jdong6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.31702781 0.34434069 0.00912752]\n",
      " [0.09666205 0.08708225 0.80036621]]\n",
      "Add 5 to every element\n",
      "[[5.31702781 5.34434069 5.00912752]\n",
      " [5.09666205 5.08708225 5.80036621]]\n",
      "Get the first row\n",
      "[5.31702781 5.34434069 5.00912752]\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZYNXUwGB2eF23LPB54PsMvmtlNdVAkjXAbwJXD3WfyhrgiHVsBg5t/zVT/vs0CoN+Ed3M6jJgT1V9cKh941C3TcA93fZO4NXdq/RnAd+sqoOP24AXsVgNVXVHVT2tqmarapZB2JxRVQ+xSmro2ofXSX8DuLPb3glclOSEJBuAjcDnH6/xLuZINQB/y+AFWZI8GziewRdqraYaAF4M3FNV+4fapq4GWLKOrwK/1m2fDRxagpq658TIJv1q8DTegBcweIHmduC27nYeg5nAnV373zF4gRYGr8p/lMG7C+4A5qa1hsP67OOxd92smhqAv+zGeDuDJ+O6oXPe3dVwL907Kaa0huOBv+p+n24Fzl5tNXTHPgG8dpFzpqqGZR6LFwC7GbxTaBfwS13/qXtOjHrzk7GS1DiXbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+38nVlEc5YSvLAAAAABJRU5ErkJggg==\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": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number: 30\n",
      "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"
     ]
    }
   ],
   "source": [
    "fib_seq = [0,1]\n",
    "\n",
    "for x in range(29):\n",
    "    fib_seq.append(fib_seq[-1] + fib_seq[-2])\n",
    "    \n",
    "num_input = input(\"Enter a number: \")\n",
    "parameters = int(num_input)\n",
    "if parameters > 31:\n",
    "    print(\"Number exceeds the limit.\")\n",
    "        \n",
    "else:\n",
    "    for x in range(0, parameters):\n",
    "            print(fib_seq[x])\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": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean is:  0.471 \n",
      "\n",
      "Standard deviation is:  0.4994080580130925\n"
     ]
    }
   ],
   "source": [
    "from statistics import stdev\n",
    "coin = np.random.binomial(1, 0.5, size= 1000)\n",
    "\n",
    "count = 0\n",
    "for n in range(0,len(coin)):\n",
    "    count += coin[n]\n",
    "    \n",
    "mean = count/len(coin)\n",
    "print(\"Mean is: \", mean, \"\\n\")\n",
    "\n",
    "print(\"Standard deviation is: \", stdev(coin, mean))\n",
    "\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": 124,
   "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\n",
    "import matplotlib.pyplot as plt\n",
    "x_dist = numpy.random.normal(0, 5, size = 100)\n",
    "e_dist = numpy.random.normal(0, 1, size = 100)\n",
    "y_dist = 0.5 * x_dist + e_dist",
    "scatterplot = plt.scatter(x_dist, y_dist)\n"
   ]
  }
 ],
 "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
}