awilki13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
      "[[0.98393822 0.81796797 0.31816451]\n",
      " [0.81098878 0.75674723 0.96108016]]\n",
      "Add 5 to every element\n",
      "[[5.98393822 5.81796797 5.31816451]\n",
      " [5.81098878 5.75674723 5.96108016]]\n",
      "Get the first row\n",
      "[5.98393822 5.81796797 5.31816451]\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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\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": 7,
   "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": [
    "n0 = 0\n",
    "n1 = 1\n",
    "n2 = 0\n",
    "\n",
    "print(n0)\n",
    "print(n1)\n",
    "\n",
    "while n1<1000000 : \n",
    "    n2 = n0 + n1\n",
    "    n0 = n1\n",
    "    n1 = n2\n",
    "    if n1<1000000:\n",
    "        print(n1)"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.508\n",
      "0.49993599590347565\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "n, p = 1, .5\n",
    "s = np.random.binomial(n, p, 1000)\n",
    "\n",
    "m = np.mean(s)\n",
    "sd = np.std(s)\n",
    "\n",
    "print(m)\n",
    "print(sd)\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\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 = 0.5 * x + e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0154761550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "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
}