spate118.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",
      "[[0.26164005 0.82202412 0.68417867]\n",
      " [0.33870801 0.12359645 0.69239361]]\n",
      "Add 5 to every element\n",
      "[[5.26164005 5.82202412 5.68417867]\n",
      " [5.33870801 5.12359645 5.69239361]]\n",
      "Get the first row\n",
      "[5.26164005 5.82202412 5.68417867]\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": 5,
   "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": [
    "def fibonacci():\n",
    "    num1 = 0\n",
    "    num2 = 1\n",
    "    \n",
    "    while num1 < 1000000:\n",
    "        print(num1)\n",
    "        temp = num1 + num2\n",
    "        num1 = num2\n",
    "        num2 = temp\n",
    "        \n",
    "fibonacci()"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:  0.502\n",
      "Standard Devitation:  0.4999959999839998\n"
     ]
    }
   ],
   "source": [
    "temp = np.random.binomial(1,0.5,1000)\n",
    "\n",
    "print(\"mean: \", np.average(temp))\n",
    "print(\"Standard Devitation: \", np.std(temp))"
   ]
  },
  {
   "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": 19,
   "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",
    "\n",
    "x = np.random.normal(0,5,100)\n",
    "e = np.random.normal(0,1,100)\n",
    "\n",
    "y = 0.5 * x + e\n",
    "\n",
    "plt.xlabel(\"x = Gaussian(0,5)\")\n",
    "plt.ylabel(\"y = 0.5 * x + e (e with gauss (0,1))\")\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}