rwill166.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
      "[[0.27142682 0.56768879 0.90623109]\n",
      " [0.49184161 0.5147603  0.26799111]]\n",
      "Add 5 to every element\n",
      "[[5.27142682 5.56768879 5.90623109]\n",
      " [5.49184161 5.5147603  5.26799111]]\n",
      "Get the first row\n",
      "[5.27142682 5.56768879 5.90623109]\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.all>"
      ]
     },
     "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.all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5VT1SVT+uqp8Af8VjlwAmJjcc9njZB1zXXQb4IvATBg+yWg7ZSbIC+D3g6qHVl0P2LcCj03/HhB4zCzLui/7DXwxuWnwK+NCc8U3APcDUnPHn8tM3ah5gfDdqDpd949D0m4Fru+lz+OkbNV+ctPd9zjp7eeyG6kRkP8J7vnpo+iIG13uXy/HyeuBPuunnMLgkkOWQvVu2Cfj8nLGJz87g2vuLu+mzgN3d9EQc6yP9t447wJw3+LcY3Ni4E7ij+zqbwQ2Yh4bGPj60zSUM7r7fR3e3e8Kyfwa4qxv/ewY3WR89yD7aZf8KMD1p2eesM1zuE5H9CO/533S57mTwTKPhsp/04+VE4G+7Y+Z24CXLJXu37JPA6+fZZqKzd+O7GfwS2gX8Wrf+RBzro3z5+AFJatDEXXOXJPVnuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QG/R+nzXTPKiG55wAAAABJRU5ErkJggg==\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fibonacci Sequence:\n",
      "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(\"Fibonacci Sequence:\")\n",
    "print(n1)\n",
    "print(n2)\n",
    "while (n1 + n2) < 1000000:\n",
    "    output = n1 + n2\n",
    "    print(output)\n",
    "    n1 = n2\n",
    "    n2 = output"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean =  0.513  (0 is Tails, 1 is Heads)\n",
      "Standard Deviation =  0.5000810745080052\n"
     ]
    }
   ],
   "source": [
    "x = np.random.binomial(1, 0.5, 1000)\n",
    "mean = sum(x)/1000\n",
    "print(\"Mean = \", mean, \" (0 is Tails, 1 is Heads)\")\n",
    "standardDeviation = np.sqrt((sum(x)*(1-mean)**2+(1000-sum(x))*mean**2)/(1000-1))\n",
    "print(\"Standard Deviation = \", standardDeviation)"
   ]
  },
  {
   "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": {
      "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, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "y = 0.5 * x + e\n",
    "plt.xlabel(\"x = Gauss(0,5)\")\n",
    "plt.ylabel(\"y=0.5*x+e\\ne~Gauss(0,1)\")\n",
    "plt.title(\"Scatterplot\")\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
}