jstaman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.1.0\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": 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": 2,
   "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": [
    "n1 = 0\n",
    "n2 = 1\n",
    "print(n1)\n",
    "print(n2)\n",
    "\n",
    "while (n1+n2 < 1000000):\n",
    "    print(n1+n2)\n",
    "    tmp = n1\n",
    "    n1 = n2\n",
    "    n2 = tmp + n1"
   ]
  },
  {
   "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 = 0.497\n",
      "Standard Deviation = 0.4999909999189985\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "tosses = np.random.binomial(1, .5, size = 1000)\n",
    "mean = np.mean(tosses)\n",
    "std = np.std(tosses)\n",
    "print(f'Mean = {mean}')\n",
    "print(f'Standard Deviation = {std}')"
   ]
  },
  {
   "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": 14,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "y = 0.5*x + e\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}