awalsh15.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 8.3.0\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "import json\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "import bson\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "import pymongo\n",
    "print (\"IPython version:      %6.6s\", IPython.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.54548659 0.22825596 0.91090045]\n",
      " [0.16481767 0.19482107 0.43271051]]\n",
      "Add 5 to every element\n",
      "[[5.54548659 5.22825596 5.91090045]\n",
      " [5.16481767 5.19482107 5.43271051]]\n",
      "Get the first row\n",
      "[5.54548659 5.22825596 5.91090045]\n"
     ]
    }
   ],
   "source": [
    "#Here is what numpy can do\\n\",\n",
    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
    "x = np.random.random((2, 3))\n",
    "print (x)\n",
    "\n",
    "#array operation (as in R)\n",
    "print (\"Add 5 to every element\")\n",
    "x = x + 5\n",
    "print (x)\n",
    "\n",
    "# get a slice (first row) (as in R)\n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
    "#       on possible completions.\n",
    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
    "x.any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QcAdwW/dzPoOLJPcPtX1iaJ33MLhCfjfd1edp+Fmmls8CX+na/5rBRdZHnwQf62r5MjA36RpWU8+iPsPhPrX1LLNv/rwb6x0M7pE0HPZT+TxboZ7jgb/onm+3Ai+f9nqWe54BnwZeN2KdqaxlhX3zEuAAgz9M+4Gf7fqP9brx9gOS1KBNcc5dkrQ2hrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8DqvwTMMxmKZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "heads = np.random.binomial(500, .5, size=500)\n",
    "histogram = plt.hist(heads, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Task 1\n",
    "## write a program to produce Fibonacci numbers up to 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269]\n"
     ]
    }
   ],
   "source": [
    "fib = [0, 1]\n",
    "while fib[len(fib) - 1] < 1000000:\n",
    "    fib.append(fib[len(fib) - 1] + fib[len(fib) - 2])\n",
    "print(fib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
    "## Calculate the mean and standard deviation of that sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average flip: 0.485\n",
      "Standard deviation: 0.49993599590347565\n"
     ]
    }
   ],
   "source": [
    "flips = np.random.binomial(1, .5, 1000)\n",
    "avgFlip = np.mean(test)\n",
    "stdDevFlip = np.std(flips)\n",
    "print(f\"Average flip: {avgFlip}\\nStandard deviation: {stdDevFlip}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3\n",
    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
    "### use numpy.random.normal to generate gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(0,5,500)\n",
    "e = np.random.normal(0,1,500)\n",
    "y = []\n",
    "for i in range(len(x)):\n",
    "    y.append(0.5 * x[i] + e[i])\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}