Appendix A - A Crash Course in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix A - A Crash Course in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython and Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic notebook operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = 'world'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + ' ' + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referencing output from previous cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Out[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referencing input from previous cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "In[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Types and Collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numeric Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternative numeric types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ser = pd.Series([100, 200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ser[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[0] + 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(u'hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytes"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b'hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Can't convert 'bytes' object to str implicitly",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-87055824beb7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;34mu'hello'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34mb'world'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly"
     ]
    }
   ],
   "source": [
    "u'hello' + b' world'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'helloworld'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u'hello' + b' world'.decode('utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Booleans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists and Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 100]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = ['a', 'b', 'c', 100]\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing from the end rather than the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 'c']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'c']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 100]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[1::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a', 'b', 'c', 100)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = ('a', 'b', 'c', 100)\n",
    "items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries and Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 100, 'key2': 200, 'key3': 400}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs = {'key1': 100, 'key2': 200, 'key3': 400}\n",
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 100, 'key2': 200, 'key3': 400}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs = dict(key1=100, key2=200, key3=400)\n",
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 100, 'key2': 200, 'key3': 400, 'key4': 1000, 'key5': 2000}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs['key4'] = 1000\n",
    "pairs['key5'] = 2000\n",
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs['key4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['key5', 'key2', 'key1', 'key4', 'key3'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([2000, 200, 100, 1000, 400])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100, 200, 400, 1000, 2000}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = {100, 200, 400, 2000, 1000}\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1', 'key2', 'key3'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = set(['key1', 'key2', 'key3'])\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1', 'key2', 'key3', 'key4', 'key5'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.add('key4')\n",
    "items.add('key5')\n",
    "items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow Control"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value is sufficient\n"
     ]
    }
   ],
   "source": [
    "if value >= 30:\n",
    "    print('value is sufficient')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if value >= 30:\n",
    "    print('value is sufficient')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value is insufficient\n"
     ]
    }
   ],
   "source": [
    "if value >= 30:\n",
    "    print('value is sufficient')\n",
    "else:\n",
    "    print('value is insufficient')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value = 26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting closer\n"
     ]
    }
   ],
   "source": [
    "if value >= 30:\n",
    "    print('value is sufficient')\n",
    "elif value > 25:\n",
    "    print('getting closer')\n",
    "elif value > 22:\n",
    "    print('not there yet')\n",
    "else:\n",
    "    print('value is insufficient')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## While Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a condition as the loop expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "while value < 5:\n",
    "    print(value)\n",
    "    value += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items = [100, 200, 300]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using an object as a loop condition.  When the list is empty, the loop ends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "200\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "while items:\n",
    "    print(items.pop())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over a range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(2, 6, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over items in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "items = [100, 200, 300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "for i in items:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pairs = {'key1': 100, 'key2': 200, 'key3': 300}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over keys in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key1\n",
      "key2\n",
      "key3\n"
     ]
    }
   ],
   "source": [
    "for key in pairs:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looping over key / value pairs in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key1 : 100\n",
      "key2 : 200\n",
      "key3 : 300\n"
     ]
    }
   ],
   "source": [
    "for key, value in pairs.items():\n",
    "    print('%s : %s' % (key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_hello():\n",
    "    print('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_hello(name):\n",
    "    print('hello %s' % name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin\n"
     ]
    }
   ],
   "source": [
    "print_hello('Kevin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_hello(name, excited=False):\n",
    "    if excited:\n",
    "        print('hello %s!' % name)\n",
    "    else:\n",
    "        print('hello %s.' % name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin.\n"
     ]
    }
   ],
   "source": [
    "print_hello('Kevin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin!\n"
     ]
    }
   ],
   "source": [
    "print_hello('Kevin', excited=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin!\n"
     ]
    }
   ],
   "source": [
    "print_hello('Kevin', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin!\n"
     ]
    }
   ],
   "source": [
    "print_hello(excited=True, name='Kevin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin.\n"
     ]
    }
   ],
   "source": [
    "print_hello('Kevin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(print_hello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = [print_hello]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.print_hello>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello Kevin.\n"
     ]
    }
   ],
   "source": [
    "a[0]('Kevin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classes and Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class BankAccount(object):\n",
    "\n",
    "    def __init__(self, name, amount=0):\n",
    "        self.name = name\n",
    "        self.balance = amount\n",
    "\n",
    "    def report(self):\n",
    "        print('%s : $%.2f' % (self.name, self.balance))\n",
    "\n",
    "    def deposit(self, amount):\n",
    "        self.balance += amount\n",
    "\n",
    "    def withdraw(self, amount):\n",
    "        self.balance -= amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ba = BankAccount('Kevin', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kevin : $1000.00\n"
     ]
    }
   ],
   "source": [
    "ba.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ba.deposit(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kevin : $1500.00\n"
     ]
    }
   ],
   "source": [
    "ba.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ba.withdraw(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kevin : $1300.00\n"
     ]
    }
   ],
   "source": [
    "ba.report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exceptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-86-a187b7beb4f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;36m100\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "100 / 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An error occurred\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    100 / 0\n",
    "except:\n",
    "    print('An error occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An error occurred\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    100 / 0\n",
    "except ZeroDivisionError:\n",
    "    print('An error occurred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Context Managers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "with open('/u/username/data/iris.csv') as f:\n",
    "    print(f.closed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.closed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Pandas Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ser = pd.Series([100, 200, 300, 1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     100\n",
       "1     200\n",
       "2     300\n",
       "3    1000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'name': pd.Series(['Joe', 'Chris', \n",
    "                                     'Mandy']),\n",
    "                   'age': pd.Series([18, 22, 19]),\n",
    "                   'height': pd.Series([70., 68., 64.])},\n",
    "                   columns=['name', 'age', 'height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Joe</td>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mandy</td>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age  height\n",
       "0    Joe   18    70.0\n",
       "1  Chris   22    68.0\n",
       "2  Mandy   19    64.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       object\n",
       "age         int64\n",
       "height    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.set_index('name', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandy</th>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Joe     18    70.0\n",
       "Chris   22    68.0\n",
       "Mandy   19    64.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       18.0\n",
       "height    70.0\n",
       "Name: Joe, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Joe']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandy</th>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Joe     18    70.0\n",
       "Chris   22    68.0\n",
       "Mandy   19    64.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Joe      18\n",
       "Chris    22\n",
       "Mandy    19\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select a range of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandy</th>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Chris   22    68.0\n",
       "Mandy   19    64.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select a range of rows based on row labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandy</th>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Chris   22    68.0\n",
       "Mandy   19    64.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Chris':'Mandy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select alternating rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandy</th>\n",
       "      <td>19</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Joe     18    70.0\n",
       "Mandy   19    64.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `loc` and `iloc` DataFrame Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       22.0\n",
       "height    68.0\n",
       "Name: Chris, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Chris']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height    68.0\n",
       "age       22.0\n",
       "Name: Chris, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Chris', ['height', 'age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>68.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       height  age\n",
       "name              \n",
       "Chris    68.0   22"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['Chris'], ['height','age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Joe     18    70.0\n",
       "Chris   22    68.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2, [0,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `at` and `iat` DataFrame Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at['Chris', 'age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `ix` DataFrame Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>70.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>68.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       height  age\n",
       "name              \n",
       "Joe      70.0   18\n",
       "Chris    68.0   22"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[:2, ['height', 'age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Boolean Masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = pd.Series([True, True, False])\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask.index = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joe</th>\n",
       "      <td>18</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Joe     18    70.0\n",
       "Chris   22    68.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Joe      False\n",
       "Chris     True\n",
       "Mandy    False\n",
       "Name: age, dtype: bool"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df['age'] > 20\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Chris   22    68.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Chris   22    68.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['age'] > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>22</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  height\n",
       "name              \n",
       "Chris   22    68.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.age > 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Plots and Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting from Pandas DataFrame Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plotdata = pd.DataFrame(np.random.rand(10, 3), columns=['A', 'B', 'C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a70255b38>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YnebnCQDGpxvjD6B5mp8PAPA2MxfZi+j4aCo4qSClpKbYZb2Ru0bSmD1j5E0SHU1UsCBR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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a512fe7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a702a3be0>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a701d6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting DataFrames using Plotly and Cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cufflinks as cf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "cf.go_offline()\n",
    "\n",
    "plotdata = pd.DataFrame(np.random.rand(10, 3), columns=['A', 'B', 'C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"424058e3-a8bd-4c57-92c7-9bffd490ee38\" style=\"height: 587; width: 1116px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"424058e3-a8bd-4c57-92c7-9bffd490ee38\", [{\"name\": \"A\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], \"text\": \"\", \"type\": \"bar\", \"orientation\": \"v\", \"marker\": {\"line\": {\"width\": 1, \"color\": \"rgba(255, 153, 51, 1.0)\"}, \"color\": \"rgba(255, 153, 51, 0.6)\"}, \"y\": [0.024404093005446104, 0.9027457935183334, 0.3633567358433484, 0.809324205827196, 0.22547789785815286, 0.10127860974240932, 0.7559318849420191, 0.36774211223564324, 0.9590053527147406, 0.533796168339199]}, {\"name\": \"B\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], \"text\": \"\", \"type\": \"bar\", \"orientation\": \"v\", \"marker\": {\"line\": {\"width\": 1, \"color\": \"rgba(55, 128, 191, 1.0)\"}, \"color\": \"rgba(55, 128, 191, 0.6)\"}, \"y\": [0.7431597143750966, 0.08057034120349049, 0.9027310091354875, 0.5148909782597069, 0.10473653091067059, 0.06879611716851941, 0.10988893663593235, 0.25933758362170467, 0.06256954998641184, 0.9374493709194965]}, {\"name\": \"C\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], \"text\": \"\", \"type\": \"bar\", \"orientation\": \"v\", \"marker\": {\"line\": {\"width\": 1, \"color\": \"rgba(50, 171, 96, 1.0)\"}, \"color\": \"rgba(50, 171, 96, 0.6)\"}, \"y\": [0.6007328850427528, 0.06671739469239546, 0.30854248553428376, 0.4450893910432421, 0.004084238309707944, 0.4518774375724449, 0.28486771063146055, 0.24569590122676155, 0.37414832379870067, 0.4138077235913177]}], {\"width\": 1116, \"titlefont\": {\"color\": \"#4D5663\"}, \"height\": 587, \"barmode\": \"stack\", \"yaxis1\": {\"titlefont\": {\"color\": \"#4D5663\"}, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": \"\", \"showgrid\": true, \"zerolinecolor\": \"#E1E5ED\", \"gridcolor\": \"#E1E5ED\"}, \"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}, \"traceorder\": \"normal\"}, \"plot_bgcolor\": \"#F5F6F9\", \"paper_bgcolor\": \"#F5F6F9\", \"xaxis1\": {\"titlefont\": {\"color\": \"#4D5663\"}, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": \"\", \"showgrid\": true, \"zerolinecolor\": \"#E1E5ED\", \"gridcolor\": \"#E1E5ED\"}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata.iplot(kind='bar', barmode='stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Graphics using Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe607db8780>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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60T0+ePZnSZLUc1W6x68B/gM4LCLui4gzOx+WJGk2tbZBm99/AEn1smwy+LpRNpEk9RGT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe60e08a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "width = 1\n",
    "height = 0.5\n",
    "\n",
    "p1 = plt.barh(plotdata.index + 0.2, plotdata['A'], height, color='b')\n",
    "\n",
    "p2 = plt.barh(plotdata.index + 0.2, plotdata['B'], height, color='g')\n",
    "\n",
    "p3 = plt.barh(plotdata.index + 0.2, plotdata['C'], height, color='r')\n",
    "\n",
    "plt.yticks(plotdata.index + width / 2., list(plotdata.index))\n",
    "plt.legend((p1[0], p2[0], p3[0]), ('A', 'B', 'C'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Visualization with Bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"068b486f-67b9-46a2-9e75-1dfab743a570\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#068b486f-67b9-46a2-9e75-1dfab743a570\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from bokeh.charts import Bar, Scatter, Line, show, output_notebook\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "plotdata = pd.DataFrame(np.random.rand(10, 3), columns=['A', 'B', 'C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"2aa02584-04a6-4dd3-892d-ad7f3bf50b29\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
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       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
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       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[145]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7f8a41087da0>"
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   "source": [
    "show(Line(plotdata))"
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   "cell_type": "code",
   "execution_count": 146,
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   "source": [
    "df = plotdata[['A', 'B', 'C']].unstack().reset_index(level=0)\n",
    "df = df.rename(columns={'level_0': 'name', 0: 'value'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
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    "show(Bar(df, label='index', values='value', stack='name', \n",
    "             legend='top_right', xlabel='index', ylabel='value'))"
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