lpassare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.5.0\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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0ee5n8Dym4eKfxePhcOAvu2PiXuD0Wc7brfs88PYVtpla3lXe4zcAOxh8I9oO/MIsHMN9P/zNWElqXLPX6CVJAxa9JDXOopekxln0ktQ4i16SGmfRS1LjLHpJapxFL0mN+39tKjVy4QG7CQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "1 1\n",
      "2 1\n",
      "3 2\n",
      "4 3\n",
      "5 5\n",
      "6 8\n",
      "7 13\n",
      "8 21\n",
      "9 34\n",
      "10 55\n",
      "11 89\n",
      "12 144\n",
      "13 233\n",
      "14 377\n",
      "15 610\n",
      "16 987\n",
      "17 1597\n",
      "18 2584\n",
      "19 4181\n",
      "20 6765\n",
      "21 10946\n",
      "22 17711\n",
      "23 28657\n",
      "24 46368\n",
      "25 75025\n",
      "26 121393\n",
      "27 196418\n",
      "28 317811\n",
      "29 514229\n",
      "30 832040\n"
     ]
    }
   ],
   "source": [
    "def fib(n):\n",
    "    if n < 2:\n",
    "        return n\n",
    "    return fib(n-2) + fib(n-1)\n",
    "\n",
    "    \n",
    "for i in range(31):\n",
    "    print(i, fib(i))\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of 1000 tosses of a fair coin is 0.501\n",
      "The standard deviation of 1000 tosses of a fair coin is 0.49999899999899994\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "heads =np.random.binomial(1, 0.5,1000)\n",
    "Mean_heads=np.mean(heads)\n",
    "Std_heads =np.std(heads)\n",
    "print (\"The mean of 1000 tosses of a fair coin is\", Mean_heads)\n",
    "print (\"The standard deviation of 1000 tosses of a fair coin is\", Std_heads)"
   ]
  },
  {
   "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": 17,
   "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",
    "%matplotlib inline \n",
    "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=np.zeros(100)\n",
    "\n",
    "for num in range(y.size):\n",
    "    y[num]=0.5*x[num] + e[num]\n",
    "\n",
  
    "plt.scatter(x,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
}