delzinga.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.83836093 0.66960498 0.42614393]\n",
      " [0.20509363 0.47941061 0.83773066]]\n",
      "Add 5 to every element\n",
      "[[5.83836093 5.66960498 5.42614393]\n",
      " [5.20509363 5.47941061 5.83773066]]\n",
      "Get the first row\n",
      "[5.83836093 5.66960498 5.42614393]\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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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 = [1, 1]\n",
    "while fib[-1] + fib[-2] < 1000000:\n",
    "    fib.append(fib[-1] + fib[-2])\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.504 0.4999839997439918\n"
     ]
    }
   ],
   "source": [
    "tosses = np.random.binomial(n=1, p=0.5, size=1000)\n",
    "mean = np.mean(tosses)\n",
    "std = np.std(tosses)\n",
    "print(mean, 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": 31,
   "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(loc=0, scale=5, size=1000)\n",
    "e = np.random.normal(loc=0, scale=1, size=1000)\n",
    "y = 0.5*x + e\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel('x', fontsize=15); plt.ylabel('y', fontsize=15)\n",
    "plt.show()"
   ]
  }
 ],
 "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
}