yahmad1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.92817842 0.47078123 0.52034727]\n",
      " [0.29101269 0.48325051 0.38927697]]\n",
      "Add 5 to every element\n",
      "[[5.92817842 5.47078123 5.52034727]\n",
      " [5.29101269 5.48325051 5.38927697]]\n",
      "Get the first row\n",
      "[5.92817842 5.47078123 5.52034727]\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "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": [
    "%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": 29,
   "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(a, b):\n",
    "    if(a + b < 1000000):\n",
    "        c = a+b\n",
    "        print(c)\n",
    "        fibonacci(b,c)\n",
    "    else:\n",
    "        return\n",
    "        \n",
    "if(__name__ == \"__main__\"):\n",
    "    a = 0\n",
    "    b = 1\n",
    "    print(a)\n",
    "    print(b)\n",
    "    fibonacci(a,b)"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.509\n",
      "0.4999189934379369\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "tosses = np.random.binomial(1,0.5,1000)\n",
    "print(np.mean(tosses))\n",
    "print(np.std(tosses))"
   ]
  },
  {
   "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": 32,
   "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(0,5,1000)\n",
    "e = np.random.normal(0,1,1000)\n",
    "y = 0.5*x+e\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel(\"Guassian with (0,5)\")\n",
    "plt.ylabel(\"y = 0.5*x*e\")\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
}