mmahbub.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.1.0\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",
      "[[ 0.20354485  0.87353642  0.79226415]\n",
      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
      "Add 5 to every element\n",
      "[[ 5.20354485  5.87353642  5.79226415]\n",
      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
      "Get the first row\n",
      "[ 5.20354485  5.87353642  5.79226415]\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": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fibonacci Numbers up to 1000000 :\n",
      "0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,1597,2584,4181,6765,10946,17711,28657,46368,75025,121393,196418,317811,514229,"
     ]
    }
   ],
   "source": [
    "n = 1000000\n",
    "n1 = 0\n",
    "n2 = 1\n",
    "n3 = 1\n",
    "\n",
    "print (\"Fibonacci Numbers up to\", n, \":\")\n",
    "while n3 < n:\n",
    "    print (n1, end = \",\")\n",
    "    n3 = n1 + n2\n",
    "    n1 = n2\n",
    "    n2 = n3\n"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean is :  501.208\n",
      "The Standard Deviation is :  15.467279528087671\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "n, p = 1000, 0.5\n",
    "x = np.random.binomial (n, p , 1000)\n",
    "\n",
    "mean = np.mean (x)\n",
    "print (\"The Mean is : \", mean)\n",
    "\n",
    "stdev = np.std (x)\n",
    "print (\"The Standard Deviation is : \", stdev)\n"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed (198555)\n",
    "\n",
    "x = np.random.normal (0,5, 1000)\n",
    "e = np.random.normal (0,1, 1000)\n",
    "\n",
    "y = 0.5*x + e\n",
    "\n",
    "plt.scatter (x, y, marker=\"o\")\n",
    "plt.xlabel (\"X\")\n",
    "plt.ylabel (\"Y\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}