npatel79.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",
      "[[0.81000283 0.39231928 0.42523453]\n",
      " [0.74412923 0.81979243 0.29040905]]\n",
      "Add 5 to every element\n",
      "[[5.81000283 5.39231928 5.42523453]\n",
      " [5.74412923 5.81979243 5.29040905]]\n",
      "Get the first row\n",
      "[5.81000283 5.39231928 5.42523453]\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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qnwB/zePTAmPPC0c8J/YBN3TTA18AfsLgA67GnvkoP3OrgN8Drh3afFLzbgEeW/57JuWcOJ4T/JP8xeCmxyeAD84bPwe4D5iaN/5cfvrmzoMc35s7R8q7aWj5TcD13fJ5/PTNnS9MwvM7b5u9PH5DdSLzAmuHli9hMAc89vNhkcyvA/6kW34Og6mCjDvz0c6J7ufuc/PGJjIvg7n3F3fLZwO7J+IcPp4Hm+Qv4DcZ3Bi5G7ir+zqXwU2bh4fG/mpon8sY3LF/gO5u+QTk/RRwTzf+Dwxusj52Yn6ky/tlYGYS8s7bZrjcJzIv8LddnrsZfF7ScNmP7XxYJPPJwN9158WdwEsmIfPRzgng48DrFthn4vJ247sZ/OLZBfzaJJzDfvyAJDXIOXdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhr0fyz2erLyXg/VAAAAAElFTkSuQmCC\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": [
      "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": [
    "up_to = 1000000\n",
    "at1 = 0\n",
    "at2 = 1\n",
    "while at1 < up_to:\n",
    "    print(at1)\n",
    "    at2 = at1 + at2\n",
    "    at1 = at2 - at1"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean =  0.504\n",
      "standard deviation =  0.4999839997439918\n"
     ]
    }
   ],
   "source": [
    "\n",
    "s = np.random.binomial(1, .5, 1000)\n",
    "total = 0;\n",
    "for a in s:\n",
    "    total += a;\n",
    "print(\"mean = \", total/1000)\n",
    "print(\"standard deviation = \", np.std(s))"
   ]
  },
  {
   "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": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0b3e64d340>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 = .5*x+e\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "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
}