, + "execution_count": 55, "metadata": {}, - "outputs": [], - "prompt_number": 56 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "newDf2 = pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])\n", + "newDf2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "os.remove('foo.xlsx')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/PyMC.ipynb b/PyMC.ipynb index 8aef635..fdfb994 100644 --- a/PyMC.ipynb +++ b/PyMC.ipynb @@ -1,386 +1,1703 @@ { - "metadata": { - "name": "", - "signature": "sha256:b0b5c4915d6059f592b8d73112601dcde3d9b6ddfbdaceb400de0ad4f63cfc91" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "PyMC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics." - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PyMC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# objective is to model the number of coal mining disasters per year in the UK from 1851 to 1962 \n", + "# start with some imports\n", + "%matplotlib inline\n", + "import numpy as np\n", + "from pymc import *\n", + "from pymc.Matplot import plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# number of mining disasters per year\n", + "disasters_array = np.array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n", + " 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n", + " 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,\n", + " 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n", + " 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n", + " 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n", + " 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# objective is to model the number of coal mining disasters per year in the UK from 1851 to 1962 \n", - "# start with some imports\n", - "%matplotlib inline\n", - "import numpy as np\n", - "from pymc import *\n", - "from pymc.Matplot import plot" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# variable representing the presumed \"switch point\" at which disasters became less frequent\n", + "switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')\n", + "switchpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# number of mining disasters per year\n", - "disasters_array = np.array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n", - " 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n", - " 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,\n", - " 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n", - " 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n", - " 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n", - " 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])" - ], - "language": "python", + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# variables for the pre and post-switchpoint mean\n", + "early_mean = Exponential('early_mean', beta=1.)\n", + "late_mean = Exponential('late_mean', beta=1.)\n", + "early_mean, late_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# deterministic rate parameter of the distribution of disasters\n", + "@deterministic(plot=False)\n", + "def rate(s=switchpoint, e=early_mean, l=late_mean):\n", + " ''' Concatenate Poisson means '''\n", + " out = np.empty(len(disasters_array))\n", + " out[:s] = e\n", + " out[s:] = l\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# variable representing the presumed \"switch point\" at which disasters became less frequent\n", - "switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')\n", - "switchpoint" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# actual distribution of disasters (observed)\n", + "disasters = Poisson('disasters', mu=rate, value=disasters_array, observed=True)\n", + "disasters" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# variables for the pre and post-switchpoint mean\n", - "early_mean = Exponential('early_mean', beta=1.)\n", - "late_mean = Exponential('late_mean', beta=1.)\n", - "early_mean, late_mean" - ], - "language": "python", + "data": { + "text/plain": [ + "({'lower': 0, 'upper': 110},\n", + " {'mu': },\n", + " {})" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "(,\n", - " )" - ] - } - ], - "prompt_number": 4 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# take a look at the parents/children for these variables to see relationships\n", + "switchpoint.parents, disasters.parents, rate.children" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create the model\n", + "model = Model([switchpoint, early_mean, late_mean, disasters])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# deterministic rate parameter of the distribution of disasters\n", - "@deterministic(plot=False)\n", - "def rate(s=switchpoint, e=early_mean, l=late_mean):\n", - " ''' Concatenate Poisson means '''\n", - " out = np.empty(len(disasters_array))\n", - " out[:s] = e\n", - " out[s:] = l\n", - " return out" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " [-----------------100%-----------------] 10000 of 10000 complete in 3.7 sec" + ] + } + ], + "source": [ + "# Markov Chain Monte-Carlo sampling\n", + "M = MCMC(model)\n", + "M.sample(iter=10000, burn=1000, thin=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# actual distribution of disasters (observed)\n", - "disasters = Poisson('disasters', mu=rate, value=disasters_array, observed=True)\n", - "disasters" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[91 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46\n", + " 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46\n", + " 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46\n", + " 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46\n", + " 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 37 37 37 37 37 37 37 37 37 37\n", + " 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37\n", + " 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37\n", + " 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37\n", + " 37 37 37 37 48 40 40 40 40 40 40 40 40 40 40 40 40 45 45 46 43 43 43 37 43\n", + " 43 43 37 41 41 41 41 41 41 41 41 41 41 41 41 36 37 41 41 41 41 41 41 41 41\n", + " 41 40 40 40 40 40 40 40 43 38 38 36 38 38 38 39 39 46 38 43 43 39 41 39 39\n", + " 39 39 39 39 39 39 39 37 38 38 36 36 39 39 39 39 39 40 40 37 37 44 39 39 44\n", + " 44 37 37 37 38 37 37 37 37 46 40 40 40 36 46 39 39 39 45 43 46 47 39 39 46\n", + " 36 40 39 44 39 39 39 39 39 39 41 41 46 42 43 43 44 39 39 39 39 39 39 41 40\n", + " 40 40 40 40 40 40 40 46 46 41 41 41 41 41 39 41 41 41 41 41 41 36 36 41 41\n", + " 41 41 41 41 41 41 41 41 36 46 37 37 41 41 41 41 41 41 41 39 39 39 39 44 39\n", + " 39 39 39 39 37 43 43 39 39 39 40 40 37 41 41 41 41 41 41 41 41 41 37 44 37\n", + " 37 38 38 37 37 39 39 44 44 39 39 39 41 41 46 39 39 39 39 39 39 39 39 39 39\n", + " 39 39 39 44 39 39 36 41 36 39 39 39 39 39 39 39 40 40 37 39 39 39 39 37 36\n", + " 36 46 37 41 41 43 39 39 39 44 39 39 39 37 40 40 40 47 36 39 39 42 42 42 42\n", + " 42 40 40 40 40 40 40 40 39 36 40 40 40 40 40 39 39 41 41 41 41 41 37 41 36\n", + " 36 41 37 37 40 40 40 38 41 46 41 38 42 36 39 39 39 43 40 40 40 40 40 39 36\n", + " 42 43 42 41 40 36 39 39 40 40 40 39 39 40 40 40 40 40 43 41 41 41 42 41 41\n", + " 36 42 38 42 38 38 41 41 40 40 40 40 40 39 35 36 40 42 40 40 40 40 40 38 38\n", + " 41 41 41 41 41 36 36 46 40 43 39 39 39 39 44 37 37 41 42 35 36 40 40 39 39\n", + " 39 39 39 39 44 42 40 40 36 40 40 36 36 40 42 37 37 40 40 40 36 44 42 36 41\n", + " 41 41 38 36 36 37 40 40 40 44 40 36 36 49 39 36 46 41 41 37 41 41 40 40 36\n", + " 40 40 40 40 40 40 40 40 41 44 43 41 41 41 41 38 40 36 40 41 42 37 39 37 41\n", + " 39 39 40 39 39 41 41 46 40 40 41 37 39 42 40 36 43 39 36 40 42 37 41 43 37\n", + " 40 42 40 36 35 36 41 41 37 41 41 42 40 41 41 41 41 41 46 37 40 46 39 44 41\n", + " 42 41 37 36 40 46 40 40 38 39 39 39 41 41 41 41 41 41 46 40 36 40 42 42 36\n", + " 40 40 39 39 43 37 39 38 39 39 39 39 41 36 39 36 40 40 40 42 35 35 41 41 45\n", + " 39 41 41 41 39 46 38 40 36 39 43 32 34 36 40 40 41 41 38 41 40 39 40 37 41\n", + " 44 42 40 38 39 40 42 37 40 36 40 42 43 36 39 39 37 37 39 42 39 39 39 37 37\n", + " 42 42 36 41 36 42 40 41 37 40 37 39 41 41 39 41 43 41 37 36 41 36 39 41 39\n", + " 40 40 40 32 39 39 40 42 40 39 39 39 39 39 40 40 37 42 46 41 41 43 38 44 41]\n" + ] + } + ], + "source": [ + "# examine a slice of the output\n", + "print M.trace('switchpoint')[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# take a look at the parents/children for these variables to see relationships\n", - "switchpoint.parents, disasters.parents, rate.children" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "({'lower': 0, 'upper': 110},\n", - " {'mu': },\n", - " {})" - ] - } - ], - "prompt_number": 7 + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting switchpoint\n", + "Plotting late_mean\n", + "Plotting early_mean\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create the model\n", - "model = Model([switchpoint, early_mean, late_mean, disasters])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/john/anaconda/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2499: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n", + " VisibleDeprecationWarning)\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Markov Chain Monte-Carlo sampling\n", - "M = MCMC(model)\n", - "M.sample(iter=10000, burn=1000, thin=10)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAloAAAFwCAYAAABghNUnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmYFNW5x/HvywgCsrgGF3AJSkSDMLjL4mDccMVc43aj\n", + "oiY3alSM3hgwMQxJVFwSNYvghhKNxi2JGnONuIziLgEURSIoIIiMOyhg2N77x+marunpGWamu6er\n", + "Z36f55mnq6ur6rxVM9P99jmnzjF3R0RERETyr12xAxARERFprZRoiYiIiBSIEi0RERGRAlGiJSIi\n", + "IlIgSrRERERECkSJloiIiEiBNJhomdkkM6s2s1mxdZub2RQze9vMHjezTWOvjTGzuWY2x8wOLWTg\n", + "IiKNYWZlZjbDzB5JPa80s8WpdTPMbHhsW72HiUhebahG63bg8Ix1o4Ep7t4HeDL1HDPbDTgR2C21\n", + "z41mphozESm2UcBsIBo00IHfuHt56uf/QO9hIlIYDb6JuPtU4LOM1ccAk1PLk4ERqeVjgXvcfY27\n", + 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"stream", - "stream": "stdout", - "text": [ - "\r", - " [--------------- 39% ] 3981 of 10000 complete in 1.5 sec" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\r", - " [-----------------55% ] 5515 of 10000 complete in 2.0 sec" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\r", - " [-----------------67%----- ] 6713 of 10000 complete in 2.5 sec" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\r", - " [-----------------81%----------- ] 8166 of 10000 complete in 3.0 sec" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\r", - " [-----------------95%---------------- ] 9532 of 10000 complete in 3.5 sec" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\r", - " [-----------------100%-----------------] 10000 of 10000 complete in 3.7 sec" - ] - } - ], - "prompt_number": 9 + "output_type": "display_data" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# examine a slice of the output\n", - "print M.trace('switchpoint')[:]" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFwCAYAAAB6uDWpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl4VEXWxt+TkDCsIoggEFyRHQHZRlASZRDcQGdUFBfw\n", + "+xBxx93RkTCOOsyA44L6IQqIA7gh4orKaJSRcYEB2WRRFgERBBGIhCXJ+f6ornT17bt1p7tvd3J+\n", + "z5Onb9+lqu6tTtfb55w6RcwMQRAEQRAEofJkBd0AQRAEQRCEqoIIK0EQBEEQhAQhwkoQBEEQBCFB\n", + "iLASBEEQBEFIECKsBEEQBEEQEoQIK0EQBEEQhAThKayIaAoRbSei5S7n5BPREiJaQURFCW2hIAhC\n", + "DBBRHhF9TEQrQ99JN4f2FxLRltB31RIiGmhccy8RrSOi1UTUP7jWC4KQ6ZBXHisiOh1AMYDpzNzR\n", + "5ngDAJ8BOJuZtxDRUcy8MymtFQRB8ICImgJoysxLiagugMUABgO4BMA+Zn7Ucn47ADMBdAfQHMB8\n", + "ACczc3lqWy4IQlXA02LFzAsA7HY55XIAs5l5S+h8EVWCIAQGM//IzEtD28UAvoESTABANpcMAjCL\n", + "mQ8z80YA3wLokYq2CoJQ9UhEjFUrAA1DpvdFRHRlAsoUBEGoNER0HIAuAD4P7bqJiL4moudD1nYA\n", + "aAZgi3HZFoSFmCAIQkwkQljlAOgK4BwAZwP4ExG1SkC5giAIcRNyA74G4JaQ5eoZAMcD6AxgG4AJ\n", + 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"output_type": "stream", - "stream": "stdout", - "text": [ - " early_mean\n" - ] - }, - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/home/john/anaconda/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2499: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n", - " VisibleDeprecationWarning)\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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WIGl9opIWj0gpa3OV+mVlzU96miKeaMUTn6ZqbF+feHnxxChqQooe402JTTl+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\nJ8lGCwHO6uPjd03+qWA+8ADZE7Etv2obMLOksNpxOfBN4J3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vbxj7ZxLRo1AuwFYAvrSWaworQRCqHtYfTPFaBwNxBepfueaXsR7w3CxWBw9G\nW1uc8GOxMmdNeRHrwGG27+WX1cCXlwdcfrn9+W6uQJOyMuAvf/GuXwur++4D/vCHyGP6GTrFWFnx\nK6w2bQLMz6G2lC1eHH5+euaZHX6WT6mssLKzWB08CNx2W3S9O3cCZ58dfa5Z16OPqqWPHnhA1eP1\nY8BJWJmCbtYs5zKA6PxdTz+tAsl12Vq4mhYrEztXoF+L1QsvRO7TglBfs2IFMH68e/tTQG8AVwAo\nIKIlob8BAP4K4HdEtBbAmaH3YOZVAF4BsArAewCuD7kKBUEQYiblwupf/wIaNVLbpnVKB9K6Wax6\n9oy0LrjhV1j5JZ5ZgcuXK2vRkCHADTeofTt3qhQLpgUJ8C+s9uwBJk70rlsLK7t79HIFWvErrKZP\nj3R96fJvvx34+9/ty3rjjXDgvp8YK/N6q0DwY+Vy6vPPPotug90CxLqfzDgmQM2g27vXfRaknbDS\n15sWKyfxrWnYEBgxInq/9bNpjVvTXHWVeo1VWNlhZ7G6807/1ycDZv43M2cxc2dm7hL6m8fMPzNz\nP2Y+mZn7M/MvxjUPM/NJzNyGmd8Psv3pisT3BI/0QWbg6QgjoikAzgWwg5k7OpzzBICBAPYDGMbM\nS5zK2749vF1WFh4otavCTVh9/XV4e/BgJRCcZvX5EVZnn20f72OHXqMuFmHVqVM4P1H9+uqVSOWv\nsg7aBw+q/W3aqPexLkViRVuGUimszP769ddIV+Vrr9mXNWqUfc4k3Vfffw8ce2x4v3k/lXEFmutF\nAspVaW2DnfVJX79sGbBjR3T6hlhd1LrtsU4QWLMmep/1s2m6Ak1mz1avsQoru8++FoTp5AoUkoPE\n9wSP9EFm4Gf4ngpggNNBIjoHwEnM3ArAtQCecSvM/HI2hZXdcS+cgqqPO87enWL98ncTLy1aAL/9\nbfi91SUE+EvqqIWAFlZ6jT29Fpvmp5+UJcuaCiFe9EBt94ysCUK9hFM8wspc6Niubk29euFtO2Gl\nZ51pTGGlz9+2Lfp6pzr19aZYA/wLK22xWrFCBbOb7SktDc8+tMPNFRirsNqzJ/oau7Ld/p+chJXT\nAth24ikdY6wEQRCCxHP4ZuYFAHa7nFKRdI+ZvwDQgIiaOJ1sjZFxy4RNBJx8MnD//V6tjMw+7rRW\nnvXL/5hjnC0M5eX24oZZ5ez57LPIQcua4FOXq+vUAevffqterZY2LSD0YFfZNd50zI6dsPJa0sbp\nfDduuSWYYPwSAAAgAElEQVSyL53yGrmV5ZZzS2NnsdLLy3ilDwDU87Brg1VYffSRvSgxr12yRFmu\n7NrmhJ27DogWSXv2qP7ZsgW2rFmjZvoB4X60s1i5LZYda2JPsVgJgiB4k4gYq+YATLvCFqjpyrZ4\nWaysX/br1gEff2xflh4wi4sjp/Y71Wk3K9DEHFzKy+0Hm3/9C2jXLtois3y5fd36fmrXjtxvXYZG\no59HZS1WOhu73WBvrSMRwuqJJyItHU7351aXnbCynm9nsbJe41bn4cP2+ateflnNptOC9Prro61l\n1jrMtfgA97xiTm3Uz9baJm0JMmfzWVm92r3sw4eB005zvt7JYuWE3edAtzNd1w0UEofE9wSP9EFm\nkKh0C9avZUcHhJfFKhZXoP4y1/FE//gH0LWr8/lm3XrgNPeZViIni5Wuq2bN2GaxWddUc3JjWt10\nsaDjmIBw7iVdj1mfX4vVgQNqZp9fV6D5DOyEVVaWu+s3VmFlFRJ+LVZWEdOypYq5MhOLrlkTPcFA\n15mVZS/inMSkxi143Wqx0m0+fFjNqpwxQ81AtKJj/4Doz6CbW9KsQ7fNC7vPrLZYvf229/VCZiPx\nPcEjfZAZJMJi5Su5HqDywMyZUwigEEBRhcXKzNBtftlb17SzYg4+gJoy/8AD0efpAeeOO8L7eveO\ntphpMfPllyow2c0dZ22THjDvuSfyuNNg72TdqIzF6uKLw9v6mejXRx6JPqaxa+M774StgPEIKzur\nUE6Oe1l2qRRisVg5PWvrrEBr2664wrlNVh55xNky5rU8TizCavFi9VpaqmaB6jxZVszcX9bn8dRT\n7u2J1cpkJ6y0mAynWCgCUAiiQsn7JAhCtSQRwupNAFcBABH1AvCLsR5XBIWFhbjggkIoYZWP8nI1\ncJgpFMzByStuQx93G2zNfebiyzVrOgsrnffJTdxkZdlbWPSizZpYhdWMGc51x5LVWg+Cun4zVsea\nMsCuLf/5T3jbSQx995161dYxqyvVSm6u+7qEfmKsXn45vG0NsvZrsbJalrxErBlgv8Rxvqu3xQrw\n7wq85JLwcbfkoXaJP+0480z3tvj5bNl9TqL35UP9f4uwEgSheuIprIhoFoCFAFoT0WYiuoaIRhLR\nSABg5ncBrCeibwFMAnC9W3nml3lZmRJFprAyf7l7/aLWx/3Etqh7CW9rYWXWoQdYHVsTj7Cy4jTb\ny8kVeOON6tXOWmbXHp053lqeVVjtM1ZK05MB3JJ2mukgnITVSSepVx1E7eXGrVkzsqw1a8LizHq9\ndYFlzTvvhLety8U4BY97xVh5TRT405/cj2t0ri4n3GYFOlm7vITV9OnAV1+pbbfnX68esHGjyrel\nidUV+N//Ru9z+9+zi1ETMheJ7wke6YPMwDPGipkv83HOjX4rtLp7rK5AO2Hl5Qo0v9ztzrULXrez\nWGVnh3P8AIkRVk6YLhyn8q3Y3ZsWK/v2Re7XIkPna7Iu1QJEBv9bMS1cXq7ADRucjzVoEA5wzsmJ\nHMxNUQVET2wwX/3gNMh7xVh5WaxGjwbuusu7/rlz3Y+bqwpodD+5CSv9/Iiin5m5LqLbZ3DuXPV8\n5s0L74tVWNm52d2sdCee6F2mkDlIfE/wSB9kBilfK9AaQ2V1BZrCykus6LLML3c3V6BJjRrRsw33\n749c/sVtwLVaOdxmGMaDXd12AkfXM3Jk5H6rBctuTUQ3i5XZD/HEWNnVm5sbLuvQIbUOn9P1TjFW\nbjgN8ubsuQMHYrdY+V1P0gsz+a3GK49VaWnkfZn3UqNGZN94WQxNUQWofidS6xmuXOl+rRNuwiqW\nlQ0EQRCqCilf0kYv7QKEZwU6Way0C8TNYlVWBnz6aeztsBtMrYN4LBarAwcSm8vHb/C6fjampQ2I\nHtTspu1bZ1WamAN2ZYSV2XemsFq4UK2xZ2I+Px0/Fcsz1Rar449Xa+dpzLXr9u0Dzjsv8jqnZz1h\ngrdl0Q86Z5mdxcpLWB0+HNmXZpoPnXtL88sviAmdU+2WW4Bnn43tWo2fuDJBEITqRCCLMGu8LFaX\neTghy8uBd9/1dtPYDfh+EnC6nWMdjPfvT2wuH6elevxitVjptfDMjO96wLZzBSZLWOlnZBdjZmfh\niMdiVVbm/vz8Wqzq1QOaOKa69U9urnpldk4Q6uYKNJ+V+YyswipW3Fy4fhFhVX2Q+J7gkT7IDFLu\nCjTZsEHNKDMHwViW9rBLMGqH1UqwebNzRmsTv67Ao49WVp9ECiu/7qe6daPjqwDnQbNNm/Caiz//\nrJb+MVMxaMx78Sus7O7fFFY1aoQHYrsy7fbFI6xKS2MTpk79XNns95rcXPX5KC+PdrmZFqsLLgDe\nfDPy+O9/77x0kjXpbKzs2FG56wERVtUJie8JHumDzCBQi9XSpcoqZYoIu0SDbq5APy4zq7Bq0cJ5\n0DQHZLNe03IxZIgKCtfl1q2rBkZTGFQmxur77wG7/5/+/aP3ffFFbGWbA+GOHdHpITTxWKzsRJDZ\nPzVqhM+xK7OoyF+ZTpgWK/2ZcsvIr3H6LPgRVtY4MTu0xeqLL4BBgyKPmXmszLQOdudYqVvXu243\nfv65ctcDIqwEQRCsBCqsgGiLlXZZ+cGPsCKyn/ZtHTSnTFFrB7ZvH95nlm0Kpd/+VmW81uuw1aun\nLBJ+BYgXK1bY77ezUOTlRe9zwxwIt293Fg9+LVZeMyPN52YmCPUb2ByPFTBWYeUkgv2sAuBHQGtX\n9/PPRx8bMkS9lpRExhqaOKXmqKwr0G0dQb+IsBIEQYgkcGEFRLu9rMHFgL1lpqzM38BmBm7rbNSm\noDjrLFXnDz8AJ5wQ3m+WbQYxN2wIdOoUfl+3bmJdgTc6JK9IhHAzUxIUFzsLU7/CyhStXsIqO1uV\n9fPP/u/FzFtl8pe/OF9jugLN+D0nKtNv+vnpRbbt0BYrN0pKnNvq9Kz8CKvevZ2P2fXXvHn2+aqs\n1K+vXmXmX/VB4nuCR/ogMwg0xkpjFVb6S1tDZJ+ksbw8Oq+PHebApAcvU1g98gjQuLHadovNqVVL\nDYDZ2ZHWhTp1lOXNmhPrnHOU1eO997zbaOIUH+V3PbbXXwcuusj+mGlhOHjQ2WLl1xVoWnXc0kEA\nYVdgo0bRMwKdcEqO6WapNIWV1Qr0ySdA377R59vhx2Kl2+EmnvzEex065Cys7CYXAP5cgbG6pHv2\nVLnHvOjcWc3Gzc6WBZirCxLfEzzSB5lBSi1WTlPnrQOPk0vEyv79wK23Ru6zGwztBk5TUJjCzm0Q\nPPpo9ZqVFS2sSkqiY6yYgaOOci4v0ej7cAt8N60cBw86C5R4YqzsXFam9S03F/jpJ7Xt19Lh5AaL\nV1jZCSAnYRCLK9DNMubXXev3c6/xY7GKVVj5EVVA+H+5bdvYyhcEQajqpFRYOVkprELAOsA4DQ7b\nbVckjMYUBnpAdxJWetu6XAoQFklWYVW7dnTwelaWGnwqm1zSjytL07ChenWr88knw6LDamEziWdW\noCmC6tcHFi2KFL4NGgBNm4bL1OvhueE0S9TO0qYHeTPdgjXGyk5YVcbFqgWeWz/Vrg20bu1dVqzB\n5JWNsaoM+vMRy+dTEAShOpBSYbV2rf1+L2HlhJ2wshNhpnVED/5Owkpf36dPZBnHHgv066e2nYSV\ndYmQRAgrc203Lxo1Uq9udTZoEI4jq1kz/GzM+9GxUBo74aEFU9eu4X2HDgEdOqhZl6WlStRYLUu6\n7oMH/VlH7JKX6jZa0WLYyWL19tuxWaz8oO/PzYLG7F5Hly7qNdZg8mRYrPyiLVZ+4seEqoHE9wSP\n9EFmkNIYK6cv+Xhdge++G73PyxVoJ6z8TKvfuFGVM25cdIxV7drRrkC9fEllcyENG+acAHXq1Mj3\nWqhoYdWvHzB/fuQ52dnhZ5SdHV4i5Te/CYuYnJxIt62dsNJ1mFaWQ4eA888HZs5U12RlRQsOc9Fo\nP6LTaakVu+faqJH627XLPni9adPYLFaxuAK9Zqe6WcX0tXv3Ru4/+mj3XFNuAfPJ4NhjgU2b1LYW\niiKsqg8S3xM80geZQUotVk7Cys5i1aFD5HV2g5xeksMLM4WDlyvQHCCtQfT6GqJIYaXTCJhWLr3c\nTmUsVuefHw6qt2PYMPv26TrtBr2srPAz+PXXcGC09X70Of/3f/Y5tez68tAh1QZdh962a+PBg5V7\nNk6CVVt97CxWWVnRz8S6MDQQdqnGErzuJqC9LFb6WTZrFrn/7LPVq7aUWrHmvUpEJnU3zD4XYSUI\ngmBP2gqr5cuBSy8N77NaXgD/OXQ2bgxvewkrs42PPRZpFTCPmZYQPQDrwGxADTyVtVjF66JyE1bZ\n2faB4xdcEN4++uhwWga7rOyAvejQswy1ENbb1voB1Xd+s6PbPUOv52oXY2UKKy24Dh2Ktibde697\n2Z07A8cdFy7Tqz1ewiorS81+1KlANLpMJ2uYdVagdgWbJNIVaJaln5ldH5ruYUEQhOpGWggr6yCt\nBz3TQmSHXfzNunXubdCuQGsaAI05iNWrF5mvymyvU5ZsjR9hZc0xdPzxke9jXdRZ35Nfi5XJ008r\nl2ObNsDtt4f75Pvv/ddvWqwA+3vXaTO8XIEdO6rXxx+PthwC3tYuPeBbRbO2RpmWLKuw0vfuZLGq\nUQPo3j1cJuDtCrQTVnfcEb62fv1oF7huu1PZ1udid16yhJX+DNl9xqZMSVydQvog8T3BI32QGaSF\nsLIO9Ha5puywmzHmNVOwR4/oOp0sVmZbrIwYEd5mBq67LvJ4ImKsYrVYjRypXq3Cyvw/tAamm4wb\nB3zzTfwLQK9dq+5ZP0PrvRMBZ5wBnHiisli5iSPd9tq17QWDX4tVVpaqT29r8WJaO+NJt6CPVcZi\npRfE1s/L6bPnJKys4j5RaxsCwFtvRe8z26d/oNgJK+sPBKFqMGbMGInxCRjpg8wgLYSVdaC3Wqyc\nsBNWbjEfL78cdnkddVRkHJfGOojZBdIzAy1bRu575hngyCPD72OJsSosDJdrEouw6ts3PNBahVW7\nduHz7CxW1lxbpjiNdTr9mWfau1uBSJfpiy+qdpppCMy6dNtr1gz3iSle/Qqrl14KLzpt9q0prKyf\nv1at3MtmjhZWTgslA0ps2vWl1dXnV1jp+7GmcLCe9+678Vus7FY/8GOxmj49bEk77bT46hYEQchk\nPIUVEQ0gotVEtI6I7rY5fhQRzSOipUS0goiGOZdlv9860Pt1BdqV75Z40iyndm0Vx2XWZ1eXnbBg\nVtdb8w6Zwki7Av0IK/0DxElY+c1XpJ+XfrWzPGVnh60NOn7MujyMuXxP587q9aGHgBde8G5D48bh\nmDYn8WMuPKyXTykujhQ7dsLq5pvV68iRkWU/+mh0Hfq516zp/Tmy5uwaPFht+7FY6TJ37nQ/X9fx\n+9+H93nFUOlnYD2uY6uOPjoyrs/6vAcOtL/n0093bqcbdsLK+hm78srw9oAB8dUjCIKQybgKKyLK\nBjARwAAA7QBcRkTWXMs3AljCzJ0B5AOYQES2csJpADl8GNi2TSWUBMJCJ9ZZY14uLLtBhjkyVsWv\nsAIiLVRAZEyUl7DyM+NMD8bFxWoNNy+sOZX0wGzWlZUVttAsWaJerW00rXHjxqnXo44Czj3Xuw05\nOWrtRcDeFQiE62/WzFlc6OduCit9bmFhZNlnnhndDn19Tk70c5k4MXJihDnrTpd73HFAr152d6iw\nWqycFs7W5+q+nDEjvN/JUqWD52vWBJ59NjqYXn/Oc3Iin4NXnBegLGtvvaWWo2EGZs3yvkbjZLGK\nJdeakLlIfE/wSB9kBl5fxT0AfMvMG5n5MICXAAyynLMNgJYm9QHsYmbbKB6nnDy5uSrHkHWKvJOl\nwSnPVXa2uyvQj+VLB01rYnGFmcLq8GFg8WKgfXv/1zMDBQXh9zpxJBCeeu92rTW2yWlWoBY2Ti5X\nHVRdt2443UONGv6eRU5O2N3qZLHSIuPWW51jlEyL1bZt4TYA6hrz/PbtgWnT1PZ994XPAdQEB6t4\nu+EGYMiQ8PWXXBIpeACVukDHQAFqge4WLaLvRZfpNtHAFFbmM3QSlbVrq9fcXBXLd/LJkcdNi6R5\nrd3n27qvvFxZKrXVyhTV2jrphFOM1dCh7tcJVQOJ7wke6YPMwEtYNQew2Xi/JbTPZDKA9kT0A4Cv\nAdziVNjMmdH7HnlEuZmA8Je8dcD/5BP1+uc/q1en2KOsLDXYNG6sfunbHfdixIjIpJRuFivre7vB\n9aSTwkHl1un0JpdfDlxzTXiAHz/e/0LFGieLlQkR8MEH6s9u5pw+B1CiRJdVo4a/xK25uWEXo1Ny\nUPM5OYkLU1hZr8/Ojk6XcfXValu7C3VqBWZ7y1CsAfrHHBP5/qSTIsv0msHptkC11X2tXb92MVZm\nKgO7BKxOdbRpY99Os++/+MJfWUBYWFnFnSAIQnXHy9nmw2GFPwJYysz5RHQigA+J6BRm3hd9aiF+\n8xsdS5MPIB/9+4cHKSdhpQceL+tAVpYq4+DBSGuPxo/FKisrMtjbKXjdDrv95qBjPW62RwsqHTOT\nm2s/YD37LHDttfb1W2OsnITV736ntnWmbzt35YwZSgyYSUfN85yeQU5OWFhZrVDm9VZrpJOwMp+/\nncXKakUy45Lq1FECQNdhtjme5KRTp6pnxww8/LBKHbF1qzpm/Uyef76yig0d6p3HykxgC4Rzb+l7\nMZ/j4sXAli3h99ZnXL9+dAZ3QM32tEu0az4Hr2SfTq5Ac39RURGKiooAAB9/7F6eIAhCVcTrt+ZW\nAHnG+zwoq5XJaQBeBQBm/g7ABgAOS84WokOHQgCFUMIqckB1ElYVjQ2daw5S1innublKuNm5oeKZ\nIeXHYqUxB1c9+NWuHT7faUFhE6+AZnMhYyv6Gv38tEB0E0FmnSaXXw5cdVWkxcrESSjk5qqgarty\n7Ra7JlJCwMtiNXBgpAVHl60nIFivy8pSs+Lef9+5nbFixmLVqKE+e05if8AA9QyByFmEgBJder8d\n2hVotVjt2xddl/W56SVnnLC2M5YUDeb/z+23q1frD4D8/HwUFhYCKMSZZxb6L1xIeyS+J3ikDzID\nr9/tiwC0IqLjAPwA4FIAl1nOWQ2gH4DPiKgJlKha71TgSy8pC5VeB838Ym/SRM3IygtJOSdhZVKr\nVnjAKStTYkEnqrQSj7Dyu24hEDlQNm+ugs7r1FGuwCOPdJ+xqHFLCllYqALD7e7DXPBYC5vmzVWb\nXnnFvi43YaWxLpNjvRZQaSt08HZOTtgCaS3XvMYszy7Zqj5Xu8WaN48UnXrb2j+msLIGn5v9c/rp\n9rma/GA+fydXoNVSOW5ceN+bb7p/Fs0YK7Msfc95ecDs2dH1AEp4N24ctnzaxVjFwrBhwKRJSuTp\nss44A/jb34C//z3aYiVUXSS2J3ikDzIDV2HFzKVEdCOA9wFkA3iemb8hopGh45MAPAxgKhF9DWUB\nu4uZf7Yrr2vXcLLGF15QVghzYKhTB5gzJ/zeLrO6hkgNEk2ahPeVltq7TzTxxILEa7ECwqKga1f1\nZ/2hMXBgtNvGTVjp/ynrQLZypRJTq1aFr23UKGzZcmqvfkZOFjCzHVZhlZ2tFp6uVStSGOkYtxtv\nDF/z0kvKLWbnCnRC96N+hg0aRAoMJ3enW2oFqyvQLleTH6yzLAF3SxCz80LadlhjrKzilgi46KLo\nevwQa6602rXDz5hIxUPqJLuAs8taEAShuuIZacLM7wF4z7JvkrG9E8D5fiozFxT2Yy1xW07FbgDV\nFiuncpPlCtSBvHpwdRIq+jzNH/+o/ky8ljGxQ7v8zGt27ow+zynGy23mopPFqqzMXtzofU8+Gd53\n6aVKWDmtz2hH9+4q6aq23jRoEGmx0oLA+pyIlPXMrnw/ealiRdevU4VY98dTtlPwejw/FszP/HPP\nRVsHY0lCm5UV/Xl1C163zmYUBEGoDsQRwhs/prDyIyB273Y+ZjfTyxRWiVo3zW55DutAqS1rdeqE\nc3LZUVlXoBfxXOM16FstVrNnqySXTZqEB3q/KSlMseM1K2/gQPVqZ7Eyc3HZ4SQU4xVPbuXoNlnT\ndFRGWGkxqe/dOtvTxOszbR7/n/+JPu4lrMy229Xl5Ao8fFj1t5kvTMhsdGyPuKOCQ/ogM0ipsDIt\nOebUeSf27HE+Zhd/VV7u7gqMR1gNHhzdDuuSJ8XF6lUvNeKEH2GlB08/WbydrrXSoIF3vU5YLVYX\nXaTid448MlzfiBHK1fnXv7qXFYsrUAsnc1ke03Lj51lacXumlY0TslrI4nWPTZkSXmrJFFaxiLNY\n7sWvsGrVSi2bZKV5c/t7jWfWpZDeyGAePNIHmUFKv/5OOCG87WfgiUVY1aihXG163Tu7gTuexYWJ\nIjOz2w1wOnjea/HZWMSAW5CxU+yZk1jp3x/YvNn+mBd2rkDr2oINGypXn5ewMp+/TingREmJeiVS\n/ZqTExZbXhYrJxLlCrRaSa37gNgsVq+9Ft4ePjzc936XMnLDS2T5fY5r10bv27VLCWzr0k6CIAjV\nmZQKq549o/e5CYg77lAz2v797+hjVmGmBcCQISr5pVVkLFxoX38i0BYrLxIlrJzqcxKrRPZZw/3g\nFLxukp2tLEoTJriXZfaJk7A67zzg7beVCJ8+Xe2zuneJYhdWb70FtLUuxhQnplByakcswsrJ2hXL\njNR48fqfcGt7w4bqVYLXBQC49NKrsWOHy6/hGNi0aS2YL0hIWYKQalIqrMys0Ro3AXHTTepv7FiV\nasDEGmN11lnA9u3Ab3+rYlSsv/Z/+9u4m+3K//6v/7ITJayAcByOSTIGOKfgdWu9OTnAbbe5l6UT\nhwLOouGcc9TsxuzsyAV9dT16oeZYXYHxzgD0Ila3rB1OzzaW5ZRM7NJBOHHCCWoR7vvvj68uP3UI\nVQOv+J733puHffvGAahE7EEErbxPqWZIjFVmEHgkhB8XjN0Xd6NGkcfmzlWvWVnKVaZdSUBy4z0m\nT/Z/rnVWoBtez8XOTZSMAc6vxcqLFSsiZ4k5WaxGjVJ/TmixccEF3hayWOjWTS28HCtO7mUvi9VT\nTykXLeD8bOvW9d+OO++03+/nM/HHPzqng/Dz/ykWq+qBv8F8IIAmnmcJ8SGCKjMIXFj5SVhoHRw2\nbYrOX2Q37V4TT5btZHDTTcD8+cqy5oXbc+nY0X7JHu2aSSR+LFZ+hJV1pp5XjJUXRx7pbSGLhdat\n1cLLsXLSScB6m3S45ufRLv3G9deHt+0E3bZt4USvfnB6npMne98XkbNAjOWHT+/e3ucKgiBUdQIV\nVu+95y/uxSqaWrYMbzv9Ik9HYdWzJ/Dgg85r/Zm4Catly+wHvJYtw66yRJEoi5WVJlXoR63dpAVz\n3cZLLnG+1km4uCVttcNcLsn87Ddrpv6Sif6MvPFGcusRBEHIBAITVsxqPTU/xOPiMq+JZzZg0HhZ\n8pyeSbxxOU4kymJlsm5dZgurWKw48cxejAdTWD3zTHhJm8oSiytQXIJVG4nvCR7pg8wgcFegH/Qg\ndeqp0cecBnV9zWWXAQUFyWlXMol1Tbdk4WWxuvZa5UaLBb2WYFUm1SLDFFaDByemzGefBfLzvc/T\n/2sirKo2MpgHj/RBZhCYsIrFCqXP7dcv+hf0xx+7pzsYMyb2gT8dSDdh5TRoTppkv7+6k0qR8dFH\niUslYTJihL/zxGIlCIIQJqMsVnaYSUftrkm3L/vLL1eB116ki7DSpNtzDIp27YAzz3Q+rhemTuXz\nCtoim67/a4IgCEGQ8cLK65p0+7KvUwf4wx+8z0s3YSW5ihQrV7of1/m54gnoz1TEYlU9kPie4JE+\nyAwCE1axZJWuSsLKL4laMDhRZOpzDIJhw4AePYJuRerI9P81wR8ymAeP9EFmEIiwWrs2eiFjN6qj\nsEo3i1WmPscgmDo16Baklkz/XxMEQUgkgXwVxiKqgMoJq0x1YaWTsCoqij2vklB9yNT/MUEQhGTg\nKayIaAARrSaidUR0t8M5+US0hIhWEFFRohtZmS/uTP3STydh1bdv5j5HITXMn58+iXiF5DB27NiK\nGB8hGKQPMgNXVyARZQOYCKAfgK0AviKiN5n5G+OcBgCeAnA2M28hoqMS3cjKuBgyVRBUp+BnIfM5\n66ygWyAkG4nvCR7pg8zAK8aqB4BvmXkjABDRSwAGAfjGOOdyALOZeQsAMPPORDeyulmsvvgC6NAh\n6FYIgiAIghArXrag5gA2G++3hPaZtALQkIg+JqJFRHRlIhsIVD9h1aMHULt20K0QBEEQBCFWvCxW\nfib95wDoCuAsALUB/IeIPmfmdZVtnKYy4khmKgmCIFQeyaEUPNIHmYGXsNoKIM94nwdltTLZDGAn\nM5cAKCGiTwGcAiBKWBUWFlZs5+fnI9/PQmSofhYrQchUioqKUFRUFHQzhCQgg3nwSB9kBl7CahGA\nVkR0HIAfAFwK4DLLOXMBTAwFutcE0BPAo3aFmcIqFkRYCUJmYP3BJDOYBEGobrg6ypi5FMCNAN4H\nsArAy8z8DRGNJKKRoXNWA5gHYBmALwBMZuZViWxkgwbxXyvCShCqF0Q0hYi2E9FyY18hEW0JpYVZ\nQkQDjWP3htLJrCai/sG0WhCEqoJn5nVmfg/Ae5Z9kyzvxwMYn9imhbnsMqBnz2SVLghCFWMqgCcB\nTDf2MYBHmTnCmk5E7aAs8e2gJubMJ6KTmTmNMsmlBxLfEzzSB5lBRizCnJUFnHRSfNfWqpXYtgiC\nkN4w84JQ+IIVO/v1IACzmPkwgI1E9C1UmpnPk9fCzEQG8+CRPsgMqvScOWagbt2gWyEIQppwExF9\nTUTPhxIbA0AzRE7IsUspIwiC4JsqLawEQRBCPAPgeACdAWwDMMHlXD9pZgRBEGzJCFegIAhCZWDm\nHXqbiJ4D8FborTWlTIvQvijiTRdTVZD4nuCRPkguiUoXQ8yp+XFGRJyqugRBSA+ICMyc8rm5oRir\nt59qq8EAACAASURBVJi5Y+j9Mcy8LbQ9GkB3Zr48FLw+EyquqjmA+QBOsn5ZyfeXN/XrN8G+fcsA\nNAm6KRb0xy8x/Ver1ihMmNAJo0aNSkh5QvoS7/eXWKwEQahSENEsAH0BHEVEmwGMAZBPRJ2hRtcN\nAHS6mFVE9ApUOplSANeLghIEoTKIsBIEoUrBzNYkxgAwxeX8hwE8nLwWCYJQnZDgdUEQBMGTsWPH\nSib9gJE+yAzEYiUIgiB4IgHTwSN9kBmIxUoQBEEQBCFBiLASBEEQBEFIECKsBEEQBE8kvid4pA8y\nA4mxEgRBEDyR+J7gkT7IDMRiJQiCIAiCkCBEWAmCIAiCICQIcQUKgiAInsg6dWE2b96MpUuXJqSs\nmjVrom3btr7OlT7IDDzXCiSiAQAeA5AN4DlmHudwXncA/wFwCTO/bnNcVooQhGpGUGsFJhr5/vKm\nuqwVmJ39EOrUeTUhZZWXH0CDBuXYvHltQsoTEku831+urkAiygYwEcAAAO0AXEZEUdI6dN44APMQ\n/hQHSiJWqJb6gq1T6svs+gShKlJWdh/27l2akL/i4jdRWhr0HQmJxivGqgeAb5l5IzMfBvASgEE2\n590E4DUAPyW4fXFT1QctEVZSX7rXJwiCUB3xElbNAWw23m8J7auAiJpDia1nQrvEXi4IglDFkBxK\nwSN9kBl4Ba/7EUmPAbiHmZmICGniChQEoepARNMBzGLm94JuS3VFAqaDR/ogM3ANXieiXgAKmXlA\n6P29AMrNAHYiWo+wmDoKwH4AI5j5TUtZYskShGpIIoLXiagmgEsBnAtgIdREml8rW24M9UvwugfV\nJXg9saxF06bnYds2CV5PR+INXveyWC0C0IqIjgPwA9QX22XmCcx8gtGIqQDesoqq0HliyRIEIV4a\nATgBwB4A2wFMgfo+EgRBSCtchRUzlxLRjQDeh0q38Dwzf0NEI0PHJ6WgjYIgCLcDeJqZvwMAItrs\ncb6QYCSHUvBIH2QGnnmsBEEQgoaIzmfmt0Lb5zLzOymuX1yBHogrMB7EFZjOJCWPVSIgogFEtJqI\n1hHR3QksdwoRbSei5ca+hkT0IRGtJaIPiKiBcezeUBtWE1H/OOrLI6KPiWglEa0gopuTWScR/YaI\nviCipUS0iogeSfY9hsrIJqIlRKQHsWQ+041EtCxU35cpqK8BEb1GRN+EnmnPJPZf69B96b89RHRz\nCvrv3tBndDkRzSSimkl+preE6lpBRLeE9iWjvr7G9umxtlMQBCFlMHPS/qDch98COA5ADoClANom\nqOzTAXQBsNzY9zcAd4W27wbw19B2u1DdOaG2fAsgK8b6mgLoHNquC2ANgLZJrrN26LUGgM8B9Elm\nfaFybgMwA8CbKXimGwA0tOxLZn0vALjGeKZHJPt5hsrKArANQF6S7+84AOsB1Ay9fxnA1cmqE0AH\nAMsB/Abqf/1DACcmo75Q350F4EwAU+Pph8r8AWDBnXr1jmbgRwY4zf4Q+gu6HXZ/a7hp01ZBd53g\nQOj/Pubvi2RbrPwmGI0ZZl4AYLdl9wVQX8AIvQ4ObQ+Cmqp9mJk3Qn2h94ixvh+ZeWlouxjAN1A5\nvZJZ5/7QZi7UwLU7mfURUQsA5wB4DmH7edLq09Va3ielPiI6AsDpzDwFUPGDzLwnWfVZ6Af1f7A5\nyfXtBXAYQG0iqgGgNtSkk2TV2QbAF8x8gJnLAHwC4PdJqu9mACeH6rw1hjYKCUJyKAWP9EFmkOxF\nmO0SjPZMYn1NmHl7aHs7ws7+ZlAWH7MdEYlOY4HULMkuAL5IZp1ElAXgv1BWgGeYeSURJfMe/wHg\nTgD1jX3JrI8BzCeiMgCTmHlyEus7HsBPpGaungJgMdQAnYrPzBAAs0LbSauPmX8mogkAvgdQAuB9\nZv4wiZ+ZFQAeIqKGAA5AifJFSM49toSyMNYEcAuAP8fQTiEBSMB08EgfZAbJFlaBRQsyM5N77qy4\n2kZEdQHMBnALM+8jChtcEl0nM5cD6ByytrxPRAWW4wmrj4jOA7CDmZcQUb5DexL9THsz8zYiagzg\nQyJancT6agDoCuBGZv6KiB4DcE8S6wMAEFEugPOhXGKRhSW4PiI6EUosHgeVluBVIroiWXUy82oi\nGgfgAwC/Qrn5ypJU320AJkBZ5ARBENKWZLsCt0LFlWjyoH6lJovtRNQUAIjoGAA7HNrRIrQvJogo\nB0pUvcjMb6SiTgAIuazeAXBqEus7DcAFRLQByrpyJhG9mMT6wMzbQq8/AZgD5RZKVn1bAGxh5q9C\n71+DElo/Jrn/BgJYHLpHILmfl24AFjLzLmYuBfA6gN8iiffIzFOYuRsz94VyVa9Fcu5xBTOvYOY1\nzLwmljYKgiCkkmQLq4oEo6Ff7pcCiEoemkDehArWRej1DWP/ECLKJaLjAbQC8GUsBZMyTT0PYBUz\nP5bsOonoKD2biohqAfgdgCXJqo+Z/8jMecx8PJTr6iNmvjKJ91ebiOqFtusA6A8VCJ2s+/sRwGYi\nOjm0qx+AlQDeSkZ9Bpch7AbU5SarvtUAehFRrdDntR+AVUjiPRLR0aHXlgAuAjAzSfdYQERvEdGr\nRPRqLG0UEoPE9wSP9EGGEE/Eeyx/UL/Y10AFqt6bwHJnQQXmHoKK4xoOoCGA+VC/mj8A0MA4/4+h\nNqwGcHYc9fUBUA7l7lgS+huQrDoBdISKr1oKYBmAO0P7k3aPRjl9EZ4VmKz7Oz50b0uhYnXuTfb9\nQcVWfQXgayhrzhFJrq8OgJ0A6hn7ktp/AO6CEozLoQLHc5J8j5+G6lsKoCBZ9wg1E7d7aLtFvJ/t\nSvxPsOCOzAqM509mBaYziHNWoCQIFQQh7SGiyQAOMfMNRPQ0M1+f4vpZvivdkQSh8SAJQtMZStJa\ngYIgCOlAMcLpVUqCbIggCIIbSc+8LgiCkAB2AjgtlE6iPOjGVEckvid4pA8yA3EFCoKQERBRG6hM\n7asCqFtcgR6IKzAexBWYzogrUBCEKgsR6ZmVtUJfdoNdLxAEQQgIEVaCIKQ9zHwZUJH2ZHTAzREE\nQXBEhJUgCGkPEbWH8uXkAGgfcHOqJTq2R5ZVCQ7pg8xAYqwEQUh7iEiPJAcBvMfMX6e4fomx8kBi\nrOJBYqzSGYmxEgShKrPI2G5BRC2Y+Z3AWiMIguCACCtBEDKB/wXwGZTZoQ/Cy+QIgiCkFSKsBEHI\nBFYz83gAIKLGzPxC0A2qbkh8T/BIH2QGEmMlCELaQ0SPADgaymK1nZnvS3H9EmPlgcRYxYPEWKUz\nEmMlCEJV5j4ALQD8AhXALgiCkJbIkjaCIGQCjwEYw8x7ATwZdGMEQRCcEGElCEImUA5gU2j7lyAb\nUl2RdeqCR/ogM5AYK0EQ0h4iGgfgWKiZgZ2YeUSK65cYKw8kxioeJMYqnZEYK0EQqiShZWxeA3AU\n1Cj5dLAtEgRBcEaElSAIaQ0zMxEVMPPfgm6LIAiCFyKsBEFIa4hoEIBBRHQ2gJ8BgJkvDrZV1Q/J\noRQ80geZQcpirIgoHR3cgiAkmXhiFEyI6BlmHqVfE9WuGNsgMVYeSIxVPEiMVToTb4xVSmcFMnOV\n+BszZkzgbZD7kHvJhL8E0ZKIzg29nkNE5ySqYEEQhEQjrkBBENKdV6EC118B0DjgtgiCILgiwkoQ\nhLSGmacF3QZB4nvSAemDzECEVRzk5+cH3YSEUFXuA5B7EYRkI4N58EgfZAYpDV5PVV2CIKQH8QZ/\nphvy/eWNBK/HgwSvpzMZEbwuCIIgCIJQlfEUVkQ0hYi2E9Fyl3OeIKJ1RPQ1EXVJbBMFQRCEoJF1\n6oJH+iAz8HQFEtHpAIoBTGfmjjbHzwFwIzOfQ0Q9ATzOzL1szhNTuiBUM8QVWH0QV2A8iCswnUma\nK5CZFwDY7XLKBQBeCJ37BYAGRJRu/1mCIAiCIAhJJxExVs0BbDbebwHQIgHlCoIgCIIgZBSJCl63\nmsrS0eYqCIIgxInE9wSP9EFmkIg8VlsB5BnvW4T2RVFYWFixnZ+fL/l6BKGKUVRUhKKioqCbISQB\nyaEUPNIHmYGvPFZEdByAt3wEr/cC8JgErwuCAAQTvE5EUwCcC2CH/s4iooYAXgZwLICNAC5h5l9C\nx+4FcA2AMgA3M/MHNmXK95cHErweDxK8ns4kLXidiGYBWAigNRFtJqJriGgkEY0EAGZ+F8B6IvoW\nwCQA18faCEEQhAQyFcAAy757AHzIzCcD+FfoPYioHYBLAbQLXfM0EUl+P0EQ4sbTFcjMl/k458bE\nNEcQBKFyMPOCkJXd5AIAfUPbLwAoghJXgwDMYubDADaGfiD2APB5ShqbQcg6dcEjfZAZyFqBgiBU\nB5ow8/bQ9naE/VXNECmitkDNdBYsyGAePNIHmUHamLynTZuGp556yvH45MmTU9gaQRCqKqFgKbeA\nm3QMxhEEIUNIG4sVkXt82LPPPosRI0akqDWpp7y8HFlZYZ3LzJ7PRBAE32wnoqbM/CMRHQNgR2i/\nzGoWBAFA4mY1p42wMhk6dCi2bt2KsrIyzJw5E4sWLcKaNWtQUFCAa6+9Fj179sT111+PgwcPokuX\nLnj00Uejyti4cSMuv/xytGzZEqtWrcKDDz6I5557Dps3b8Zrr72Gk046CdOmTcOUKVNQVlaGv/zl\nLygoKMD48ePxzjvvYO/evRg3bhz69euHYcOGoVatWvjuu+9Qp04dzJkzJ6Kuf/7zn3j++eexd+9e\njB49GldccQV++uknDB8+HMXFxcjLy8OLL76Il156CY899hiICGPHjkX//v2Rn5+Pnj17YsmSJRg6\ndCjee+897N+/H6NGjcLAgQNT9cgFoarzJoCrAYwLvb5h7J9JRI9CuQBbAfjSrgBTWFVHJL4neKQP\nkov1B1PcOcOYOSV/qipnpk2bxhMnTmRm5v379zMz85w5c/i+++5jZuZu3bpVnHvxxRfz+vXrmZl5\n1KhRvGjRoqjyNmzYwG3btuXy8nL+4IMPuHv37szMPHfuXH7ggQd4165dPGDAAGZmLi4u5vz8/Ii6\nt2/fzn379mVm5mHDhvGLL77IzMyXXnopL1u2LKIufc3+/fu5a9euzMw8evRofv311yvOKS0t5VNO\nOYUPHjzIe/furbif/Px8/uijjyqewVVXXeX6nAQhkwj936fse0ZViVkAfgBwCGpViOEAGgKYD2At\ngA8ANDDO/yOAbwGsBnC2Q5nJfVBVgHr1jmbgRwY4zf4Q+gu6HXZ/a7hp01ZBd53gQLzfX2lnsSov\nL8edd96J5cuXo6SkBB07RqXOwpo1a3DNNdcAAIqLizFgwACceuqpUee1a9cORIRjjjkG7dq1AwA0\na9YM8+fPx3fffYeVK1eioKAAALBz504AwPTp0zFz5kxkZWXhxx9/rCirS5cuAIC8vDzs3h25dOK8\nefPwxBNPgJnx3XffAQBWr16N+++/v+Kcn376CS1btkRubi5yc3ORk5ODsrIyAED37t0rzuvWrVuM\nT0wQBBN2nsncz+H8hwE8nLwWCYJQnUg7YbV06VLs2bMHn3zyCWbPno23334bQGQMVuvWrTF+/Hi0\nbNkSACoEihXzGnObmXHCCSegU6dOFeWXlpYCACZOnIhly5Zhx44dOP300x2vN3nooYewYMECMDNO\nPPFEAEDbtm3xySef4MILLwQzo3Hjxti0aRMOHjyIgwcP4tChQ8jOzgaAiNgqc1sQBEEQhMwirYQV\nEaFNmzbYtGkT+vfvjzZt2lQImoKCAgwePBjDhw/HuHHjcN111+HAgQPIzs7GlClTkJeXF1WWvtZu\nu1GjRhgyZAj69u2L7OxsdOzYEY8//jj69OmD3r17o1evXqhXr55jO00uuugi9OnTB127dkXDhg0B\nAPfeey+GDRuGxx9/vCLG6p577sEZZ5yBrKwsPPTQQ77KFgRBSAckvid4pA8yA19L2iSkIlkSQhCq\nHUEsaZMM5PvLG1nSJh5kSZt0Jt7vr7SyWMXL2rVrMXLkyIh9M2bMQLNmzQJqkSAIgiAI1ZEqIaxO\nPvlkfPzxx0E3QxAEQRCEao5ESguCIAiejB07Nv68PkJCkD7IDCTGShCEpCExVtUHibGKB4mxSmfi\n/f4Si5UgCIIgCEKCEGElCIIgCIKQIERYCYIgCJ5IfE/wSB9kBhJjJQhC0pAYq+qDxFjFg8RYpTNJ\ni7EiogFEtJqI1hHR3TbHjyKieUS0lIhWENGwWBshCIIgCIJQFXAVVkSUDWAigAEA2gG4jIjaWk67\nEcASZu4MIB/ABCKqEvmxBEEQBEEQYsHLYtUDwLfMvJGZDwN4CcAgyznbANQPbdcHsIuZSxPbTEEQ\nBCFIJL4neKQPMgMvy1JzAJuN91sA9LScMxnAR0T0A4B6AC5JXPMEQRCEdEAW/g0e6YPMwMti5Sfa\n748AljJzMwCdATxFRPUq3TJBEARBEIQMw8titRVAnvE+D8pqZXIagIcAgJm/I6INAFoDWGQtrLCw\nsGI7Pz8f+fn5MTdYEIT0paioCEVFRUE3QxAEITBc0y2EgtDXADgLwA8A/r+9+w+eo67vOP58+Q1i\nAQky6YAJ0WAbLTJFJRBUin6tjIZYpWpHSMUqoM3UAe0wUwnO1O8302lBZqqMYjFGZKwTJE5EGxx+\nGAZvrBbBKBCVRBM1QwISRURUiPNN8u4ft9+wfLm73bvv3u3u3esxc5PbH7f7/tzndr/v++w7e3cD\nKyJia2qdjwG/jYjVko4BvgecFBGPztiW/7uy2Yjx7RaGx3RtT7vLUb7dQi+6u91CVh9YsXo9f2Xe\nx0rSWcBVwBhwbURcLmklQESskTQPuA54Ac1Li5dHxPUttjPyJyazUePEanQ4seqF72NVZb2evzJv\nixARtwC3zJi3JvX8EeDN3e7YzMzMbNj4J23MzMzMCuLEyszMMvkeSuVzH9SDfyvQzPrGNVajwzVW\nvXCNVZX17bcCzczMzCwfJ1ZmZmZmBXFiZWZmmVzfUz73QT24xsrM+sY1VqPDNVa9cI1VlbnGyszM\nzKxkTqzMzMzMCuLEyszMMrm+p3zug3pwjZWZ9Y1rrEaHa6x64RqrKnONlZmZmVnJnFiZmZmZFcSJ\nlZmZZXJ9T/ncB/XgGisz6xvXWI0O11j1wjVWVeYaKzMzM7OSZSZWkpZJ2iZpu6RL26wzLukeST+U\n1Cg8SjMzM7MamNNpoaQx4GrgTOBB4LuSNkbE1tQ6RwGfAt4YEbslzetnwGZmNnjTtT0TExMlRzK6\n3Af10LHGStKrgImIWJZMrwKIiCtS67wfODYiPtJxR65RMBs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kY6WEXhAfq1273NPa62q3TjtZgIIIq06d4tMQmYVO27aWIFZceilw9dXmfNU9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\n+I1hAsJRnZCruLXaa5v1WkfJ51d8YJq0ycDK6UeTgdVkTNMbWP1Gn4H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- "text": [ - "" - ] - } - ], - "prompt_number": 11 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# generate a composite figure for the model\n", + "plot(M)" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/SciPy.ipynb b/SciPy.ipynb index 9da7413..9242c51 100644 --- a/SciPy.ipynb +++ b/SciPy.ipynb @@ -1,465 +1,1351 @@ { - "metadata": { - "name": "", - "signature": "sha256:768eaf72b7619ca0f75d8938dbd702fec1bf6e99e91c3f51b35f37f95dcdf6ac" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "SciPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# needed to display the graphs\n", - "%matplotlib inline\n", - "from pylab import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numpy import *\n", - "from scipy.integrate import quad, dblquad, tplquad" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# integration\n", - "val, abserr = quad(lambda x: exp(-x ** 2), Inf, Inf)\n", - "val, abserr" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "(0.0, 0.0)" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.integrate import odeint, ode" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# differential equation\n", - "def dy(y, t, zeta, w0):\n", - " x, p = y[0], y[1]\n", - " \n", - " dx = p\n", - " dp = -2 * zeta * w0 * p - w0**2 * x\n", - "\n", - " return [dx, dp]\n", - "\n", - "# initial state\n", - "y0 = [1.0, 0.0]\n", - "\n", - "# time coodinate to solve the ODE for\n", - "t = linspace(0, 10, 1000)\n", - "w0 = 2*pi*1.0\n", - "\n", - "# solve the ODE problem for three different values of the damping ratio\n", - "y1 = odeint(dy, y0, t, args=(0.0, w0)) # undamped\n", - "y2 = odeint(dy, y0, t, args=(0.2, w0)) # under damped\n", - "y3 = odeint(dy, y0, t, args=(1.0, w0)) # critial damping\n", - "y4 = odeint(dy, y0, t, args=(5.0, w0)) # over damped\n", - "\n", - "fig, ax = subplots()\n", - "ax.plot(t, y1[:,0], 'k', label=\"undamped\", linewidth=0.25)\n", - "ax.plot(t, y2[:,0], 'r', label=\"under damped\")\n", - "ax.plot(t, y3[:,0], 'b', label=r\"critical damping\")\n", - "ax.plot(t, y4[:,0], 'g', label=\"over damped\")\n", - "ax.legend();" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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SJUvon//8Z0optddfUFBgPx4ZGUm3bNli/zxz5kz66quvUkopfe+992hCQoJT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\npNcknSzz74P3+HwnjrtNmzbR09PjTcEAbNiwgbGxMW7cuOH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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.fftpack import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# fourier transform\n", - "N = len(t)\n", - "dt = t[1]-t[0]\n", - "\n", - "# calculate the fast fourier transform\n", - "# y2 is the solution to the under-damped oscillator from the previous section\n", - "F = fft(y2[:,0]) \n", - "\n", - "# calculate the frequencies for the components in F\n", - "w = fftfreq(N, dt)\n", - "\n", - "fig, ax = subplots(figsize=(9,3))\n", - "ax.plot(w, abs(F));" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Linear Algebra" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "A = array([[1,2,3], [4,5,6], [7,8,9]])\n", - "b = array([1,2,3])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SciPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import *\n", + "from scipy.integrate import quad, dblquad, tplquad" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# solve a system of linear equations\n", - "x = solve(A, b)\n", - "x" - ], - "language": "python", + "data": { + "text/plain": [ + "(0.0, 0.0)" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "array([-0.33333333, 0.66666667, 0. ])" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# integration\n", + "val, abserr = quad(lambda x: exp(-x ** 2), Inf, Inf)\n", + "val, abserr" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.integrate import odeint, ode" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# eigenvalues and eigenvectors\n", - "A = rand(3,3)\n", - "B = rand(3,3)\n", - "\n", - "evals, evecs = eig(A)\n", - "\n", - "evals" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8U1X6/z8nS9ukS5rue4GyyCKLrIJIAVnc+CGIIFtR\n", + 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ratio\n", + "y1 = odeint(dy, y0, t, args=(0.0, w0)) # undamped\n", + "y2 = odeint(dy, y0, t, args=(0.2, w0)) # under damped\n", + "y3 = odeint(dy, y0, t, args=(1.0, w0)) # critial damping\n", + "y4 = odeint(dy, y0, t, args=(5.0, w0)) # over damped\n", + "\n", + "fig, ax = subplots()\n", + "ax.plot(t, y1[:,0], 'k', label=\"undamped\", linewidth=0.25)\n", + "ax.plot(t, y2[:,0], 'r', label=\"under damped\")\n", + "ax.plot(t, y3[:,0], 'b', label=r\"critical damping\")\n", + "ax.plot(t, y4[:,0], 'g', label=\"over damped\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.fftpack import *" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "evecs" - ], - "language": "python", + "data": { + "image/png": [ + 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"prompt_number": 11, - "text": [ - "array([[-0.24560175, -0.93054452, 0.46758953],\n", - " [-0.84521949, -0.00332278, -0.56599975],\n", - " [-0.4746407 , 0.36616371, 0.67897299]])" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "display_data" + } + ], + "source": [ + "# fourier transform\n", + "N = len(t)\n", + "dt = t[1]-t[0]\n", + "\n", + "# calculate the fast fourier transform\n", + "# y2 is the solution to the under-damped oscillator from the previous section\n", + "F = fft(y2[:,0]) \n", + "\n", + "# calculate the frequencies for the components in F\n", + "w = fftfreq(N, dt)\n", + "\n", + "fig, ax = subplots(figsize=(9,3))\n", + "ax.plot(w, abs(F));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Algebra" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "A = array([[1,2,3], [4,5,6], [7,8,9]])\n", + "b = array([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "svd(A)" - ], - "language": "python", + "data": { + "text/plain": [ + "array([-0.33333333, 0.66666667, 0. ])" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "(array([[-0.23411483, 0.96016594, 0.15255034],\n", - " [-0.84815445, -0.12501447, -0.51478677],\n", - " [-0.47520972, -0.24990547, 0.84363676]]),\n", - " array([ 1.3457633 , 0.23442523, 0.1079208 ]),\n", - " array([[-0.21657397, -0.81553512, -0.53665463],\n", - " [ 0.7658064 , 0.19902251, -0.61149865],\n", - " [-0.60550497, 0.54340824, -0.58143892]]))" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "execute_result" + } + ], + "source": [ + "# solve a system of linear equations\n", + "x = solve(A, b)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "text/plain": [ + "array([ 1.34211023, 0.22314378, -0.11368553])" + ] + }, + "execution_count": 10, "metadata": {}, - "source": [ - "Optimization" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "# eigenvalues and eigenvectors\n", + "A = rand(3,3)\n", + "B = rand(3,3)\n", + "\n", + "evals, evecs = eig(A)\n", + "\n", + "evals" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import optimize" - ], - "language": "python", + "data": { + "text/plain": [ + "array([[-0.24560175, -0.93054452, 0.46758953],\n", + " [-0.84521949, -0.00332278, -0.56599975],\n", + " [-0.4746407 , 0.36616371, 0.67897299]])" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, + "output_type": "execute_result" + } + ], + "source": [ + "evecs" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(x):\n", - " return 4*x**3 + (x-2)**2 + x**4\n", - "\n", - "fig, ax = subplots()\n", - "x = linspace(-5, 3, 100)\n", - "ax.plot(x, f(x));" - ], - "language": "python", + "data": { + "text/plain": [ + "(array([[-0.23411483, 0.96016594, 0.15255034],\n", + " [-0.84815445, -0.12501447, -0.51478677],\n", + " [-0.47520972, -0.24990547, 0.84363676]]),\n", + " array([ 1.3457633 , 0.23442523, 0.1079208 ]),\n", + " array([[-0.21657397, -0.81553512, -0.53665463],\n", + " [ 0.7658064 , 0.19902251, -0.61149865],\n", + " [-0.60550497, 0.54340824, -0.58143892]]))" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, + "output_type": "execute_result" + } + ], + "source": [ + "svd(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "x_min = optimize.fmin_bfgs(f, -0.5)\n", - "x_min" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAHxdJREFUeJzt3XmYVNWZx/HvyyIIKMgiIIsQBSKKKxIRI+2SiIlCYozG\n", + "zIxGjfGJjMk4WUazIJk8Gk2iMTGT5YkGTVSMS1zQqCjSEREhJKBCQwABpYk2CDayr+/8cartsu2G\n", + 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"output_type": "display_data" + } + ], + "source": [ + "def f(x):\n", + " return 4*x**3 + (x-2)**2 + x**4\n", + "\n", + "fig, ax = subplots()\n", + "x = linspace(-5, 3, 100)\n", + "ax.plot(x, f(x));" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Statistics" + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 2.804988\n", + " Iterations: 4\n", + " Function evaluations: 24\n", + " Gradient evaluations: 8\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import stats" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 0.46961745])" + ] + }, + "execution_count": 15, "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "x_min = optimize.fmin_bfgs(f, -0.5)\n", + "x_min" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create a (continous) random variable with normal distribution\n", - "Y = stats.norm()\n", - "\n", - "x = linspace(-5,5,100)\n", - "\n", - "fig, axes = subplots(3,1, sharex=True)\n", - "\n", - "# plot the probability distribution function (PDF)\n", - "axes[0].plot(x, Y.pdf(x))\n", - "\n", - "# plot the commulative distributin function (CDF)\n", - "axes[1].plot(x, Y.cdf(x));\n", - "\n", - "# plot histogram of 1000 random realizations of the stochastic variable Y\n", - "axes[2].hist(Y.rvs(size=1000), bins=50);" - ], - "language": "python", + 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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "display_data" + } + ], + "source": [ + "# create a (continous) random variable with normal distribution\n", + "Y = stats.norm()\n", + "\n", + "x = linspace(-5,5,100)\n", + "\n", + "fig, axes = subplots(3,1, sharex=True)\n", + "\n", + "# plot the probability distribution function (PDF)\n", + "axes[0].plot(x, Y.pdf(x))\n", + "\n", + "# plot the commulative distributin function (CDF)\n", + "axes[1].plot(x, Y.cdf(x));\n", + "\n", + "# plot histogram of 1000 random realizations of the stochastic variable Y\n", + "axes[2].hist(Y.rvs(size=1000), bins=50);" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "Y.mean(), Y.std(), Y.var()" - ], - "language": "python", + "data": { + "text/plain": [ + "(0.0, 1.0, 1.0)" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "(0.0, 1.0, 1.0)" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "execute_result" + } + ], + "source": [ + "Y.mean(), Y.std(), Y.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# t-test example\n", - "t_statistic, p_value = stats.ttest_ind(Y.rvs(size=1000), Y.rvs(size=1000))\n", - "t_statistic, p_value" - ], - "language": "python", + "data": { + "text/plain": [ + "(-1.193733722708372, 0.23272385793970249)" + ] + }, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "(-1.193733722708372, 0.23272385793970249)" - ] - } - ], - "prompt_number": 19 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# t-test example\n", + "t_statistic, p_value = stats.ttest_ind(Y.rvs(size=1000), Y.rvs(size=1000))\n", + "t_statistic, p_value" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Scikit-learn.ipynb b/Scikit-learn.ipynb index 19a12fa..441c54f 100644 --- a/Scikit-learn.ipynb +++ b/Scikit-learn.ipynb @@ -1,1002 +1,1067 @@ { - "metadata": { - "name": "", - "signature": "sha256:782e86572650d5805953978de96ae5b65c6dcc87182f8200a81f486950c39e93" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scikit-learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn contains simple and efficient tools for data mining and data analysis. It implements a wide variety of machine learning algorithms and processes to conduct advanced analytics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn import datasets\n", + "from sklearn import svm" + ] + }, { - "cells": [ + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Scikit-learn" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0. 5. ..., 0. 0. 0.]\n", + " [ 0. 0. 0. ..., 10. 0. 0.]\n", + " [ 0. 0. 0. ..., 16. 9. 0.]\n", + " ..., \n", + " [ 0. 0. 1. ..., 6. 0. 0.]\n", + " [ 0. 0. 2. ..., 12. 0. 0.]\n", + " [ 0. 0. 10. ..., 12. 1. 0.]]\n" ] - }, + } + ], + "source": [ + "# import a sample dataset and view the data\n", + "digits = datasets.load_digits()\n", + "print(digits.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "array([0, 1, 2, ..., 8, 9, 8])" + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "Scikit-learn contains simple and efficient tools for data mining and data analysis. It implements a wide variety of machine learning algorithms and processes to conduct advanced analytics." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "# view the target variable\n", + "digits.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "General" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "from sklearn import datasets\n", - "from sklearn import svm" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# import a sample dataset and view the data\n", - "digits = datasets.load_digits()\n", - "print(digits.data)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[ 0. 0. 5. ..., 0. 0. 0.]\n", - " [ 0. 0. 0. ..., 10. 0. 0.]\n", - " [ 0. 0. 0. ..., 16. 9. 0.]\n", - " ..., \n", - " [ 0. 0. 1. ..., 6. 0. 0.]\n", - " [ 0. 0. 2. ..., 12. 0. 0.]\n", - " [ 0. 0. 10. ..., 12. 1. 0.]]\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# view the target variable\n", - "digits.target" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "array([0, 1, 2, ..., 8, 9, 8])" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# train a support vector machine using everything but the last example \n", - "classifier = svm.SVC(gamma=0.001, C=100.)\n", - "classifier.fit(digits.data[:-1], digits.target[:-1])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n", - " gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n", - " random_state=None, shrinking=True, tol=0.001, verbose=False)" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# predict the target of the last example\n", - "classifier.predict(digits.data[-1])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "array([8])" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# persist the model and reload\n", - "import pickle\n", - "from sklearn.externals import joblib\n", - "joblib.dump(classifier, 'model.pkl')\n", - "classifier2 = joblib.load('model.pkl')\n", - "classifier2.predict(digits.data[-1])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "array([8])" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import os\n", - "os.remove('model.pkl')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# another example with the digits data set\n", - "svc = svm.SVC(C=1, kernel='linear')\n", - "svc.fit(digits.data[:-100], digits.target[:-100]).score(digits.data[-100:], digits.target[-100:])" - ], - "language": "python", + "data": { + "text/plain": [ + "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n", + " gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n", + " random_state=None, shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "0.97999999999999998" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# perform cross-validation on the estimator's predictions\n", - "from sklearn import cross_validation\n", - "k_fold = cross_validation.KFold(n=6, n_folds=3)\n", - "for train_indices, test_indices in k_fold:\n", - " print('Train: %s | test: %s' % (train_indices, test_indices))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Train: [2 3 4 5] | test: [0 1]\n", - "Train: [0 1 4 5] | test: [2 3]\n", - "Train: [0 1 2 3] | test: [4 5]\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# apply to the model\n", - "kfold = cross_validation.KFold(len(digits.data), n_folds=3)\n", - "cross_validation.cross_val_score(svc, digits.data, digits.target, cv=kfold, n_jobs=-1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "array([ 0.93489149, 0.95659432, 0.93989983])" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# use the grid search module to optimize model parameters\n", - "from sklearn.grid_search import GridSearchCV\n", - "gammas = np.logspace(-6, -1, 10)\n", - "classifier = GridSearchCV(estimator=svc, param_grid=dict(gamma=gammas), n_jobs=-1)\n", - "classifier.fit(digits.data[:1000], digits.target[:1000])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "GridSearchCV(cv=None,\n", - " estimator=SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", - " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False),\n", - " fit_params={}, iid=True, loss_func=None, n_jobs=-1,\n", - " param_grid={'gamma': array([ 1.00000e-06, 3.59381e-06, 1.29155e-05, 4.64159e-05,\n", - " 1.66810e-04, 5.99484e-04, 2.15443e-03, 7.74264e-03,\n", - " 2.78256e-02, 1.00000e-01])},\n", - " pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n", - " verbose=0)" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.best_score_" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "0.92400000000000004" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "classifier.best_estimator_.gamma" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "9.9999999999999995e-07" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# run against the test set\n", - "classifier.score(digits.data[1000:], digits.target[1000:])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "0.94228356336260977" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# nested cross-validation example\n", - "cross_validation.cross_val_score(classifier, digits.data, digits.target)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "array([ 0.93521595, 0.95826377, 0.93791946])" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Other Classifiers" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# import the iris dataset\n", - "iris = datasets.load_iris()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# k nearest neighbors\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier()\n", - "knn.fit(iris.data, iris.target)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_neighbors=5, p=2, weights='uniform')" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# decision tree\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "dtree = DecisionTreeClassifier()\n", - "dtree.fit(iris.data, iris.target)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n", - " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", - " random_state=None, splitter='best')" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# stochastic gradient descent\n", - "from sklearn.linear_model import SGDClassifier\n", - "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n", - "sgd.fit(iris.data, iris.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# train a support vector machine using everything but the last example \n", + "classifier = svm.SVC(gamma=0.001, C=100.)\n", + "classifier.fit(digits.data[:-1], digits.target[:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([8])" + ] + }, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,\n", - " fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',\n", - " loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,\n", - " random_state=None, shuffle=False, verbose=0, warm_start=False)" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# naive bayes\n", - "from sklearn.naive_bayes import GaussianNB\n", - "gnb = GaussianNB()\n", - "y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)\n", - "print(\"Number of mislabeled points : %d\" % (iris.target != y_pred).sum())" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# predict the target of the last example\n", + "classifier.predict(digits.data[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([8])" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Number of mislabeled points : 6\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "heading", - "level": 3, + "output_type": "execute_result" + } + ], + "source": [ + "# persist the model and reload\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "joblib.dump(classifier, 'model.pkl')\n", + "classifier2 = joblib.load('model.pkl')\n", + "classifier2.predict(digits.data[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "os.remove('model.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97999999999999998" + ] + }, + "execution_count": 8, "metadata": {}, - "source": [ - "Regression" + "output_type": "execute_result" + } + ], + "source": [ + "# another example with the digits data set\n", + "svc = svm.SVC(C=1, kernel='linear')\n", + "svc.fit(digits.data[:-100], digits.target[:-100]).score(digits.data[-100:], digits.target[-100:])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: [2 3 4 5] | test: [0 1]\n", + "Train: [0 1 4 5] | test: [2 3]\n", + "Train: [0 1 2 3] | test: [4 5]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# load another sample dataset\n", - "diabetes = datasets.load_diabetes()" - ], - "language": "python", + } + ], + "source": [ + "# perform cross-validation on the estimator's predictions\n", + "from sklearn import cross_validation\n", + "k_fold = cross_validation.KFold(n=6, n_folds=3)\n", + "for train_indices, test_indices in k_fold:\n", + " print('Train: %s | test: %s' % (train_indices, test_indices))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.93489149, 0.95659432, 0.93989983])" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# linear regression\n", - "from sklearn import linear_model\n", - "regr = linear_model.LinearRegression()\n", - "regr.fit(diabetes.data, diabetes.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# apply to the model\n", + "kfold = cross_validation.KFold(len(digits.data), n_folds=3)\n", + "cross_validation.cross_val_score(svc, digits.data, digits.target, cv=kfold, n_jobs=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=None,\n", + " estimator=SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", + " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False),\n", + " fit_params={}, iid=True, loss_func=None, n_jobs=-1,\n", + " param_grid={'gamma': array([ 1.00000e-06, 3.59381e-06, 1.29155e-05, 4.64159e-05,\n", + " 1.66810e-04, 5.99484e-04, 2.15443e-03, 7.74264e-03,\n", + " 2.78256e-02, 1.00000e-01])},\n", + " pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n", + " verbose=0)" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# regression coefficients\n", - "print(regr.coef_)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# use the grid search module to optimize model parameters\n", + "from sklearn.grid_search import GridSearchCV\n", + "gammas = np.logspace(-6, -1, 10)\n", + "classifier = GridSearchCV(estimator=svc, param_grid=dict(gamma=gammas), n_jobs=-1)\n", + "classifier.fit(digits.data[:1000], digits.target[:1000])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.92400000000000004" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163\n", - " 476.74583782 101.04457032 177.06417623 751.27932109 67.62538639]\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# mean squared error\n", - "np.mean((regr.predict(diabetes.data)-diabetes.target)**2)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "classifier.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9.9999999999999995e-07" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "2859.6903987680657" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# explained variance\n", - "regr.score(diabetes.data, diabetes.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "classifier.best_estimator_.gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.94228356336260977" + ] + }, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "0.51774942541329338" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# ridge regression\n", - "regr = linear_model.Ridge(alpha=.1)\n", - "regr.fit(diabetes.data, diabetes.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# run against the test set\n", + "classifier.score(digits.data[1000:], digits.target[1000:])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.93521595, 0.95826377, 0.93791946])" + ] + }, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n", - " normalize=False, solver='auto', tol=0.001)" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# lasso regression\n", - "regr = linear_model.Lasso()\n", - "regr.fit(diabetes.data, diabetes.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# nested cross-validation example\n", + "cross_validation.cross_val_score(classifier, digits.data, digits.target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other Classifiers" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the iris dataset\n", + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_neighbors=5, p=2, weights='uniform')" + ] + }, + "execution_count": 17, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 27, - "text": [ - "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n", - " normalize=False, positive=False, precompute='auto', tol=0.0001,\n", - " warm_start=False)" - ] - } - ], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# logistic regression (this is actually a classifier)\n", - "iris = datasets.load_iris()\n", - "logistic = linear_model.LogisticRegression(C=1e5)\n", - "logistic.fit(iris.data, iris.target)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# k nearest neighbors\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier()\n", + "knn.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n", - " fit_intercept=True, intercept_scaling=1, penalty='l2',\n", - " random_state=None, tol=0.0001)" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "heading", - "level": 3, + "output_type": "execute_result" + } + ], + "source": [ + "# decision tree\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "dtree = DecisionTreeClassifier()\n", + "dtree.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,\n", + " fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',\n", + " loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,\n", + " random_state=None, shuffle=False, verbose=0, warm_start=False)" + ] + }, + "execution_count": 19, "metadata": {}, - "source": [ - "Preprocessing" + "output_type": "execute_result" + } + ], + "source": [ + "# stochastic gradient descent\n", + "from sklearn.linear_model import SGDClassifier\n", + "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n", + "sgd.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of mislabeled points : 6\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# feature scaling\n", - "from sklearn import preprocessing\n", - "X = np.array([[ 1., -1., 2.],\n", - " [ 2., 0., 0.],\n", - " [ 0., 1., -1.]])\n", - "X_scaled = preprocessing.scale(X)" - ], - "language": "python", + } + ], + "source": [ + "# naive bayes\n", + "from sklearn.naive_bayes import GaussianNB\n", + "gnb = GaussianNB()\n", + "y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)\n", + "print(\"Number of mislabeled points : %d\" % (iris.target != y_pred).sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# load another sample dataset\n", + "diabetes = datasets.load_diabetes()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" + ] + }, + "execution_count": 22, "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# save the scaling transform to apply to new data later\n", - "scaler = preprocessing.StandardScaler().fit(X)\n", - "scaler" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# linear regression\n", + "from sklearn import linear_model\n", + "regr = linear_model.LinearRegression()\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163\n", + " 476.74583782 101.04457032 177.06417623 751.27932109 67.62538639]\n" + ] + } + ], + "source": [ + "# regression coefficients\n", + "print(regr.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2859.6903987680657" + ] + }, + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "StandardScaler(copy=True, with_mean=True, with_std=True)" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "scaler.transform(X)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# mean squared error\n", + "np.mean((regr.predict(diabetes.data)-diabetes.target)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.51774942541329338" + ] + }, + "execution_count": 25, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, - "text": [ - "array([[ 0. , -1.22474487, 1.33630621],\n", - " [ 1.22474487, 0. , -0.26726124],\n", - " [-1.22474487, 1.22474487, -1.06904497]])" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# range scaling\n", - "min_max_scaler = preprocessing.MinMaxScaler()\n", - "X_minmax = min_max_scaler.fit_transform(X)\n", - "X_minmax" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# explained variance\n", + "regr.score(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n", + " normalize=False, solver='auto', tol=0.001)" + ] + }, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "array([[ 0.5 , 0. , 1. ],\n", - " [ 1. , 0.5 , 0.33333333],\n", - " [ 0. , 1. , 0. ]])" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# instance normalization using L2 norm\n", - "X_normalized = preprocessing.normalize(X, norm='l2')\n", - "X_normalized" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# ridge regression\n", + "regr = linear_model.Ridge(alpha=.1)\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n", + " normalize=False, positive=False, precompute='auto', tol=0.0001,\n", + " warm_start=False)" + ] + }, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "array([[ 0.40824829, -0.40824829, 0.81649658],\n", - " [ 1. , 0. , 0. ],\n", - " [ 0. , 0.70710678, -0.70710678]])" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# category encoding\n", - "enc = preprocessing.OneHotEncoder()\n", - "enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n", - "enc.transform([[0, 1, 3]]).toarray()" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# lasso regression\n", + "regr = linear_model.Lasso()\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n", + " fit_intercept=True, intercept_scaling=1, penalty='l2',\n", + " random_state=None, tol=0.0001)" + ] + }, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 34, - "text": [ - "array([[ 1., 0., 0., 1., 0., 0., 0., 0., 1.]])" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# binning\n", - "binarizer = preprocessing.Binarizer().fit(X)\n", - "binarizer.transform(X)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# logistic regression (this is actually a classifier)\n", + "iris = datasets.load_iris()\n", + "logistic = linear_model.LogisticRegression(C=1e5)\n", + "logistic.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# feature scaling\n", + "from sklearn import preprocessing\n", + "X = np.array([[ 1., -1., 2.],\n", + " [ 2., 0., 0.],\n", + " [ 0., 1., -1.]])\n", + "X_scaled = preprocessing.scale(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardScaler(copy=True, with_mean=True, with_std=True)" + ] + }, + "execution_count": 30, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "array([[ 1., 0., 1.],\n", - " [ 1., 0., 0.],\n", - " [ 0., 1., 0.]])" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "heading", - "level": 3, + "output_type": "execute_result" + } + ], + "source": [ + "# save the scaling transform to apply to new data later\n", + "scaler = preprocessing.StandardScaler().fit(X)\n", + "scaler" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , -1.22474487, 1.33630621],\n", + " [ 1.22474487, 0. , -0.26726124],\n", + " [-1.22474487, 1.22474487, -1.06904497]])" + ] + }, + "execution_count": 31, "metadata": {}, - "source": [ - "Clustering" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# k means clustering\n", - "from sklearn import cluster\n", - "k_means = cluster.KMeans(n_clusters=3)\n", - "k_means.fit(iris.data)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "scaler.transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5 , 0. , 1. ],\n", + " [ 1. , 0.5 , 0.33333333],\n", + " [ 0. , 1. , 0. ]])" + ] + }, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n", - " n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n", - " verbose=0)" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "heading", - "level": 3, + "output_type": "execute_result" + } + ], + "source": [ + "# range scaling\n", + "min_max_scaler = preprocessing.MinMaxScaler()\n", + "X_minmax = min_max_scaler.fit_transform(X)\n", + "X_minmax" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.40824829, -0.40824829, 0.81649658],\n", + " [ 1. , 0. , 0. ],\n", + " [ 0. , 0.70710678, -0.70710678]])" + ] + }, + "execution_count": 33, "metadata": {}, - "source": [ - "Decomposition" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create a signal with 2 useful dimensions\n", - "x1 = np.random.normal(size=100)\n", - "x2 = np.random.normal(size=100)\n", - "x3 = x1 + x2\n", - "X = np.c_[x1, x2, x3]" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# instance normalization using L2 norm\n", + "X_normalized = preprocessing.normalize(X, norm='l2')\n", + "X_normalized" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 0., 1., 0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 34, "metadata": {}, - "outputs": [], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# compute principal component analysis\n", - "from sklearn import decomposition\n", - "pca = decomposition.PCA()\n", - "pca.fit(X)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# category encoding\n", + "enc = preprocessing.OneHotEncoder()\n", + "enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n", + "enc.transform([[0, 1, 3]]).toarray()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 1.],\n", + " [ 1., 0., 0.],\n", + " [ 0., 1., 0.]])" + ] + }, + "execution_count": 35, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "PCA(copy=True, n_components=None, whiten=False)" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pca.explained_variance_" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# binning\n", + "binarizer = preprocessing.Binarizer().fit(X)\n", + "binarizer.transform(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n", + " n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n", + " verbose=0)" + ] + }, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 39, - "text": [ - "array([ 2.77625101e+00, 9.03048616e-01, 3.02456658e-31])" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# only the 2 first components are useful\n", - "pca.n_components = 2\n", - "X_reduced = pca.fit_transform(X)\n", - "X_reduced.shape" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# k means clustering\n", + "from sklearn import cluster\n", + "k_means = cluster.KMeans(n_clusters=3)\n", + "k_means.fit(iris.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create a signal with 2 useful dimensions\n", + "x1 = np.random.normal(size=100)\n", + "x2 = np.random.normal(size=100)\n", + "x3 = x1 + x2\n", + "X = np.c_[x1, x2, x3]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, n_components=None, whiten=False)" + ] + }, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 40, - "text": [ - "(100L, 2L)" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# generate more sample data\n", - "time = np.linspace(0, 10, 2000)\n", - "s1 = np.sin(2 * time) # signal 1 : sinusoidal signal\n", - "s2 = np.sign(np.sin(3 * time)) # signal 2 : square signal\n", - "S = np.c_[s1, s2]\n", - "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", - "S /= S.std(axis=0) # standardize data" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# compute principal component analysis\n", + "from sklearn import decomposition\n", + "pca = decomposition.PCA()\n", + "pca.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.77625101e+00, 9.03048616e-01, 3.02456658e-31])" + ] + }, + "execution_count": 39, "metadata": {}, - "outputs": [], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# mix data\n", - "A = np.array([[1, 1], [0.5, 2]]) # mixing matrix\n", - "X = np.dot(S, A.T) # generate observations" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "pca.explained_variance_" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100L, 2L)" + ] + }, + "execution_count": 40, "metadata": {}, - "outputs": [], - "prompt_number": 42 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# compute independent component analysis\n", - "ica = decomposition.FastICA()\n", - "S_ = ica.fit_transform(X) # get the estimated sources\n", - "A_ = ica.mixing_.T\n", - "np.allclose(X, np.dot(S_, A_) + ica.mean_)" - ], - "language": "python", + "output_type": "execute_result" + } + ], + "source": [ + "# only the 2 first components are useful\n", + "pca.n_components = 2\n", + "X_reduced = pca.fit_transform(X)\n", + "X_reduced.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# generate more sample data\n", + "time = np.linspace(0, 10, 2000)\n", + "s1 = np.sin(2 * time) # signal 1 : sinusoidal signal\n", + "s2 = np.sign(np.sin(3 * time)) # signal 2 : square signal\n", + "S = np.c_[s1, s2]\n", + "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", + "S /= S.std(axis=0) # standardize data" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# mix data\n", + "A = np.array([[1, 1], [0.5, 2]]) # mixing matrix\n", + "X = np.dot(S, A.T) # generate observations" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 43, - "text": [ - "True" - ] - } - ], - "prompt_number": 43 - } - ], - "metadata": {} + "output_type": "execute_result" + } + ], + "source": [ + "# compute independent component analysis\n", + "ica = decomposition.FastICA()\n", + "S_ = ica.fit_transform(X) # get the estimated sources\n", + "A_ = ica.mixing_.T\n", + "np.allclose(X, np.dot(S_, A_) + ica.mean_)" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Seaborn.ipynb b/Seaborn.ipynb index 7e93fdd..14fbb51 100644 --- a/Seaborn.ipynb +++ b/Seaborn.ipynb @@ -1,446 +1,5479 @@ { - "metadata": { - "name": "", - "signature": "sha256:464c247fec623402de06a978f7770a298ad078fbd577a21ad7cf350c63810df5" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels." + ] + }, { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sb\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Themes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# global config settings to control things like style, font size, color palette etc.\n", + "sb.set(context=\"notebook\", style=\"darkgrid\", palette=\"dark\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 1, + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAYFJREFUeJzt2EEqRlEAhuF7pUgWYGI9dmFkB0Jm/0zIDozswnpMLECi\n", + "5JhL/+x/r3ie6TmDr269ne48xpgAaGwtPQDgPxFdgJDoAoREFyAkugAh0QUIba87fHv/GLs7a68A\n", + "8M18cjaN+9v5p7O1Rd3d2Z7mw9VGRv0G42k1bZ0fLD1jYz5vnqe7vR+/+59w+jqmi/lh6RkbcT2O\n", + "p8er/aVnbMzR5cs0n5wtPWMRfi8AhEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHR\n", + "BQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJ\n", + "LkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBI\n", + "dAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC8xhj6Q0A/4aXLkBIdAFCogsQEl2AkOgChEQX\n", + "IPQFu2UddlnlgNcAAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Seaborn" - ] + "output_type": "display_data" }, { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAcNJREFUeJzt2DFqFVEAhtE7khSSXbgBUUFwEzY2gqlSCHYRC9dgIdoJ\n", + "Kawi2Ni4CUEw4gayi0cKhbG3eHzNeDWcU85M8d/q486yrusAAPa7MXsAAPwPBBMAAsEEgEAwASAQ\n", + "TAAIBBMAgoN9L9efv9blcO8nAHCt7E4ux9H7W8ufz/fWcDk8GFfPX223arKbb16O72/vzp6xmTun\n", + "F+Pd+fU937Pji3Hv89PZMzbx7eHZuP/pw+wZm/n66Ml48PHH7Bmb+fL49jh/vZs9YzPHL47G7uRy\n", + "9oy/zi9ZAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwAC\n", + "wQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\n", + "EwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBM\n", + "AAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwCCZV3X2RsA4J/nhgkAgWACQCCYABAI\n", + "JgAEggkAgWACQPAbQxckyJWg960AAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels." - ] + "output_type": "display_data" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sb\n", - "%matplotlib inline" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAaRJREFUeJzt2LFN5GAYRdF/RkwnDrcFSiB2SAdoS6CEFR0QOp4SaIHQ\n", + "nSBh8g1GNxkMo3NCO3lfdGUftm0bAMBlx70HAMBvIJgAEAgmAASCCQCBYAJAIJgAENxdevn58bEd\n", + "T6fv2gIAu1vn85iWh8P/zy8G83g6jfVxvt6qnU2vy1jfbvi++2W8rLd739O0jHl93nvGVSzT85jf\n", + "171nXM3yZxrz3xu+7980Xufbve9xmcY6n/ee8e38kgWAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\n", + "EwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBM\n", + 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- "collapsed": false, - "input": [ - "# global config settings to control things like style, font size, color palette etc.\n", - "sb.set(context=\"notebook\", style=\"darkgrid\", palette=\"dark\")" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAcBJREFUeJzt1yGKlVEch+HzXVyBG7grGBExTxMEwWAeMFkMNrlruNgM\n", + "FpMweYIgCDaziOgKZgMuYT674fKWz6P4PPGc8msv/2Vd1wEAnLabPQAA/gWCCQCBYAJAIJgAEAgm\n", + "AASCCQDBrVOf683Nuuw0FYD/x9n+fPy4/rz8/n4ymMtuN35+/7Ldqslu37k/rt9/mD1jM/vHj8a3\n", + "15ezZ2zm7ouL8fHlm9kzNvHw1fPx9uI4e8Zmnl0exuHBYfaMzRw/HceTe09nz9jM1dd342x/PnvG\n", + "H+d8BIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwA\n", + "CAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEg\n", + "EEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBA\n", + "MAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgWNZ1nb0BAP56LkwACAQTAALBBIBAMAEg\n", + "EEwACAQTAIJfldsjGDxlD4AAAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, + "output_type": "display_data" + } + ], + "source": [ + "# sequential palette with linear increase in brightness\n", + "sb.palplot(sb.cubehelix_palette(8))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# seaborn has some nice built-in color palette features\n", - "sb.palplot(sb.color_palette())\n", - "sb.palplot(sb.color_palette(\"husl\", 8))\n", - "sb.palplot(sb.color_palette(\"hls\", 8))" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYFJREFUeJzt2EEqRlEAhuF7pUgWYGI9dmFkB0Jm/0zIDozswnpMLECi\n5JhL/+x/r3ie6TmDr269ne48xpgAaGwtPQDgPxFdgJDoAoREFyAkugAh0QUIba87fHv/GLs7a68A\n8M18cjaN+9v5p7O1Rd3d2Z7mw9VGRv0G42k1bZ0fLD1jYz5vnqe7vR+/+59w+jqmi/lh6RkbcT2O\np8er/aVnbMzR5cs0n5wtPWMRfi8AhEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHR\nBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJ\nLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBI\ndAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC8xhj6Q0A/4aXLkBIdAFCogsQEl2AkOgChEQX\nIPQFu2UddlnlgNcAAAAASUVORK5CYII=\n", - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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"iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# matplotlib colormap of evenly spaced colors\n", - "sb.palplot(sb.color_palette(\"coolwarm\", 7))" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFkCAYAAAAwmQKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXeQJNd95/l5LzPLV3VX+/F+egYeA2DgnQgCBAkCpEgt\n", + "yT3ppNuNkI6UKN6uNhgnaY8UT25X0m1Q4kkKcfdutdJR9JRIgiQAgoQHBm7gB9Mzg/GmfXmbme/d\n", + "H9ndMxiMqe6p6q7ueZ+IjDKdVe9VdVZ+82fe7ye01hgMBoPBYFh8yIWegMFgMBgMhrlhRNxgMBgM\n", + "hkWKEXGDwWAwGBYpRsQNBoPBYFikGBE3GAwGg2GRYkTcYDAYDIZFij3fAw4ODlrAfwU2Axr4X4eG\n", + "ht6a73kYDAaDwbDYWQhL/D5ADQ0N3QL8R+CPF2AOBoPBYDAseuZdxIeGhr4P/MbUw7VAZr7nYDAY\n", + "DAbDUmDe3ekAQ0ND/uDg4N8DHwU+vhBzMBgMBoNhsSMWsuzq4OBgP/A8sHVoaKhypn201loIMb8T\n", + "MxgMBoNh4WhY9BYise1XgJVDQ0N/ClQANbWdESEEY2OF+ZreRUlvb9J8xy3GfMetx3zH84P5nltP\n", + "b2+y4X0Xwp3+HeDvBwcHnwAc4HNDQ0O1BZiHwWAwGAyLmnkX8Sm3+Sfme1yDYT7wtY9C4SkXH5/p\n", + "YFXIVRS8EgiwsZBYWNJCIpHClGswGAxzY0ES2wyGxYyvfaqqgqtdfO3hTW1aq0C0hUYg3iXOdt2l\n", + "oEoAaK3RKFSwK0JILGFhYQW3wiEsQoRlxAi8wWA4J0bEDYbz4CqXsirh6jp1VcfXPlIITk24FCIQ\n", + 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\"BOLD signal\", color=\"muted\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Advanced Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# sequential palette with linear increase in brightness\n", - "sb.palplot(sb.cubehelix_palette(8))" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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RK8oyZwVxK4lWrTEwIjLKbR138ZGuT9Bj97G3+jbfHP8fvFl6taXWshBQ9HMM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\nx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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, + "output_type": "display_data" + } + ], + "source": [ + "# facetting histograms by subsets of data\n", + "sb.set(style=\"darkgrid\")\n", + "\n", + "tips = sb.load_dataset(\"tips\")\n", + "g = sb.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n", + "bins = np.linspace(0, 60, 13)\n", + "g.map(plt.hist, \"total_bill\", color=\"steelblue\", bins=bins, lw=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfAAAAHwCAYAAABZrD3mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlwJOd9Hv6nu+fo6bkADIDBvSf2IpekSFGkJJoSaSuy\n", + "ypQUW2YkRrGkKKlIqYpVsVOJk0ri0q9+tqOKIruKVZZjWZaP2LHssmzJkiXa1uW1SYpcitRyL1yL\n", + "+wYGwFw9d3f+GDQWC2J3cczM293zfP6RdjHd810QwIN+3+/7vpJpmiAiIiJnkUUXQERERPvHACci\n", + 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"sb.distplot(d, kde=False, color=b, ax=axes[0, 0])\n", + "sb.distplot(d, hist=False, rug=True, color=r, ax=axes[0, 1])\n", + "sb.distplot(d, hist=False, color=g, kde_kws={\"shade\": True}, ax=axes[1, 0])\n", + "sb.distplot(d, color=p, ax=axes[1, 1])\n", + "\n", + "plt.setp(axes, yticks=[])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# facetting histograms by subsets of data\n", - "sb.set(style=\"darkgrid\")\n", - "\n", - "tips = sb.load_dataset(\"tips\")\n", - "g = sb.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n", - "bins = np.linspace(0, 60, 13)\n", - "g.map(plt.hist, \"total_bill\", color=\"steelblue\", bins=bins, lw=0)" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 7 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# several distribution plot examples\n", - "sb.set(style=\"white\", palette=\"muted\")\n", - "f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)\n", - "sb.despine(left=True)\n", - "\n", - "rs = np.random.RandomState(10)\n", - "\n", - "b, g, r, p = sb.color_palette(\"muted\", 4)\n", - "\n", - "d = rs.normal(size=100)\n", - "\n", - "sb.distplot(d, kde=False, color=b, ax=axes[0, 0])\n", - "sb.distplot(d, hist=False, rug=True, color=r, ax=axes[0, 1])\n", - "sb.distplot(d, hist=False, color=g, kde_kws={\"shade\": True}, ax=axes[1, 0])\n", - "sb.distplot(d, color=p, ax=axes[1, 1])\n", - "\n", - "plt.setp(axes, yticks=[])\n", - "plt.tight_layout()" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAaYAAAGoCAYAAAANe0FzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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+87Xb71FaX0clnb7j63a+HsDWNfnp6Vtec8ufDeOWP1s1AoBZLCJ98WL1vQzj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\nbEtkXUOCa9uguoYp1vYHnkLXdf0zwJff97/bgP9USp3QdR1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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, + "output_type": "display_data" + } + ], + "source": [ + "# hexbin plot with marginal distributions\n", + "from scipy.stats import kendalltau\n", + "sb.set(style=\"ticks\")\n", + "\n", + "rs = np.random.RandomState(11)\n", + "x = rs.gamma(2, size=1000)\n", + "y = -.5 * x + rs.normal(size=1000)\n", + "sb.jointplot(x, y, kind=\"hex\", stat_func=kendalltau, color=\"#4CB391\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# hexbin plot with marginal distributions\n", - "from scipy.stats import kendalltau\n", - "sb.set(style=\"ticks\")\n", - "\n", - "rs = np.random.RandomState(11)\n", - "x = rs.gamma(2, size=1000)\n", - "y = -.5 * x + rs.normal(size=1000)\n", - "sb.jointplot(x, y, kind=\"hex\", stat_func=kendalltau, color=\"#4CB391\")" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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6NZzAR3Uwruq5PFs4w1QH7w2g6jscKk5QdGsdjXshkmas4wq+hGGtWzlvEIWU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- "text": [ - "" - ] - } - ], - "prompt_number": 9 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# faceted logistic regression\n", - "sb.set(style=\"darkgrid\")\n", - "df = sb.load_dataset(\"titanic\")\n", - "\n", - "pal = dict(male=\"#6495ED\", female=\"#F08080\")\n", - "g = sb.lmplot(\"age\", \"survived\", col=\"sex\", hue=\"sex\", data=df,\n", - " palette=pal, y_jitter=.02, logistic=True)\n", - "g.set(xlim=(0, 80), ylim=(-.05, 1.05))" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAwYAAAFiCAYAAABMPM+xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcZNdd3/3PubX03j3TPT27JMuyfIQthCXLNtjGq4zZ\n", + "7UCIMWACZnuZxFmAh5A8IYbkRUiexBAeJywxEBYHGww2jwnBxnZshIUsr7JsyTrSjGY0mq2np3um\n", + "967l3vP8cW6tXb1Nd3VXdX/fr9dMd1XduvWrW9VVv989m/HeIyIiIiIi+1u02wGIiIiIiMjuU2Eg\n", + "IiIiIiIqDERERERERIWBiIiIiIigwkBERERERFBhICIiIiIiqDAQ2TBr7bdaa89aa/+wTft/lbX2\n", + 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VSdnrY07iUMClaz1QKoSjUDdFqUmoFUOFAubsU4393+vHBMRl/JGjtUR8ZART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\n7MI0k3a9iJtNB0g6S/eMwRf/xo/SOIZ79a72tt57SvHKDWV6RIH+HMw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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, + "output_type": "display_data" + } + ], + "source": [ + "# faceted logistic regression\n", + "sb.set(style=\"darkgrid\")\n", + "df = sb.load_dataset(\"titanic\")\n", + "\n", + "pal = dict(male=\"#6495ED\", female=\"#F08080\")\n", + "g = sb.lmplot(\"age\", \"survived\", col=\"sex\", hue=\"sex\", data=df,\n", + " palette=pal, y_jitter=.02, logistic=True)\n", + "g.set(xlim=(0, 80), ylim=(-.05, 1.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# linear regression with marginal distributions\n", - "sb.set(style=\"darkgrid\")\n", - "tips = sb.load_dataset(\"tips\")\n", - "color = sb.color_palette()[2]\n", - "g = sb.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\",\n", - " xlim=(0, 60), ylim=(0, 12), color=color, size=7)" - ], - "language": "python", + "data": { + "image/png": [ + 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8GIbJkiVL+fGP7wNKt9i//OUvKsCrRCEuImV38OAB7rzzdtLpJF/4wp9jmiZ3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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "display_data" + } + ], + "source": [ + "# linear regression with marginal distributions\n", + "sb.set(style=\"darkgrid\")\n", + "tips = sb.load_dataset(\"tips\")\n", + "color = sb.color_palette()[2]\n", + "g = sb.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\",\n", + " xlim=(0, 60), ylim=(0, 12), color=color, size=7)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# time series factor plot\n", - "sb.set(style=\"white\")\n", - "planets = sb.load_dataset(\"planets\")\n", - "years = np.arange(2000, 2015)\n", - "g = sb.factorplot(\"year\", data=planets, palette=\"BuPu\",\n", - " aspect=1.5, x_order=years)\n", - "g.set_xticklabels(step=2)" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 12 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# correlation matrix\n", - "sb.set(style=\"darkgrid\")\n", - "\n", - "rs = np.random.RandomState(33)\n", - "d = rs.normal(size=(100, 30))\n", - "\n", - "f, ax = plt.subplots(figsize=(9, 9))\n", - "cmap = sb.blend_palette([\"#00008B\", \"#6A5ACD\", \"#F0F8FF\",\n", - " \"#FFE6F8\", \"#C71585\", \"#8B0000\"], as_cmap=True)\n", - "sb.corrplot(d, annot=False, sig_stars=False,\n", - " diag_names=False, cmap=cmap, ax=ax)\n", - "f.tight_layout()" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAhUAAAFhCAYAAADHv1g0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAFvVJREFUeJzt3XuQpWWdH/DvjEODuDPAwMh1mEawH1HE8hLXwguyGNRN\n", + "Urgmu1sRdldrAbUs1iRLUFlviRh3N0BKLNdsySrqJmYLVskaoquiKS4VNBq8IPggq4c7zWVuDKA9\n", + "wOSPc0ZaMsP0zHnec/oMn08VRfd73nN+v+ednre/87y3JVu2bAkAwLCWjrsBAGD3IFQAAE0IFQBA\n", + 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"outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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6j4Tfc+OkFI/tOJT9xCG87pUTqZleR9/zQfPzqJleh//yaFAsgL1qIge+9WRw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\nTiIiIlIm7Z2dg/pXqQr2fJnZncACd19uZkcCVwN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- "text": [ - "" - ] - } - ], - "prompt_number": 13 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# pair plot example\n", - "sb.set(style=\"darkgrid\")\n", - "df = sb.load_dataset(\"iris\")\n", - "sb.pairplot(df, hue=\"species\", size=2.5)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAx8AAALJCAYAAADVtJ4jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VOW9+PHPJJOFkCEkZIAgAQTJk7CGsFmlAalLW+u+\n", + "QKDe1lJEbb33V9rbq/TXltvftdpL7b3a1gXELrcSwH2remtVQK2KrMrygKASJWAiAYaErDO/PyYz\n", + "zL5kzmzJ9/168SJnzjnPPDPznOfMM+d7vo/J4XAghBBCCCGEEPGWkewKCCGEEEIIIfoGGXwIIYQQ\n", + "QgghEkIGH0IIIYQQQoiEkMGHEEIIIYQQIiFk8CGEEEIIIYRICBl8CCGEEEIIIRLCnOgnVEplAA8D\n", + "ZYAdWKy11h7rfwAsAhq6H1qitd6X6HoKIYQQQgghjJXwwQdwMdBfaz1LKXUhcCdwrcf6KuAGrfW2\n", + "JNRNCCGEEEIIESfJCLs6DRQopUxAAdDus34qsEwptUkpdXvCayeEEEIIIYSIi2QMPt4EcoG9wEPA\n", + 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yxn31Abzv+XB0dXHy3Xe8lg+tXIWp8Yj/Y92/VnlyWAq8fg3LtgzxuuLhWY/O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HA+GPgtcJu19vWIw7uAse1rQerxT7m6N8NNTFo69tgWEZHsUp4kyQW6BpFclY07H3fin0r1Q2NM\nYO3HCqDYWrvCGHMH8Ar+9SArrbU1WWhj0vx7bHtS9m2DiIhknwYdkgt0DSK5KBtrPr4LfDfO8ReB\nFzPXIhERERERyQQlGRQRERERkYzQ4ENERERERDJCgw8REREREcmIrGy1KyKp87unn+HDP7wZ8/jB\npiaW//M9GWyRiIiISHQafIjkuLpPP2XKnj0xj69rbclga0RERERi07QrERERERHJCN35EMlxew7U\n8NGRQzGP76w/zvUZbI+IiIhILBp8iOS4ggkj2TgjdoKppu3HMtgaERERkdg07UpERERERDJCdz5E\npM9rampi7do1AHg8hXi9jR3KLFiwCCBYLpYFCxaRn5+f+kaKiIj0Ahp8iEifV139Kfet/TMFxQOj\nHj9Zf4Szzz4HIKlyY8aMTVtbRUREcpkGHyIiwDBzLiWlw6MeO354b6fLiYiISEda8yEiIiIiIhmR\ntTsfxpg5wD3W2osiHv8esAyobX9oubX2/Uy3T6S3aW5upr429q5Y9bVempubcbvdWalPREREer+s\nDD6MMX8LXAccj3J4BnC9tXZTZlsl0vsd2TCKk56yqMdOeA/BpdmtT0RERHq3bN35+BD4GvBolGMz\ngTuNMUOAl6y192S0ZSK9lNvtZtCIqrjrFTpzlyLV9YmIiEj3GGP+GthjrX09222JJSuDD2vtM8aY\nM2IcXg38AvACzxpjvmqtfSljjRPJMd6jxzj26cGYxxv2HDn189HPY5cLOZZoOlVn68tkuWw+d6J2\niYiIpJO19r+y3YZEHD6fLytP3D74WG2tPSfi8f7W2mPtP98KDLLW/nMWmigiIiIiklbGmHnA3YAP\neAM4B9gJTAPeB74BDAJWAh78X9DfABwDHgLGAw7gr4Al7ec+BzwMDANagG8CjcAa/BtOHQYWWWs7\nJrZKsx6125UxZgCwzRhTbIxxAPOBDVluloiIiIhIulwO/Ie19jzgI/wDiWettefiH5B8BfgB8Li1\ndj7+ZQt/i38JQ0P7F/m3AtPbywPcDGxp39jpTuAeYBb+gcl8YAUQPWlVmmU7z4cPwBizGCix1q4w\nxnwfeB04CbxqrX05mw0UEREREUmju4G/N8Z8E1iH/+bAG+3H/gKMxX9345z2WUEu/OunR7eXp32j\npk3GmH9sPy9QPrD1SzPw6/bHfwMcCJybaVmbdiUiIiIi0tcZY27H/4X7TmPM8/inXV1urf2LMWYl\nsBa4BHjFWvuyMWY2MBL/dKoLrbV/057C4rL2x3YBQ4CT1tr7jTFjgC/gH7CUWWufMsbc0X78F5l+\nvRp8iIiIiIhkiTHmPOCn+Ndy7AVGAfuB4cC71trbjTEV+Ndw9AfcwI2ABR4AxuGfTbQMuB7/1Krn\ngf8EhgLFwPfayz8JtAFNwDJr7b7MvMpTNPgQEREREekhjDGvA9dYaw9luy3p0KMWnIuIiIiISO+l\nOx8iIiIiIpIRuvMhIiIiIiIZocGHiIiIiIhkhAYfIiIiIiKSERp8iIiIiIhIRmjwISIiIiKS44wx\nk4wxF2S7HYlo8CEiIiIikjoF7+zYf9uft+37m/oTzQMy+LxfByZk8Pm6RFvtioiIiIikhnvt799/\ncdUru77U2ubjqnlnbrjigtFfLB/Y70hXKzTGjMOfrbwZ/42DJcC3gPOBPPzZ0d9u/9eIP8v5QOB/\nt/9+EH9G9HzgCcABFAK3WGu3GGPuBmYCg4At1tobu9rW/8fencdHXR36/3/NJCEhZCTEhD3IIjls\nsroisrjU3YoboLZVce9t+2vv/bbX9i528bb3er299Vq1KlatCu6IttXWBdwV2WU5ICAEZUkIxCEh\n+/z+mIXZMhmSmclM8n4+HjzI5HM+53Nmcj5nPmePh3o+REREREQSYMP2/bOfe3PLN5qaPXg8sHjZ\n5ydu2L7/1g5Gezbwke//fwcuBYZaa88AzgR+BtTgraD8j7V2OfAHYLa1diawDPgX4CSgEjgfb+Wl\nlzHGBVRZa7/hO36qMWZAB9MbU3YyI28PY4wTeAQoA1qAm6y1tnNTJSIiIiISm8PhaHI6HUdee3/X\n3MFoFwA/AV4DqoHVwBRjzNu+49nAUP8ljTHFwNfW2t2+370L3AX8GBgJvIy3F+VXwGGgnzHmaeAQ\nUADkdDC9MaVjz8c3gF7W2mnAL/B+WCIiIiIiaW300KKX55xdtiSvRxbZWQ6uPLvs/TMmDrq/g9F+\nE3jXWns28DxwPfCWtXYWcA7wHLAVb6O9E+8wq2OMMf19588ALDAT2G2tPRfv8/V/4O0FGWytvRpv\nD0pPvHWmpEm7ng+8NbDexhgH0Bto6OT0iIiIiIjEo3n2zOMvP760cE5zc0vexLK+C4HaDsb5KfC4\nMaYBb+XicuBaY8w7eHsqXrTWHjLGrADuBjYANwEvGmNagCrgOl9ci4wxt+GtA/wcWAf8qzHmLWAP\n8DEwENjRwTS3Ku0mnBtjsoE3gAF4J75cbK39sHNTJSIiIiIiHZWOlY+f4h129TNjzGDgLWCctTZq\nD4jH4/E4HEntHZLuJamZSflVEkh5VTKJ8qtkCmWkJEvHYVe9gK99Px/AO+klq7XADoeDigp3hy5Y\nUuLqUBydfb7SkJjz/XEkUyLya7hEvG/FmZlxJlMi82qi3n+6xZPIuLpDmpJJZaviTGScklzpWPm4\nG/ijMeZdvBWPO6y1hzs5TSIiIiIi0kFpV/mw1h4EZnd2OkREREREJLHScaldERERERHpglT5EBER\nERGRlFDlQ0RERESkizPGnGuMuekoz7nTGHNLItORdnM+REREREQyWO6Kr9bNb25p7jG2r/ljrx49\nqzs7QQDW2tfbcVrC9+RQ5UNEREREJDFyXtrw2pJn17/6jZaWFi4yZ19zQdmsc47N73OwvREaY14A\nfmetfccYcyJwJ97dyEfiHcX0L9baZcaYzwALNAD3Aff4fq4FrvD9M9baO4wx/wJ8E29d4AFr7UPG\nmH8E5gBNwDvW2n8OS8c9wOm+l09ba+81xjwGFOHdGPxC38JRMWnYlYiIiIhIAmyq+Hz24o2vf6O5\npRkPHl61b5y4qXLrrR2M9mHgO76frwdeAyqttTOAS4Hf+471An5hrZ2Ht2KxCJgBPAD0wdeLYYyZ\nBJwHnOz7V2aMGQdcCZxmrZ0KjDTGXOhPgDHmImCotfZUYBpwte8cD/Cmtfb0eCoeoMqHiIiIiEhC\nOByOJqcj6PHaAQ4czR2M9m/AycaYPngf/McAFxhj3gaeB7KMMcf6wlrf//8BDALexNvj0RgUXxnw\nibXWY61ttNb+EzAK+Mha60/ru8DYoHNG+X6HtbYJ+MiXDoDNR/NmVPkQEREREUkAUzzi5cvGnr8k\nNzuXLGcWl40+7/2pQ6bc35E4rbUtwHPAg8BLwEZgobV2Ft4ejmeBKl/wFt//1wKPWWvPBNYDNwdF\nuQmYbIxxGGNyjDGv4a1AnGKMyTLGOIDphFYqNuKt+GCMyQGmAlvCrhkXzfkQEREREUmM5ovN2ZcP\n7zNkTnNLc974/qMX4p1z0VF/BD4H/h/e+R4PG2OWAscAv7fWeowxwZPDPwEeMcbUAM14Kx8zAY+1\ndo2vwvE+3o6I+621a40xzwb97l1r7WJjzATfOX82xsw0xnwA9ACesdauMsbAUU5KV+VDRERERCRx\nmsb2LXsqkRFaa8uB3KBffSdKmOFBP38CnBYW5PGg478BfhN2/m+B34b97udBP/+/KNe8Pr53cISG\nXYmIiIiISEqkZc+HMeY7wHW+lz2BCUA/a+3XnZYoERERERHpkLSsfFhrH8fXNWSMuQ94RBUPERER\nEZHMltbDrnwbqYy11j7S2WkREREREZGOSevKB/BTvLs4SjfmcFfjcFd3djK6LX3+IiKSavru6boc\nHs9RrY6VMsaYQuA9a+24NoKm5xuQhNj71lK23uddHnvEP9xOvzNnJvuSjiTHn1H5tRM+f4mf8qpk\nEuVXiVsnf/ckO692e2k558NnOt5dGdtUUeHu0IVKSlwdiqOzz++qaXC4q9l63/14mr2bbW79/QNk\nDT0ej6t3Uq7vjyPZOprGcIl439HirNy266g+/3jizJT3nilxJlui0pyo959u8SQyru6QpmTLlPtW\nccZ2NN/9mVq2dpQx5lxgiLX24TjC9gP+zVr73VaOTwAusdb+MsHJbFU6Vz7KgK2dnQjpXI6cHAon\nTgCgesPGTk6NROPvFm9vpSSVMimtIiJdhcNdTR31hG5TEXoculTZnFu1/NP5nqbmHr3Hj/tjdq9e\nCR0/Zq19/SjC7gWiVjx8x9cAaxKRrnilbeXDWvvfnZ0G6VweV28GXzab8meeBaB0zlVdqWBKex5X\nb4bccjM7H/I2rAy5+aaIz79+5cfs/MND3uO33Ezu5FNSns54ZVJaRUS6irbK3mjH2/ruSXM5u55/\nccnOhc98w9PczKBLL7lmwEUXnJNbXHywvREaY14Afmetfce3GNObwP3Ag8CrQCXwF2AZcB/gBvYB\ndXjnTi+y1p5mjFkLLAXG4x2q+E1gMnCLtXaeMWY+cCuQBSyx1t5pjPkHYDbQy3ed2dbaxva+F0j/\nCefSDfknmTkq91D+zLN4mpvxNDdT/uxzOCr3dHbyupXcyadQdvc9lN19T8QXhsNdzc4/PBT4++x8\n6OFWJwc63NXUVVSmIsmtXj/etIqISGK0VvYG/4t2PNZ3T7r7esPG2buef/EbnqYm8Hj4cvGSE7/e\nuOnWDkb7MEd2NL8e74JMfv2Ac6y1d+OtjHzHWnsW3tFD4XOhXMDT1tqZwJfA+f4wxpgS4CfANGvt\nZKCHMcYFFAFnW2tPxdtpcVIH30v69nxI9xTcAnL8P/2ok1Mj0PFucPU4iIiIX1P5drb97v8AGP6D\n77UaLsN6O45wOJpwOoNf43A4mjsY69+Au40xfYBpwIqgY9uttU2+nwdYa/1j1N8F5kaJa5Xv/3Ig\nL+j3w4HPrLX1ANbanwIYYxqBhcaYQ8BgElB3UM+HpI26isqQFpBtD/yB0qvn4sjOxpGdTem8uXiK\n+3d2MsXHPyzL//eJ1jWeLj0O8aRVREQSK6LsnX8D2+9/MPCdsP2BPzDkxvldqmw+ZvSol0uvumKJ\nMy8PR3Y2g6+47P3iaaff35E4rbUtwHN4ezZeAoIrMy1BP5cbY0b7fj6tlehaWxluKzDKGNMDwBjz\njDFmOvBNa+1c4Pt46w0drjuo50PSVkttLT1PPJmysScAqOKRhrxd46OA9G+lyqS0djV3/ea3/Pmv\nf4sZ5sLzz+Pm+TekKEUikir+srfAlcehQ3V4Go9MF/A0NpI3eixld9/jfd01yubmQZdecnnB8SPm\neJqa8gonTlgI1CYg3j8CnwP/D5jFkUpEcGXiduBRXy9FA7ArSphggTistZXGmP8ElhljPMASYDlQ\nY4x5B+98j5XAgI6+EVU+JG3klRRHTjLr1Rt6RS+MuuDqGBkp1ucfz6R16fqas4/h2BNjD3ludOxL\nUWpEpLN4CqJ8JxR0ye+Ept7jxj6VyAitteUcWS7s8aBDU4N+Phm42FeR+CVQb63d4Q9jrR0WFN8d\nQect8/3u8bC4Ac5KzDs4QpUPSSvxtk5rHkHmCG71creyzGIqKM+IiKRetLJXvdBJsxf4m6/n4yBH\nJqmnFVU+JGXi7alo63jwPAKAnQ897C3IMmBjoO4i/G/tcfUmr8SFO8GbQR1NeqLlGX3xiYh0XGvf\n7yp7U8ta+wLwQmenoy2qfEhKqNW5+9DfWkSk+1CZL0dLq11J0iV6xSOtXJS+0mV1q3DKMyIiiddW\nma+yV6JRz4dkJI0ZlaOlPCMiknrpMu9P0od6PiSp/C0gQ267NaLlw7+7aTxxRAvncfXWQ2QaCP77\ntNbKlYodzuPJT8ozIiKJE6vMD+8BySspPqq42yrTU/G9IsmRlj0fxpg7gIuBHOA+39JfkmHCx4GW\n/fc94PEWQvGOEdVY0vQW7e8T3sOQir+h8omISOdIRpnfVhwq8zNb2vV8GGNmAomjdMIAACAASURB\nVKdZa6cCM/Fu9y4JEG9PQ6KuVf7oHymcOIHCiRMo/+NjgYpHvPMC0nX+gHjF+vv4exiO5m8d/vuj\n6RlTPhER6XyJKI/bikNlfuZLx56PbwDrjDGLgWPw7uQoHZSIVoJ4l8p1uKuhoY7iaadT8fZSAEpm\nzQTHUV9SuoH61cv5+uOPATjmlFPInXiSWrVERDJEcHk9/Affi+uctp4nHDk5FE6cAED1ho0JSKWk\nk7Tr+QBKgCnAFcCtQEJ3iOyOEtFKUL/yYzb/04/Y/E8/on7lx22Gq3jpRSreXhq4ZsXSZeDxhol3\n9QutkpHe4vn7eFy9Kb16XiBM6by5IWEch6o5vGkTB1eu4uDKVRzetAnH/r1HlV+VT0REOkf488X2\nB/7AkBvnxyyP23qe8Lh6M/jKKzi4eg0HV69h8BWXh8bh8DZo+q9RMnOGGjczTDr2fFQCG621TcBm\nY0ydMabYWtvqrKKSBGwu19E4Ovv8WHHUUR/xuwJXHnm+8HUVldRV1FMSZTJYXUUlje5DbAnbJGjy\ngxMjJo/VVVQeKYQaGyNaLoKvyblnUzx5IkBIPBHvoZVwrUnE55hsyUhjIuOMlR8itPH3qauoZNuf\n/8Jx3/kWALsWv8yEGWcEwrrd+wOVVICKpcvoe/ZZEfGE5J0wJSWuo84nbUn3v1GqpDLN+fk94rpe\notKUyPemNKWHTLlvMy1O/6TuqGV82POFp7GR4pOmUHzSlKjnuKiP+jzhl1dSTF1FJVueXhgIU75w\nEZODvjfqqGfLe+9TOGE8AJXvf0Dp3Kta/Y6Q9JOOlY/3gB8A/2OMGQj0AvbHOqGig7sml5S4OhRH\nZ5/fdhy5DLnlZnY+9DAAQ26+CTe5uCvcMYe3+I8VTp4UEeMhdx1uQq8XfNtXb9zEgIsu5KuXFgNQ\nevW8wDWD0wUEftf6ewgN15pEfY7J1tE0hkvE+/Zr33Cn1v8+jpo6+p05ix2PPQHAwNmXcqjmSN5x\nRCmCGnN7tZpfw4W+9/jySVsS+XkmO85kS3SaY6mtbWjzeon6HBP591Ca4o8r2TLlvs2kONv+Tsil\n9Op5lC9cBEDpvLkhy+kGl8clJS4OuetCznbk5FC5fAU7H1kQuEbeyFER6Ql95sil9Ibr4/qOaI9M\nqnBnqrQbdmWt/TOwyhjzCbAEuN1a6+nkZGU872oU91B29z2BwiPWcCyHu5pdf3qSgZdcRP6QUvqe\nc3ZINyoOIobC5JUUB4a/9B43hq9eWhyIu3zhIk0IS3NJmcR3+HBIPvhq8ctw+HDgcGtDpnInn0LZ\nnf9O2Z3/rvkeIiKdIJ7vBIe7ml3PPU/hhPEUThjPrudfaHuY7G230ufkE+lz8okMu/1Wdj6yIOQa\nOGhzKG3u5FMo+9WvOOHu/9R3RAZKx54PrLU/6ew0dEVHNQ7eCf3P/Uag52LQZZdSdtdd0COPuq2b\n2fyPPwIiW0ICS+7VH+bgilUJTb90UU4nhZMmBn4GqFv2BuVPLwS8vWZ5M87urNSJiEgMnsZGDqxY\nCYAjO47HypaWwPPBMZMm48jJCQyxCojyvRBMi5JktrTr+ZCOibU8qaNyD47KPYGfqT/MkO/eHr11\nIazF+suXXgaPtwNq5wMPxmwJ8bh64ynur0nAGSbezaLCBeerwO9853iK+0dOOC/uHzjuOFTNzgce\n5MDyTzmw/FN2PvgHHHvKKfeN9w30mlXuSelS0SIi3UWsjXzjWVTkaL7rI5bhf+JPDPvubYGekCG3\n3gIeIr8XaqoD3zVaajfzpWXPh7RPrJaAkJbkOVey68XFeBobKb16HmX33ENBr7yQcZqJEL7xkKQ/\n/9+swOXND221LkXroQg/J2/G2ZSNHUdubg51rmNDj984P3qrV5imPV+x7f9+32o6RETk6LVVxsf1\nPd5GL0UIByHL8Pc9+yyaDtUc6Qk58aSIlauc+fkc/vQTyp/2zisZduvNcb47SVfq+egiQlaaCp+/\nUbknpCV510svM+D8cymcOIFdz78A7q8jI8zrGWUpO0dcYzGD+Tebk8zhcfUmr6S47Y2ewvJV+cJF\nOPaUR55TE2NzqAWPMuz2W0PzU//SkN6SIddezfYH1colIpJI8fYgxPoed7ir2fX4E+QPHkT+4EHs\neuJPsctnDyHL8DccqIqc8+EJfc4YftstlD+9KBDmi0cfY8gN12tkRQZTz0c348jJofj0qXy15FXA\nu1Z2zZrVbP73xaGtHh6oDFvKrrG6moMrVzHktlspu/sebzDd8BJDPC1W2aXDIvKTv7fE+6Innj9p\nux8RkbQTNj904OxLE9KsHdzjQv3hkGOexkbyjh9J2d33BHrpJbOo56OLCF5pypGd7R03CRHj7gsn\nTYjY/K9215cRrR4eV29Kb7ieg2vWcnDNWopPn8rB1Wu8LdwLHo0oDKTraWssb9T5HP1LY7ZYHVy5\nkpIzZx3pUZs1ExzRW9Y8xf29/wpCV0cZcustqvSKiHRQQjZobWNFwwgO71Arf3meW1zS5qaEUb9r\nju0X6KWXzJO0ng9jTBEwFyjmyAg+j7X2F8m6ZncXaClwQN3mTWz+pyMrUgVakpsaI1ah8jQ0xI6v\n/jBb7rwzsHFg8elT2fyznwXi1vj7rqut8b7BPRSe4v4R50SrpDqCxgc72hof7Be8OsqJJx3VexAR\nkeg6Y26mJ6g8LzlzFnljxkb0frc2dxCOfNdI5kpmz8diYFbYNRythJUE8bh6e1eKiDKO01PcP6Jl\nunTeXKo3bIy5koWnuD+lvvGV4T0nGn/f9YX3SoSvjOLvoYh2TniLVeHkyex7483AKib73nwLPG2s\n0qaVTUREkqYjczPbWtEw8gSoeOvtI6Mv3l4KLaFpaK3Mj/ZdI5kpmXM++lhrpycxfmknT03NkZUp\nsrMZ+R+/aXPcpPbvEGjf2urBq13VRylymsq3s+13/3dUcYqISHoI75XQHhzSlmT2fHxmjDkxifFL\nK2KN43RU7qH8yacCLc+7Fj0DNVFWu/IJbr3Q/h3dWyJ6ICLy5vwb2H5/2/vGKM+JiKQvf69E1O+J\nQ97niLqKyvjKcwdR5wZK15Hwng9jzHbfjz2Bq4wxXwFNvt95rLXDE31NiRTPOM7A/I1/vxOIbKFo\nrfVC+3fI0Yi2F0hgTojDu3JJW5TnREQyjyMnh7qN69n5yALgyLNEm88n7ZkbKBkjGcOuZvn+9xBZ\nV/Uk4XrdgmNPOQCe/qUhPREANS1uHLWNQE7IOeE3tcNdDbk9Kb32Gsqfejpk/gbAzocepuzuUYFd\nrf2tF+HHpHvyuLyrTn39yccAHHPyKd684tvdPNpYXEflHnY99zyFEycAsOv5Fxg5dhzk9vSeU+Bt\nBdv50MMAMXs1lPckkzU0NFBevqPV4wcOFFBVdYjS0uPo0aNHClMmEsrhrqaOevANxa5pcQPQy+mK\neQ5Efk8UnXEG2/733qN7lvDAvjfeDJzjyM6m8LwLI559JHMlvPJhrf0CwBjzgrX28uBjxpg3gbPa\nisMYsxLwj73YZq2dn+h0ZgKHuxqccPiTjyhf9CyOnBwGX3Yp5c88B3hbED4fkc+Gz1cAMGrEZE4o\nHB81rvrVy/n6Y99D4ymnUHbXXdDY0K75GxrPmTnaU1g7Kvfgdu8H17ERx5qqqwJ5ptcoQ927b1H+\npHcPDn+vRmhkjpDdbEtmzaTlYBWf+1Y2GXLLzZCdHf/uuCIZqrx8Bx/88PsMyM+Penw7sLu2lqm/\nvZcRI0amNnEiPuHf75uH9+ThVU8DMH/iPCb0mRBRGQk/x1N3ONDU7GmoT0i6mnZ9wbb/vTdwDT13\nZLZkDLt6CZgIDAwaguW/1s44zs8DsNbOaitsV+a/mQdeeglfLV6Cp7mZwokTKH/muZAWhKIb5zLx\n4XcBaJiTR+3MYeSHtU44DlVzeNMmDq70PjTm9O5N3vFlkJtHyayZVCxdBuDbxdx7jsfVm2PnX8P+\nR72FzrE3XK0ekQzTnkpitCFSfs2V5Xz19LOBv/2XTz9D4aSJgdflCxdRNnZcaA9Ij7yQ3rWKpcto\nOlwbkn8KJ03kwPJPATi4ajVld5cpP0mXNCA/nyEFrbcei3SmaN/vm2+ZQbOnBYAFaxZx+4m53L/8\nMcBbGZmYPTTinNK5VwWeN3oUFVE696pAo+nAS7/Z9mxjBxHPJgfe/0DPHV1IMpoZr8M79Op1YKbv\n51nAacCMOM6fAOQbY143xrxpjOlW1VuHu9o7KfzRP3pvtJaWmOGzV2wKTOzKfWkZuVUHIycB1x2O\n2FgQX8uEfxfzwgnjqXz/g0BrRU2Lm7sOv8WKG09nxY2nc1fd24HWDkl/7Zkc7qjcQ/nTCwPnlC9c\nFBhS1V711EX8ztPQ9hwPERFJsSj7Mg3PLQl5/UH5cpo9LTR7WliwZlHUMt5tNwe+R/a9+RYtTU0M\nvPhCBl58IXv+9vfYmxBC1GcTf8VDuoZkDLuqBqqNMfcAxwUd8gD9jTGfW2sPxoiiBrjbWrvAGDMS\n+Ksxpsxa2+pTeElJx1uSOhpHIs7f+9ZStt53v/f1rJlULHuH3a//nYGzL+WrxS9TvWEjpXOupPzZ\n5wEYdvONfPHoY8CRyePbfnYnACP+4Xb6nTkTwDd2M1RBcR/ySoppvuUmtv7+Ae85372N4uGDvfHV\nNtLY0sS7X28EIMuZRYErj+J+RVTdeC2VC7xDbYrnXxM4J1GfQ2eenwrJSGN4nFH/5q488mJc2+3e\nH/G73NwcXP5zSsZQP+dKvvTlv8FzrsThdHJw1WoASudcSfHo0OEilb0aOXbOpex/9mUAiq+6FGfh\nMTh85xTPv4be+YWBOILzYLxS8Xmma5zJlso05+f3iOt6iUpTIt9bPHEdOFDA9jZDQVFRQVp8pyU6\nnlTIlPs2XeOsoz6ix+GYfsPI2pcFwPzJc3hi1fMh5+T2D+3ZGHLNXMqfCQ3j7NGD8udf9MY5a6bv\n+SNGektcEc8mBH3XhH9PZFIeFS+Hx5OcOeDGmDeAk4A3fb+aCewAjgH+1Vr7dCvn9QCc1to63+uP\ngcustV+2cilPRUXHWuRLSlx0JI5EnF+5bReb/+lHoROsJk/k4MrVDLn9NvL69gW8E84b3HsB6OHq\n553L8cnH5JeWsvvPf6Wlri5wftmvfgW5Pal3NlD76ush4+7zL/wGPQq8cTrc1VH3+VhzYA0L1iwC\nYP6EuYzsPZy6ljr+68PfM7Gnt165vHYbv5j2Y3o5XWnxOSYgLyR7Qb8O59dwrb3v+pUfh0zkbmvY\nVW2Lm7qlSwMVhWOvupS8mTMCw/gc7mq23PETeo8dA0D1ho30mTyJvL7elrE9b77N8T//ZcSGhJ//\n27/Q/+wzA2HWfedU6hq9laPltdv45bQfk11TC3jzdCLee0dkUJwZk1cfePxZlu8ujhlmSt99fPeG\nuTHDJOpzTOTfI964tm7dwvaf/XPMYVc7D7kZdtdvOjznI00/p4zJr34ZVBZ0KE7/qIbGlgZ2v/cm\nxZv3AVBZ1o8B084kx+ldAKGX0xXxXDCxuR+f33VXoIzf+867DLrsMnb+8TEAhsy/gfLHnwh9Nrn7\nnriGTIXPWYw2hzFDy9ZuL5mbDDqAE6y1OwGMMQOBx/BWQpYCUSsfwPXAeOC7vnOOAXYnMZ1pq+9l\nV9B33re8+2v4frfmwBoWrPaOyb9p0tWUtbRwcMUqDq5YFegt8S9duu+F5zm4chWlN17P/o8+pnCC\ndzJ65XvvM+SCcwLX8bh6k1fiwh12A0/oM4FfT/eujLzt6+3887K7AJg+9BTe27GcxpYmspxZyfwI\npAOOdnlaD/BfOSu48nvedSIWHPiEfw0bKelpbOTAipUAOHv2JLekhK+WvAoQfS12Bxx7yskhYVpa\nmgM9annZuWw6uIXH1jwLHJnQKCIiyRf8TPHtCVfwfNYGJk72Ny6u5WfMDFnlKvi5oJfTBfv3RpTx\neSPLKPMtKhLvcurRhH9vaY5H15HMpWUG+SseANbar4ABvmFZsSwAjjHGvAMsAq6PNeSqq4i68U5x\n/5CbrabFzYLVCwPjLTdtWxkyrr9i6TIKJ03wbsozcwYHV6/xjt1f8BgD5l/LwTVrObhmLUXfuvKo\nWpjrW+p4bM2zgeu+s+MTxvcfTZYzi/kT5tLL6aKmxU1lbVUyPhrpAI+rd9wFdi+ni3njZvN4xTs8\nXvEO88ZeGvKlE55Hh952E7tfeTUk/zV4vK1bNS1ub2uah4j5RqcNnsKUgScwZeAJ3Dzl2pC8tWDN\nokArXCAOERFpl1jlaPgzxRNrX+CaCZdRW5BNbUE2V4y5kKLskog4ejldge+GZk9jRBnf3NIQ+O7x\nL6euTWIlWDJ7Pt43xjwNPAVkAXOBD4wxFwKHWjvJWtsEfCuJ6UpbidhIbde042Ha8TjuXxzS2lDd\nz0XlP14FQEPfEoriiCu4RSS4twPg0uPPY07ZpUe6YX3h1HKd2fytWgWuPDw1ORHHg/NoQ7TJ5ITm\nm5+MibyVW1o8rN69HoCJfceS48ymubkhJIzylIhIxxxtOZrjzKaxqSlQPk/pN551B9dGLLUbzBNl\n+7bw3/m/N6IN75buKZk9H7cCHwI3410B613gu3ifT7pl5SIesVqqezldzJ84jyxnFlnOLEYNnxzS\notB41Tmsdu5ntXM/DZfPCvx+0M038N8bnubJ8jd4svwN/rDq6ZBWjOBeC38LR3iLSHhvR1F2SaDH\nIzhccMu1ZKZeThfF+a1XT/15tLFXPj2/c3kgn/X89mXU9swOyQ//Yxcx+Jb5gTCDb76B/9l05Phj\na5/j0tHnBvL09ONOoaGlXnlKRKQDWvtuDu7FaGipZ9aw0wI90ZeNPj+iJ3r57jUxy+Ks4lIGXn1V\noIwfOO9KsopLI9LjHd4de96XdB9J6/mw1jYaY54AXubISPCB1tq/JOua3UHEeMvJUHb3KBqyanl1\n3yes2v4hAIXDpnLef/6cfGcBNb2cNC5bEjU+f8tIjjOby8ZcwDOfecPdftJ1EWGDeztEPMB9zZ9w\n8T9eCcArez/ix5wUEc4O68mWm6YBUDOsJ6wNPb6taifj+40G4MPyFZxZenpS0y0i0h2V134ZskfH\nENcgPBDo6Rjk6h+1J7otvWacy3DfQiTRKh4i4ZJW+TDG/BT4Z6AKQvrghiXrmqnSnl2jg4XvDtra\nsdbChb/e37MBsrN4e/uHgc2A3v7iQ2YMPo0WpzPQYxK8QoUDqGqqCLSMjO9bxjOfLQmc//DKp7hu\nwlU8tva5wDlF2aHrffvTEh63KifpI1Zea43DXe1bqjd297gDOLl0Es98sRSAmUNPxYmTWcNO40Dd\n1wCYY4fz6OpnAvnq3TWW20/8Dvd/+jjgzS/ZTicf7/YuoXjd+Cspyi6Jmqfa815EkqmhoYHNmzdT\nVdXqSGIASkuPi3lcJNHCv5uvG38lD694MmTDwDun/YilQc8Nf97yFnPHXcIiXyPk2cOmMbT3oEDz\n8Un9J9DL6Yr6DKRKhxyNZM75uBEYYa2tSOI1Uq49u0YHizUGM/jYnHGX8OKGv9DY0hRzrOb7FR/w\nzGdLmNh/bMSxxVteY/We9YHzw1euinaOX2NLE6MKR/Lr6T8FYj/wtTVPQDpHe+ZNHG3+dgAT+43x\n/ezA4xvt629JG1V8fMQ5pfmDQvLVmgNrQsYYQ2QPn+aASDoqL9/BBz/8PgPy81sNs7u2lqm/vTeF\nqRLxCi5HHRCYsxlLfnbPQJl+bH4fmlpaAuXzSf0ndPgZSASSO+djB3AgifGnXHt2jQ4WsbLEuuep\naqqgsraK2rBjz6x/hdF9y2KOea9qqgj0Vqzdu5HpQ08NGjt/Mmv3bgycX+s7P3jlquBzNlZsYd4J\nlwbGft40cV5gf4d4tDVPQFIr2njf2rDxvuFay9+tneMB3t7+ISt2r2PF7nW8/cWHePAEWtKaPS1s\n2b+d6UNPiZjT4Ree74Pzun9FFc0rknQ2ID+fIQWuVv/FqpiIpFLw/I5ZQ08jz5kXMo/02vGX8ac1\nzwfKdLt/a8gckPAVNo/2GUjEL5k9H58D7xlj3oLAdssea+0vknjNjJHjzOa00inc+a53LezrJlzV\nrrGWfo0tTXxYvoKfTfs+OWRz1/u/C7Ry5DizQ/ZSCF656sPyFdw57Ucc27uQ1bs2HNUqF5I5wvNA\nvH/PXbW7+M9P/3RU5wRr9jTz4c4VIXM6Jg0Yx+8+fAToeL4X6UyNjY3srq2NGWZ3bS2DGxvJyVGv\nsKRWcI/xdROuwulwBr7jZw47DYDa5sOBno4+ecfE1Tsi0lHJ7Pn4EniNIxUPB5FbkGWUqHtxHMW8\nj+DVqsb3H807X3wUsurPTVOuDbRAzBl7MRsrtoTspRGuKLuEOeMuCZxz2ejz6Zc9gKLsEq6bcFWr\neym8s+MTLig7kykDTwiMsQciWpfbWuVC0lf4ymg3Tb6m1f00/MLz96CwlanCzwm/hn9e0E2Trg7k\nvRP7n8Dpx53E2r0bWbt3I6eVTuHDHZ+2mu+j5fVo19G8D0kXT4/P5oGTc1r99/T4ZLbxiUQX3mP8\n2NrnOHjYzfj+Yxjffwzv7/wUd8shFq5bHOjpWLByEVeMvTBQ1o4sGh5zhU3t2SHtlczVru40xhQA\nI4B1QL61NvasvAzQ0b04/GMw61vqAi0QfuFj4Scee0Lg59acXjKV0dNGkpuXQ6+mwsDvWzyx91LY\nVb2b1XvWB8bYS9cTPN43XsHrse9taWh1lbRo1/DnU3fjoUDeG108kk/KV4X0fIwtKQuJIzzfx3sd\nkc6Wk5NDyagBuAYWthrG/dVB9XpIp8txZtPPVcJfNr8JwPShp+IIaw+ubapjfNEYjp82FAcOBmQP\nBoi6wiZox3Fpv6T1fBhjzgJW411qdwDwhTHm3GRdL5WOZtfoaHo5XYEVfWK15ta31FHfErmRW/gY\n/KLsEob2KQ05Ht7iEdy6HD4fpKbFTXF+UUR6ThowQa3NGc4/b+Joeg/867Hnt3JOrN1uq5oqWOSb\nh9TsaWHhZy9z3aSrcDocOB0Orht/ZdR8FRxHW+9FRERi6+V0hfRC3zR5Hn/Z/GbQCIiPySYrZPTE\nnLEXU5h9LL2dhQzs3S8kruCyt6PPQCLJ7A/+NXAG8Bdr7ZfGmBnAQuD1JF4zo4SvEuUfn5mfncdF\no85hc+U2AEaXjGRC0Vg8eFeqas88DH/rcn1LXch8kGjpgSOty7+ePizktWSu9vQexLPqVFtL4DZ5\nmo+sfnXsCKaWTFW+EhFJgJoWN47aRiCydy14BMTUwVPIcWYzvq+353ljxRbgyOgJ8DZkamVBSYVk\nzvlwWmt3+19Ya9cTut+HcGSVqODeirNGnMGeQ/tYvWc9q/es50v3Hj7Ys5w7lt2FPbAVp8MZcx5G\na63c/h6Xb4+/stUW8PAWDrU2dy3t+Xu2turUE+ueZ2XVSu5Ydhd3LLuLNQfWkOfMi1jdasWX6wLn\nLFr/ClVNFcpXIiIdtObAGu5Ydhffe/VfWXNgTcix8PL606/WcX7Zmazds4G1ezZw/shZOH2PgEXZ\nJRRll2hlQUmZZPZ8lBtjLgYwxhQC3wV2xnuyMaYvsAI4y1q7OTlJ7HxVTRXUHDhIfUtj4He981y8\nYv8e2PjnnR0fc/OUqxnffwwflq9kXL9RrNr9GeCdwR/c8uEvKGK1cmv8fPcR3isRz0Z94S1p/nPC\nhwCO7VsWmMQORzatCl/dKnyORyNaTUVEpCOCKwrgLX9/PX14q2V734JjWbLpb4HwS+zfGVtSRuuz\nlUSSJ5mVj1uB3wGlwDbgLeDmeE40xuQAfwBqkpa6NODfIBDgmvGzmTnsNJZ+8RGb92+LCLt693rW\n7tnA9KGnUtNQ613BaOI8tlRvY8HqheQ4s7lszAWB+NrqLlWlo+sL7z53OhxtDtkLPuemSVfT4vEE\nXt8wcU4gjwKUHTssYtGEXGce3x5/Zciuuju+LifLmQXA9ONOweUsSMK7FRERv15OF3PGXcIz618B\noH9B34gwueRGnBO8K7rmekqyJHO1q73A3HaefjfwAHBH4lKUWm21MAdvEAjw1LrF3H7itzhY9zXN\nLS3MPeGbLPJVJGYNPY1lvmV539nxMXdO+xFXjLwYgDuW3UWzp4XxfctC4murFUS6tmitYhP7jYmZ\nP8LPWb7Hu/O4//Wja57leyddR0GPXgD069WP6UNP4Z0dnwDeioWDyJ61HGc2B/p/DYDpM/yoNq8U\nEZFIDoha/gYPk3pxw18CvdBPr32Ji8rO5tUt3tWuZg09jWa8ZXvw80r4XFSRZEh45cMYsz3GYY+1\nNuban8aY64AKa+3fjDF3EMfeICUlHX+Y6Wgcwecv2/4RDy73bsx260nfYsawUyPC1xw4GPE7p9MJ\nHu/GbEX5hfzfhb/gUH0N//bmf4dMED+2dyHF+UVU1lbFTFOBK4/i/PjfV7p9jp1xfiokI43hcXqH\nTcUWnj/iOedwcx2v2jcAmDf+Uj7dtSZkiNWV4y6kON9FCUfiPbPkdMYP8oYpzi9q+80cpVR8nuka\nZ7KlMs35+T3iul6i0pSIeA4ciK8Xr6jIGy7Wl2Nw2HQoixMdTypkyn2biDgraxtDhrh++uUaRh47\nlAUrvL0W3z/tBhpbmgJDtLOcWex27wuEf2/ncmYMPZUNh9ZHPK8Eyu/8DiczQrp+npJayej5mNVW\nAGPMFGvtilYOXw94jDFnAxOBx40x3/T1pERVUdGxCVElJa4OxRF8fk2LmweX/ynQWvzgp09yXP6Q\nyI3TKGTuuEtY5OsSnTfum/xh+ZPU+fbiWL13A7+e/lMcZHHakCkhrRs17jo8NW4gJ9BFurFiS0gX\n6/wJc/HU5FBRE9/76uhnkIg4Ovt8fxzJ1tE0hov+vnMius+dDger924IvPbnD3+rV3hLWp+83lw3\n4SoeW/scAN8efzmPr3oukLcXrnuZ26ZcywMrnoyIM1JOQv4+8b337hNnqDBU6QAAIABJREFUsiU6\nzbHU1ja0eb1EfY6JiqeqKr6tq+IN5w/b2WVxouPxx5VsmXLfJibOnJAhrref+B0eXvEk4/t7dyv/\naOfKkPJ85tBTGVDQj4WfvQzAFWMuIK+lZ6vPK+n93pMfpyRXwisf1tov4gj2CDCplfNn+H82xrwN\n3BKr4pHJXDkFTOznLSh6ZuVGDeOBiAm85x93VuB4eBfpxOltb0wo3UM8SycHz/G4bsJVIT0Z7+1c\nzrnTZgU2AGxoqY9Yorlfz75tbhAoIiKJF/z9X+Ou8zZUfvExAFeOvYjFG18LlOfv7/yUX077McOn\nHQdAv+wBWslKOk0yl9rtluLdzK2mxc1ja56lBQ8tePjTuhdDNgIMXh732+OvZO3ejazdu5Fvn3BF\nZC+Kb7le/896CBS/WEsn17S4eWLtc4zvP4bx/cfw1Gcvcd2kuSF5LT9oA8A+2cURG1IVZZcoz4mI\npIF3vvg4sEzu4k2vc80Jl0WU5/2yB9AvewAQ//OKSKIlc7WrDrPWtjmEKx3Fs5StA0JaKaYPPZUh\nvo0Aw8/T0riSDLHyYGuTDcM3pBIRkc4R3nOd48ym2Td0u7GliVGFI9vsmdbzhXQG9XwkSVutwR5C\nWyne2fFxzB0Y1bosidZaHgzuSYvGvyFVLDUtbnXpi4gkSfiGgI+tfS5i9ERwz3XweeFls54vJNXS\nuuejuymv/ZL7lz8GtL1Ph0i6Ct9fRPlYRCT5SlsZPeGnslnShXo+Oqimxd3mkrfRhI+1vG78lTy8\n4slAK8YT65737n6u1mOJob35D45uflK8+TC8NW7BmkXKwyIiCdZa+d1aL4bKZkknydjnYwa0PoLI\nWvsOcEWir9sZOtqKEL5ShX8loRxnNqeVTuHOd+9pd9zS9SWiFaut8b5qKRMRSU+1zYcDK2bWNh/u\n5NSIxC8ZPR8/b+Mf1tqtSbhuSiWqFcE/vj4/qBVjfP/RvOPb0VwtFBJNIluxEtlSptVTRESSr6qp\ngoXrFrNi9zpW7F7Hws9epqqpotXwKpslnSRjn4+ZiY6zu/C3Qte31LF69/rOTo5Iu2j1FBGR9KOy\nWdJF0iacG2POAP4f0AtvD0sWMMRaOzRZ10yVmhY3DuCmSVezfM8aAE7qPyEhN7O/FTp8d2oVFBKs\nvXnE33MRvvpJ+O86co1ocYmISOIUZZcw74RL2Vi5BYDRxSPjWv5cZbOkg2SudvUI8J/Ad4B7gQuA\nF5J4vZTwj4HPcWZz+ZgLAj0UU/qNT+h11EIhbQnf3b4t0eZvtDWnQ/lQRCQ9eTwtgWeQUceO6OTU\niMQvmatdHbbWPgosAw4AN5HhE82Dx8CP7lvGos+WJHVehtbelra0tSeHX7T5G1VNFXHN6VA+FBFJ\nL1VNFSHPIIvWvxJzzodIOklmz8dhY0wRYIFTgbcBbYksIiISQ0NDA+XlO9oMV1p6HD169EhBikRE\nEieZlY//AZ4FZgOfAtcCK9s6yRiTBTwMlOFdsvdWa21azL4OHgO/sWILc8ZdwjPrXwE0L0PSW7T5\nG0XZJZpbJJKGyst38MEPv8+A/PxWw+yurWXqb+9lxIiRKUyZpIui7JKQZ5A5Yy+Oa86HSDpIZuXj\nTeAFa22LMWYK3srEwTjOuwhosdZO8+0ZchdwaRLTeVTCx8BPnH5CYMx9axN3RdJBtPkb0X5X0+LG\nUdsItD2PRESSY0B+PkMK9F0irTu9ZCqjp40kNy+HXk2FQOsLiIikk2RsMliKdy7Jn4ELjDH+Q9XA\nX4BRsc631r5sjHnV93Io3vkiaSX4pvaOuXfxxq73tBmbpL1oX0jBv9OmgiIimaMou4SSPi4qKtwq\nvyVjJGPC+S+ApcBIvJPN/f9eA/4aTwTW2mZjzGN4V8l6OglpTKjK2qqEbfgm0lkSuXGhiIikjspv\nySTJ2GTwegBjzD9ba3/TgXiuM8b8BPjYGDPaWnu4tbAlJR3vXuxIHJW1VRG/K3DlUZwff5yd/R66\nShoS8R6SLRlpTESc3qFWoY42H7clXd97psaZbKlMc35+j7iul6g0JSKeAwcK4gpXVOQNtz0JYVt7\nH+n0OaVKpty3yYizwJUX9XcdKb8z5b1nUh4Vr2TO+fitMeZngAG+7/v3G2ttQ6yTjDHfAgZba38N\nHAZafP9aVVHRsdp9SYmrQ3GUlBRFTNz11ORQURNfnB29fiLi6AppSNR7SLaOpjFcIt63V06H8nFb\nEpdOxemPM9kSneZYamsb2rxeoj7HRMVTVXUooeHaEzba+0i3z8kfV7Jlyn2bjDg9NYktvzPpvWdi\n2drdJbPy8XugApgCNOEdhrUA+FYb5z0PPGaMWYZ3xusPrLX1SUxnQmgzNukKjnbjQhERSQ96DpFM\nkczKxxRr7SRjzHnW2kPGmG8Dn7V1km941ZwkpitpdLNLV+BfRCFRPR4iIpIaeg6RTJDMHc5bjDHB\nux8V08bwKRERERER6bqSWfn4HfAG0N8Y8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Y7k70TOi0rV/SwimSG8DPvu5RN4cPE6bQIs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\nA0acUq9uTu796LGAPq3ijRKJPxKEVGM1dg4dPcz4PkZy1+9bm6h11/HJ3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m9Ch8abRNNL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- "text": [ - "" - ] - } - ], - "prompt_number": 14 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "# pair plot example\n", + "sb.set(style=\"darkgrid\")\n", + "df = sb.load_dataset(\"iris\")\n", + "sb.pairplot(df, hue=\"species\", size=2.5)" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Statsmodels.ipynb b/Statsmodels.ipynb index 2d499ef..c3d5dbe 100644 --- a/Statsmodels.ipynb +++ b/Statsmodels.ipynb @@ -1,392 +1,779 @@ { - "metadata": { - "name": "", - "signature": "sha256:9d92688f5e6821e7233f04cf70c18344cd5c958c8cde264306a3a391a7cc28e4" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Statsmodels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Regression Models" + ] + }, { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", + "np.random.seed(9876789)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create some artificial data\n", + "nsample = 100\n", + "x = np.linspace(0, 10, 100)\n", + "X = np.column_stack((x, x**2))\n", + "beta = np.array([1, 0.1, 10])\n", + "e = np.random.normal(size=nsample)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add column of 1s for intercept\n", + "X = sm.add_constant(X)\n", + "y = np.dot(X, beta) + e" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Statsmodels" + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 1.000\n", + "Model: OLS Adj. R-squared: 1.000\n", + "Method: Least Squares F-statistic: 4.020e+06\n", + "Date: Sun, 16 Nov 2014 Prob (F-statistic): 2.83e-239\n", + "Time: 20:59:31 Log-Likelihood: -146.51\n", + "No. Observations: 100 AIC: 299.0\n", + "Df Residuals: 97 BIC: 306.8\n", + "Df Model: 2 \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "const 1.3423 0.313 4.292 0.000 0.722 1.963\n", + "x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247\n", + "x2 10.0103 0.014 715.745 0.000 9.982 10.038\n", + "==============================================================================\n", + "Omnibus: 2.042 Durbin-Watson: 2.274\n", + "Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875\n", + "Skew: 0.234 Prob(JB): 0.392\n", + "Kurtosis: 2.519 Cond. No. 144.\n", + "==============================================================================\n" ] - }, + } + ], + "source": [ + "# fit model and print the summary\n", + "model = sm.OLS(y, X)\n", + "results = model.fit()\n", + "print(results.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator." + "name": "stdout", + "output_type": "stream", + "text": [ + "('Parameters: ', array([ 1.34233516, -0.04024948, 10.01025357]))\n", + "('R2: ', 0.9999879365025871)\n" ] - }, + } + ], + "source": [ + "# individual results parameters can be accessed\n", + "print('Parameters: ', results.params)\n", + "print('R2: ', results.rsquared)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Linear Regression Models" + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.933\n", + "Model: OLS Adj. R-squared: 0.928\n", + "Method: Least Squares F-statistic: 211.8\n", + "Date: Sun, 16 Nov 2014 Prob (F-statistic): 6.30e-27\n", + "Time: 20:59:31 Log-Likelihood: -34.438\n", + "No. Observations: 50 AIC: 76.88\n", + "Df Residuals: 46 BIC: 84.52\n", + "Df Model: 3 \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "x1 0.4687 0.026 17.751 0.000 0.416 0.522\n", + "x2 0.4836 0.104 4.659 0.000 0.275 0.693\n", + "x3 -0.0174 0.002 -7.507 0.000 -0.022 -0.013\n", + "const 5.2058 0.171 30.405 0.000 4.861 5.550\n", + "==============================================================================\n", + "Omnibus: 0.655 Durbin-Watson: 2.896\n", + "Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.360\n", + "Skew: 0.207 Prob(JB): 0.835\n", + "Kurtosis: 3.026 Cond. No. 221.\n", + "==============================================================================\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# needed to display the graphs\n", - "%matplotlib inline\n", - "from pylab import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import statsmodels.api as sm\n", - "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", - "np.random.seed(9876789)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# create some artificial data\n", - "nsample = 100\n", - "x = np.linspace(0, 10, 100)\n", - "X = np.column_stack((x, x**2))\n", - "beta = np.array([1, 0.1, 10])\n", - "e = np.random.normal(size=nsample)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add column of 1s for intercept\n", - "X = sm.add_constant(X)\n", - "y = np.dot(X, beta) + e" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# fit model and print the summary\n", - "model = sm.OLS(y, X)\n", - "results = model.fit()\n", - "print(results.summary())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 1.000\n", - "Model: OLS Adj. R-squared: 1.000\n", - "Method: Least Squares F-statistic: 4.020e+06\n", - "Date: Sun, 16 Nov 2014 Prob (F-statistic): 2.83e-239\n", - "Time: 20:59:31 Log-Likelihood: -146.51\n", - "No. Observations: 100 AIC: 299.0\n", - "Df Residuals: 97 BIC: 306.8\n", - "Df Model: 2 \n", - "==============================================================================\n", - " coef std err t P>|t| [95.0% Conf. Int.]\n", - "------------------------------------------------------------------------------\n", - "const 1.3423 0.313 4.292 0.000 0.722 1.963\n", - "x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247\n", - "x2 10.0103 0.014 715.745 0.000 9.982 10.038\n", - "==============================================================================\n", - "Omnibus: 2.042 Durbin-Watson: 2.274\n", - "Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875\n", - "Skew: 0.234 Prob(JB): 0.392\n", - "Kurtosis: 2.519 Cond. No. 144.\n", - "==============================================================================\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# individual results parameters can be accessed\n", - "print('Parameters: ', results.params)\n", - "print('R2: ', results.rsquared)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('Parameters: ', array([ 1.34233516, -0.04024948, 10.01025357]))\n", - "('R2: ', 0.9999879365025871)\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# example with non-linear relationship\n", - "nsample = 50\n", - "sig = 0.5\n", - "x = np.linspace(0, 20, nsample)\n", - "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n", - "beta = [0.5, 0.5, -0.02, 5.]\n", - "\n", - "y_true = np.dot(X, beta)\n", - "y = y_true + sig * np.random.normal(size=nsample)\n", - "\n", - "res = sm.OLS(y, X).fit()\n", - "print(res.summary())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.933\n", - "Model: OLS Adj. R-squared: 0.928\n", - "Method: Least Squares F-statistic: 211.8\n", - "Date: Sun, 16 Nov 2014 Prob (F-statistic): 6.30e-27\n", - "Time: 20:59:31 Log-Likelihood: -34.438\n", - "No. Observations: 50 AIC: 76.88\n", - "Df Residuals: 46 BIC: 84.52\n", - "Df Model: 3 \n", - "==============================================================================\n", - " coef std err t P>|t| [95.0% Conf. Int.]\n", - "------------------------------------------------------------------------------\n", - "x1 0.4687 0.026 17.751 0.000 0.416 0.522\n", - "x2 0.4836 0.104 4.659 0.000 0.275 0.693\n", - "x3 -0.0174 0.002 -7.507 0.000 -0.022 -0.013\n", - "const 5.2058 0.171 30.405 0.000 4.861 5.550\n", - "==============================================================================\n", - "Omnibus: 0.655 Durbin-Watson: 2.896\n", - "Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.360\n", - "Skew: 0.207 Prob(JB): 0.835\n", - "Kurtosis: 3.026 Cond. No. 221.\n", - "==============================================================================\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# look at some quantities of interest\n", - "print('Parameters: ', res.params)\n", - "print('Standard errors: ', res.bse)\n", - "print('Predicted values: ', res.predict())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('Parameters: ', array([ 0.46872448, 0.48360119, -0.01740479, 5.20584496]))\n", - "('Standard errors: ', array([ 0.02640602, 0.10380518, 0.00231847, 0.17121765]))\n", - "('Predicted values: ', array([ 4.77072516, 5.22213464, 5.63620761, 5.98658823,\n", - " 6.25643234, 6.44117491, 6.54928009, 6.60085051,\n", - " 6.62432454, 6.6518039 , 6.71377946, 6.83412169,\n", - " 7.02615877, 7.29048685, 7.61487206, 7.97626054,\n", - " 8.34456611, 8.68761335, 8.97642389, 9.18997755,\n", - " 9.31866582, 9.36587056, 9.34740836, 9.28893189,\n", - " 9.22171529, 9.17751587, 9.1833565 , 9.25708583,\n", - " 9.40444579, 9.61812821, 9.87897556, 10.15912843,\n", - " 10.42660281, 10.65054491, 10.8063004 , 10.87946503,\n", - " 10.86825119, 10.78378163, 10.64826203, 10.49133265,\n", - " 10.34519853, 10.23933827, 10.19566084, 10.22490593,\n", - " 10.32487947, 10.48081414, 10.66779556, 10.85485568,\n", - " 11.01006072, 11.10575781]))\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# plot the true relationship vs. the prediction\n", - "prstd, iv_l, iv_u = wls_prediction_std(res)\n", - "\n", - "fig, ax = plt.subplots(figsize=(8,6))\n", - "\n", - "ax.plot(x, y, 'o', label=\"data\")\n", - "ax.plot(x, y_true, 'b-', label=\"True\")\n", - "ax.plot(x, res.fittedvalues, 'r--.', label=\"OLS\")\n", - "ax.plot(x, iv_u, 'r--')\n", - "ax.plot(x, iv_l, 'r--')\n", - "ax.legend(loc='best')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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+Mh3VSpXgh5w7uerPRdC+PSxdaubZFyyAHTvg+HGS6jUg7chxksr5klypCokf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\nskmzZhqAlVLKg3jkX+S8oeOpU8eSkVGOepYkZm9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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, + } + ], + "source": [ + "# example with non-linear relationship\n", + "nsample = 50\n", + "sig = 0.5\n", + "x = np.linspace(0, 20, nsample)\n", + "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n", + "beta = [0.5, 0.5, -0.02, 5.]\n", + "\n", + "y_true = np.dot(X, beta)\n", + "y = y_true + sig * np.random.normal(size=nsample)\n", + "\n", + "res = sm.OLS(y, X).fit()\n", + "print(res.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Time-Series Analysis" + "name": "stdout", + "output_type": "stream", + "text": [ + "('Parameters: ', array([ 0.46872448, 0.48360119, -0.01740479, 5.20584496]))\n", + "('Standard errors: ', array([ 0.02640602, 0.10380518, 0.00231847, 0.17121765]))\n", + "('Predicted values: ', array([ 4.77072516, 5.22213464, 5.63620761, 5.98658823,\n", + " 6.25643234, 6.44117491, 6.54928009, 6.60085051,\n", + " 6.62432454, 6.6518039 , 6.71377946, 6.83412169,\n", + " 7.02615877, 7.29048685, 7.61487206, 7.97626054,\n", + " 8.34456611, 8.68761335, 8.97642389, 9.18997755,\n", + " 9.31866582, 9.36587056, 9.34740836, 9.28893189,\n", + " 9.22171529, 9.17751587, 9.1833565 , 9.25708583,\n", + " 9.40444579, 9.61812821, 9.87897556, 10.15912843,\n", + " 10.42660281, 10.65054491, 10.8063004 , 10.87946503,\n", + " 10.86825119, 10.78378163, 10.64826203, 10.49133265,\n", + " 10.34519853, 10.23933827, 10.19566084, 10.22490593,\n", + " 10.32487947, 10.48081414, 10.66779556, 10.85485568,\n", + " 11.01006072, 11.10575781]))\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from statsmodels.tsa.arima_process import arma_generate_sample" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# generate some data\n", - "np.random.seed(12345)\n", - "arparams = np.array([.75, -.25])\n", - "maparams = np.array([.65, .35])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, + } + ], + "source": [ + "# look at some quantities of interest\n", + "print('Parameters: ', res.params)\n", + "print('Standard errors: ', res.bse)\n", + "print('Predicted values: ', res.predict())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# set parameters\n", - "arparams = np.r_[1, -arparams]\n", - "maparam = np.r_[1, maparams]\n", - "nobs = 250\n", - "y = arma_generate_sample(arparams, maparams, nobs)" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [], - "prompt_number": 12 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# add some dates information\n", - "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", - "y = pd.TimeSeries(y, index=dates)\n", - "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", - "arma_res = arma_mod.fit(trend='nc', disp=-1)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAeEAAAFrCAYAAADmYTGwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlGX3wPHv4I4a4G6mabhlmWlWrkEpoKllC5Zptkr2\n", + "lmhvb+WaltavzUqt1zTNViw18y1IRTNcstTMJVeUcs8dAVkFzu+PWxBk1GGYFc7nuuYCZp55nnsG\n", + "5cy9nWMREZRSSinlej7uboBSSilVVmkQVkoppdxEg7BSSinlJhqElVJKKTfRIKyUUkq5iQZhpZRS\n", + "yk3KO+vEFotF9z4ppZQqU0TEUpzjnRaEAXQPsvcaP34848ePd3czlB30d+fd9PfnvSyWYsVfQIej\n", + "lVJKKbfRIKyUUkq5iQZhZVVwcLC7m6DspL8776a/v7LF4qx5W4vFIjonrJRSqqywWCyetTDLGnsm\n", + "rssC/cCilFJlj8uDMGjAuZB+MFFKqbJJ54SVUkopN9EgrJRSSrmJBmGllFLKTTQIX8Kjjz7K2LFj\n", + "3d0MpZRSpZQG4UuwWCw2LZoKDg5m1qxZLmiRUkqp0kSD8GXYspJbVzcrpZSyh8cE4ZiYlYSFjSE4\n", + 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"metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from statsmodels.tsa.arima_process import arma_generate_sample" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# generate some data\n", + "np.random.seed(12345)\n", + "arparams = np.array([.75, -.25])\n", + "maparams = np.array([.65, .35])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set parameters\n", + "arparams = np.r_[1, -arparams]\n", + "maparam = np.r_[1, maparams]\n", + "nobs = 250\n", + "y = arma_generate_sample(arparams, maparams, nobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add some dates information\n", + "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", + "y = pd.TimeSeries(y, index=dates)\n", + "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", + "arma_res = arma_mod.fit(trend='nc', disp=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(arma_res.summary())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " ARMA Model Results \n", - "==============================================================================\n", - "Dep. Variable: y No. Observations: 250\n", - "Model: ARMA(2, 2) Log Likelihood -245.887\n", - "Method: css-mle S.D. of innovations 0.645\n", - "Date: Sun, 16 Nov 2014 AIC 501.773\n", - "Time: 20:59:32 BIC 519.381\n", - "Sample: 01-31-1980 HQIC 508.860\n", - " - 10-31-2000 \n", - "==============================================================================\n", - " coef std err z P>|z| [95.0% Conf. Int.]\n", - "------------------------------------------------------------------------------\n", - "ar.L1.y 0.8411 0.403 2.089 0.038 0.052 1.630\n", - "ar.L2.y -0.2693 0.247 -1.092 0.276 -0.753 0.214\n", - "ma.L1.y 0.5352 0.412 1.299 0.195 -0.273 1.343\n", - "ma.L2.y 0.0157 0.306 0.051 0.959 -0.585 0.616\n", - " Roots \n", - "=============================================================================\n", - " Real Imaginary Modulus Frequency\n", - "-----------------------------------------------------------------------------\n", - "AR.1 1.5618 -1.1289j 1.9271 -0.0996\n", - "AR.2 1.5618 +1.1289j 1.9271 0.0996\n", - "MA.1 -1.9835 +0.0000j 1.9835 0.5000\n", - "MA.2 -32.1812 +0.0000j 32.1812 0.5000\n", - "-----------------------------------------------------------------------------\n" - ] - } - ], - "prompt_number": 14 + "name": "stdout", + "output_type": "stream", + "text": [ + " ARMA Model Results \n", + "==============================================================================\n", + "Dep. Variable: y No. Observations: 250\n", + "Model: ARMA(2, 2) Log Likelihood -245.887\n", + "Method: css-mle S.D. of innovations 0.645\n", + "Date: Sun, 16 Nov 2014 AIC 501.773\n", + "Time: 20:59:32 BIC 519.381\n", + "Sample: 01-31-1980 HQIC 508.860\n", + " - 10-31-2000 \n", + "==============================================================================\n", + " coef std err z P>|z| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "ar.L1.y 0.8411 0.403 2.089 0.038 0.052 1.630\n", + "ar.L2.y -0.2693 0.247 -1.092 0.276 -0.753 0.214\n", + "ma.L1.y 0.5352 0.412 1.299 0.195 -0.273 1.343\n", + "ma.L2.y 0.0157 0.306 0.051 0.959 -0.585 0.616\n", + " Roots \n", + "=============================================================================\n", + " Real Imaginary Modulus Frequency\n", + "-----------------------------------------------------------------------------\n", + "AR.1 1.5618 -1.1289j 1.9271 -0.0996\n", + "AR.2 1.5618 +1.1289j 1.9271 0.0996\n", + "MA.1 -1.9835 +0.0000j 1.9835 0.5000\n", + "MA.2 -32.1812 +0.0000j 32.1812 0.5000\n", + "-----------------------------------------------------------------------------\n" + ] } ], - "metadata": {} + "source": [ + "print(arma_res.summary())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/TimeSeriesStockAnalysis.ipynb b/TimeSeriesStockAnalysis.ipynb new file mode 100644 index 0000000..484dd0f --- /dev/null +++ b/TimeSeriesStockAnalysis.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Simple Time Series Analysis of the S&P 500" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook presents some basic ideas from time series analysis applied to stock market data, specificially the daily closing value of the S&P 500 index from 1950 up to the present day.\n", + "\n", + "The first thing we need to do is import the data set. This was downloaded as a CSV file from Yahoo Finance and is available in the \"data\" folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "path = os.getcwd() + '\\stock_data.csv'\n", + "data = pd.read_csv(path)\n", + "data = data.set_index('Date')\n", + "data.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/data/stock_data.csv b/data/stock_data.csv new file mode 100644 index 0000000..9213e9d --- /dev/null +++ b/data/stock_data.csv @@ -0,0 +1,16468 @@ +Date,Open,High,Low,Close,Volume,Adj Close +2015-06-12,2107.429932,2107.429932,2091.330078,2094.110107,2719400000,2094.110107 +2015-06-11,2106.23999,2115.02002,2106.23999,2108.860107,3128600000,2108.860107 +2015-06-10,2081.120117,2108.50,2081.120117,2105.199951,3414320000,2105.199951 +2015-06-09,2079.070068,2085.620117,2072.139893,2080.149902,3034580000,2080.149902 +2015-06-08,2092.340088,2093.01001,2079.110107,2079.280029,2917150000,2079.280029 +2015-06-05,2095.090088,2100.98999,2085.669922,2092.830078,3243690000,2092.830078 +2015-06-04,2112.350098,2112.889893,2093.22998,2095.840088,3200050000,2095.840088 +2015-06-03,2110.639893,2121.919922,2109.610107,2114.070068,3099980000,2114.070068 +2015-06-02,2110.409912,2117.590088,2099.139893,2109.600098,3049350000,2109.600098 +2015-06-01,2108.639893,2119.149902,2102.540039,2111.72998,3011710000,2111.72998 +2015-05-29,2120.659912,2120.659912,2104.889893,2107.389893,3927390000,2107.389893 +2015-05-28,2122.27002,2122.27002,2112.860107,2120.790039,2980350000,2120.790039 +2015-05-27,2105.129883,2126.219971,2105.129883,2123.47998,3127960000,2123.47998 +2015-05-26,2125.340088,2125.340088,2099.179932,2104.199951,3342130000,2104.199951 +2015-05-22,2130.360107,2132.149902,2126.060059,2126.060059,2571860000,2126.060059 +2015-05-21,2125.550049,2134.280029,2122.949951,2130.820068,3070460000,2130.820068 +2015-05-20,2127.790039,2134.719971,2122.590088,2125.850098,3025880000,2125.850098 +2015-05-19,2129.449951,2133.02002,2124.50,2127.830078,3296030000,2127.830078 +2015-05-18,2121.300049,2131.780029,2120.01001,2129.199951,2888190000,2129.199951 +2015-05-15,2122.070068,2123.889893,2116.810059,2122.72998,3092080000,2122.72998 +2015-05-14,2100.429932,2121.449951,2100.429932,2121.100098,3225740000,2121.100098 +2015-05-13,2099.620117,2110.189941,2096.040039,2098.47998,3374260000,2098.47998 +2015-05-12,2102.870117,2105.060059,2085.570068,2099.120117,000,2099.120117 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