jim5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[ 0.20354485  0.87353642  0.79226415]\n",
      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
      "Add 5 to every element\n",
      "[[ 5.20354485  5.87353642  5.79226415]\n",
      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
      "Get the first row\n",
      "[ 5.20354485  5.87353642  5.79226415]\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": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9DK57NFz2k3ysHAv8WXe83AOcPQ25u3WfBt404j4Tm7sb38PgF9Bu4Kcn7Rg/3JeXH5CkBk3lnLsk6fAsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktSg/wWZ4RMwGAMymwAAAABJRU5ErkJggg==",
      "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": 1,
   "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]\n"
     ]
    }
   ],
   "source": [
    "fib = []\n",
    "fib.append(0)\n",
    "fib.append(1)\n",
    "\n",
    "while fib[-1] + fib[-2] < 1000000:\n",
    "    fib.append(fib[-1] + fib[-2])\n",
    "    \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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.483\n",
      "Standard deviation: 0.49971091643068993\n"
     ]
    }
   ],
   "source": [
    "tosses = np.random.binomial(1, 0.5, size=1000)\n",
    "\n",
    "print(\"Mean:\", np.mean(tosses))\n",
    "print(\"Standard deviation:\", np.std(tosses))"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f76e1528e80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "\n",
    "plt.scatter(x, 0.5*x + e)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit",
   "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": "3.6.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}