pprovins.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.5.0\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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.37340587 0.85841458 0.0316056 ]\n",
      " [0.41733934 0.11594238 0.69933844]]\n",
      "Add 5 to every element\n",
      "[[5.37340587 5.85841458 5.0316056 ]\n",
      " [5.41733934 5.11594238 5.69933844]]\n",
      "Get the first row\n",
      "[5.37340587 5.85841458 5.0316056 ]\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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aIPsbgIu69+lFwOXj3P4RX/RJnsbg/4CrquqGofHXAK8ELqju5BkHcWuHw+lA2YdcBfxB\ntzzt2Z/H4HzqV5Ls6vLdleQXmf7sVNUTVfWjqvox8Nc8dapgarIfYH/ZDdzQnSr4EvBjBveNmZrc\nsOj7dBXw+8C1Q9NXQvZNwJPLf8+495dJXKCYli8GFzyuBD603/gZDG6pPLPf+Av5yYs8DzO5izwH\nyn7S0PKbgeu75bP5yYs8X5q2132/Obt46mLs1GcH1gwtX8TgHPHU7DOL5H498Kfd8gsYnDbItORe\nan/p3qtf2G9s6rMzOFf/sm75dODObnks+/ph/x8+TV/AbzG4KHIPcHf3dRaDizePDo19fOg5lzC4\niv8g3VXzKcv+KeDebvwfGFygfXKH+2iX/avA7LRl32/OcNFPfXbgb7ts9zC4r9Nw8U98n1kk99HA\n33X7zF3Ay6cp91L7C/BJ4PULPGeqs3fjdzL4hbQD+PVu/lj2df8yVpIad8Sfo5ek1ln0ktQ4i16S\nGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8ApNqW0K2lWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2433ca20>"
      ]
     },
     "metadata": {},
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n",
      "13\n",
      "21\n",
      "34\n",
      "55\n",
      "89\n",
      "144\n",
      "233\n",
      "377\n",
      "610\n",
      "987\n",
      "1597\n",
      "2584\n",
      "4181\n",
      "6765\n",
      "10946\n",
      "17711\n",
      "28657\n",
      "46368\n",
      "75025\n",
      "121393\n",
      "196418\n",
      "317811\n",
      "514229\n",
      "832040\n"
     ]
    }
   ],
   "source": [
    "lastFib = 0\n",
    "currentFib = 1\n",
    "nextFib = None\n",
    "\n",
    "print (lastFib)\n",
    "print (currentFib)\n",
    "while currentFib <= 1000000 :\n",
    "    nextFib = lastFib + currentFib\n",
    "    lastFib = currentFib\n",
    "    currentFib = nextFib\n",
    "    if currentFib <= 1000000 :\n",
    "        print (currentFib)\n"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were 492 heads. Median: 0.492 Standard Deviation: 0.49993599590347565\n"
     ]
    }
   ],
   "source": [
    "flipResults = np.random.binomial(1, .5, 1000)\n",
    "numberOfHeads = sum(flipResults)\n",
    "probabiltyOfHeads = numberOfHeads / 1000\n",
    "flipStandardDeviation = np.std(flipResults)\n",
    "print(\"There were {} heads. Median: {} Standard Deviation: {}\".format(numberOfHeads, probabiltyOfHeads, flipStandardDeviation))"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6bf43be7f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "y = .5*x + e\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}