vhanset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "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.pyplot as plt\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": 4,
   "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": [
    "fibNums = [0, 1]\n",
    "index = 0\n",
    "while (1):\n",
    "    if (fibNums[index] + fibNums[index+1] > 1000000):\n",
    "        break\n",
    "    \n",
    "    fibNums.append(fibNums[index] + fibNums[index+1])\n",
    "    index = index + 1\n",
    "\n",
    "print(fibNums)"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.49\n",
      "Standard Deviation: 0.4998999899979995\n"
     ]
    }
   ],
   "source": [
    "fairToss = np.random.binomial(1, .5, size=1000)\n",
    "print(\"Mean: \" + str(np.mean(fairToss)))\n",
    "print(\"Standard Deviation: \" + str(np.std(fairToss)))"
   ]
  },
  {
   "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": 44,
   "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, size=1000)\n",
    "e = np.random.normal(0,1, size=1000)\n",
    "plt.scatter(x, 0.5*x+e)\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
}