MeanMedianMode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean, Median, Mode, and introducing NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean vs. Median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create some fake income data, centered around 27,000 with a normal distribution and standard deviation of 15,000, with 10,000 data points. (We'll discuss those terms more later, if you're not familiar with them.)\n",
    "\n",
    "Then, compute the mean (average) - it should be close to 27,000:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27096.64921037032"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "incomes = np.random.normal(27000, 15000, 10000)\n",
    "np.mean(incomes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can segment the income data into 50 buckets, and plot it as a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C4juB/c32fuCuZnsHcKCqzlXVCWAO2DacUCVJy+VKxxw2VtU8QFWdATY25TcCp/rqnW7K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\nJEkt/x9VYZKZLkAY6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44ffdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(incomes, 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compute the median - since we have a nice, even distribution it too should be close to 27,000:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27159.985229669175"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(incomes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll add Donald Trump into the mix. Darn income inequality!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "incomes = np.append(incomes, [1000000000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median won't change much, but the mean does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27163.131505581998"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(incomes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127160.38252311043"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(incomes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's generate some fake age data for 500 people:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([69, 87, 31, 22, 78, 37, 77, 32, 18, 59, 29, 43, 34, 33, 56, 83, 66,\n",
       "       30, 77, 74, 31, 21, 85, 50, 47, 26, 72, 62, 33, 45, 86, 50, 86, 56,\n",
       "       31, 84, 78, 27, 76, 42, 83, 64, 48, 54, 70, 56, 24, 50, 50, 71, 49,\n",
       "       20, 85, 61, 33, 83, 55, 21, 60, 80, 56, 89, 61, 56, 52, 55, 20, 31,\n",
       "       69, 50, 21, 52, 31, 83, 43, 77, 27, 67, 39, 39, 26, 38, 40, 73, 50,\n",
       "       31, 87, 23, 50, 34, 69, 45, 83, 51, 88, 41, 64, 59, 40, 89, 57, 62,\n",
       "       55, 75, 38, 51, 24, 21, 18, 75, 58, 62, 81, 65, 89, 64, 43, 33, 53,\n",
       "       72, 20, 56, 19, 26, 81, 68, 70, 70, 41, 59, 50, 77, 62, 31, 87, 58,\n",
       "       63, 83, 35, 55, 38, 85, 53, 66, 28, 74, 42, 28, 80, 69, 54, 25, 74,\n",
       "       58, 27, 42, 87, 46, 43, 44, 33, 40, 21, 21, 73, 48, 87, 63, 84, 55,\n",
       "       61, 66, 48, 73, 27, 60, 34, 77, 59, 58, 50, 70, 30, 76, 72, 33, 80,\n",
       "       43, 63, 49, 60, 61, 53, 55, 79, 38, 46, 38, 81, 66, 29, 81, 46, 19,\n",
       "       49, 57, 31, 18, 25, 47, 20, 88, 33, 88, 50, 22, 57, 39, 20, 59, 63,\n",
       "       38, 35, 59, 28, 23, 56, 50, 46, 65, 46, 88, 87, 34, 73, 75, 32, 49,\n",
       "       67, 77, 86, 38, 80, 36, 64, 79, 65, 51, 46, 54, 23, 82, 56, 41, 78,\n",
       "       19, 45, 38, 70, 74, 56, 87, 49, 69, 30, 25, 22, 71, 39, 41, 46, 72,\n",
       "       33, 72, 88, 37, 75, 39, 37, 21, 67, 86, 77, 20, 46, 53, 22, 85, 73,\n",
       "       89, 67, 24, 24, 25, 62, 56, 58, 44, 63, 30, 36, 73, 49, 45, 26, 33,\n",
       "       20, 62, 75, 34, 81, 59, 64, 27, 43, 23, 62, 75, 81, 40, 65, 29, 61,\n",
       "       55, 81, 35, 68, 79, 86, 43, 35, 74, 59, 80, 75, 60, 82, 66, 54, 37,\n",
       "       54, 71, 88, 46, 55, 63, 79, 89, 48, 61, 68, 78, 51, 32, 26, 48, 78,\n",
       "       76, 62, 19, 19, 63, 20, 44, 28, 34, 58, 44, 36, 70, 34, 67, 50, 33,\n",
       "       31, 18, 72, 55, 49, 63, 81, 65, 51, 46, 22, 55, 77, 76, 53, 79, 47,\n",
       "       57, 46, 27, 29, 49, 71, 19, 85, 86, 77, 89, 59, 67, 26, 50, 79, 85,\n",
       "       68, 51, 30, 18, 73, 52, 22, 53, 56, 26, 45, 60, 83, 50, 34, 68, 65,\n",
       "       27, 72, 24, 34, 37, 52, 67, 79, 79, 24, 65, 71, 28, 29, 61, 34, 77,\n",
       "       35, 59, 50, 83, 27, 32, 18, 81, 36, 46, 48, 39, 52, 23, 37, 62, 54,\n",
       "       53, 50, 34, 36, 88, 83, 39, 89, 65, 83, 73, 66, 28, 36, 56, 86, 65,\n",
       "       28, 46, 18, 61, 69, 80, 85, 29, 85, 44, 18, 61, 68, 83, 89, 53, 65,\n",
       "       55, 66, 87, 55, 43, 32, 84])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages = np.random.randint(18, high=90, size=500)\n",
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([50]), count=array([16]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.mode(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}