dbarry.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",
      "[[0.7952643  0.58929194 0.71917932]\n",
      " [0.43377349 0.4775211  0.48200919]]\n",
      "Add 5 to every element\n",
      "[[5.7952643  5.58929194 5.71917932]\n",
      " [5.43377349 5.4775211  5.48200919]]\n",
      "Get the first row\n",
      "[5.7952643  5.58929194 5.71917932]\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.conjugate>"
      ]
     },
     "execution_count": 4,
     "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.conjugate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "1346269\n"
     ]
    }
   ],
   "source": [
    "n1 = 1\n",
    "n2 = 1\n",
    "print(n1)\n",
    "print(n2)\n",
    "\n",
    "while n2 <= 1000000:\n",
    "    prev = n1\n",
    "    n1 = n2\n",
    "    n2 = prev + n1\n",
    "    print(n2)"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial results: \n",
      "[1 0 0 1 1 1 1 1 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 1 1 0 1 1 0 0 0\n",
      " 1 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0\n",
      " 0 0 1 1 1 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 0\n",
      " 0 0 1 1 0 1 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 1 1 0 1 1 1 1\n",
      " 1 1 0 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 0 1 0 0 1 0\n",
      " 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0\n",
      " 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0\n",
      " 0 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
      " 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 1\n",
      " 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 1 1 0 1 0 0 0 1 0 0 0 0 1 1\n",
      " 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1\n",
      " 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 0 1\n",
      " 1 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0\n",
      " 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 0\n",
      " 0 0 0 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1\n",
      " 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 1 0 0 1\n",
      " 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 0\n",
      " 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 0 1 1 1\n",
      " 1 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 1 1 0 0\n",
      " 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0\n",
      " 1 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 1 1 1\n",
      " 1 1 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1\n",
      " 0 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 1 1\n",
      " 1 1 0 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0\n",
      " 1 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1\n",
      " 0 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 1 0\n",
      " 1]\n",
      "Average:  0.512\n",
      "Standard Deviation:  0.49985597925802594\n"
     ]
    }
   ],
   "source": [
    "val = np.random.binomial(1,0.5,1000)\n",
    "print(\"Trial results: \")\n",
    "print(val)\n",
    "avg = np.average(val)\n",
    "print(\"Average: \", avg)\n",
    "stddev = np.std(val)\n",
    "print(\"Standard Deviation: \", stddev)"
   ]
  },
  {
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(loc=2.5,scale=0.5,size=1000)\n",
    "e = np.random.normal(loc=0.5,scale=0.5,size=1000)\n",
    "y = 0.5*x + e\n",
    "#print(y)\n",
    "scatter = plt.scatter(x,y)"
   ]
  }
 ],
 "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
}