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 },
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Data Science Project 1: NYC Dept. of Education Data on NYS Math Exam results\n",
      "\n",
      "Data source: https://nycopendata.socrata.com/data?cat=education  \n",
      "Data description:  \n",
      "NYS Math Exam Results for NYC between Grades 3-8 from 2006-2011.  \n",
      "Proficieny Level 1, 2- Below level for that grade  \n",
      "Proficiency Level 3- appropriate for that grade  \n",
      "Proficiency Level 4- above the alevel apprpriate for that grade  \n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###What am I exploring?  \n",
      "- Exam Proficiency levels across all of NYC  \n",
      "- Exam Proficiency levels within boroughs  \n",
      "- Exam Proficiency levels across all of NYC by gender  \n",
      "- Exam Proficiency levels in NYC changes from 2006-2011  \n",
      "\n",
      "**------------------------------------------------------------------------------------------------**\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##1. Acquiring Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd \n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Manual data fixing done before reading it here:  \n",
      "Removed columns with percentage (This I know)  \n",
      "Added columns 1&2 together to create a new one (I don't know how to do in Python\"  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BoroMath = pd.read_csv(\"MathTest_Boro2.csv\")\n",
      "#saved locally in the same folder"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**------------------------------------------------------------------------------------------------**\n",
      "\n",
      "##2.Cleaning Data "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BoroMath = BoroMath.rename(columns={'Level1&2': 'BelowAverage', 'Level3':'Average', 'Level4':'AboveAverage'})\n",
      "#renamed the header for ease \n",
      "\n",
      "BoroMath.head(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Borough</th>\n",
        "      <th>Grade</th>\n",
        "      <th>Year</th>\n",
        "      <th>Category</th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "      <th>Level3&amp;4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2006</td>\n",
        "      <td> Female</td>\n",
        "      <td> 7984</td>\n",
        "      <td> 664</td>\n",
        "      <td> 2546</td>\n",
        "      <td> 4232</td>\n",
        "      <td> 1206</td>\n",
        "      <td> 5438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2006</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 8461</td>\n",
        "      <td> 663</td>\n",
        "      <td> 2773</td>\n",
        "      <td> 4386</td>\n",
        "      <td> 1302</td>\n",
        "      <td> 5688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2007</td>\n",
        "      <td> Female</td>\n",
        "      <td> 7803</td>\n",
        "      <td> 675</td>\n",
        "      <td> 1780</td>\n",
        "      <td> 4410</td>\n",
        "      <td> 1613</td>\n",
        "      <td> 6023</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "  Borough Grade  Year Category  Number Tested  Mean Scale Score  BelowAverage  \\\n",
        "0   BRONX     3  2006   Female           7984               664          2546   \n",
        "1   BRONX     3  2006     Male           8461               663          2773   \n",
        "2   BRONX     3  2007   Female           7803               675          1780   \n",
        "\n",
        "   Average  AboveAverage  Level3&4  \n",
        "0     4232          1206      5438  \n",
        "1     4386          1302      5688  \n",
        "2     4410          1613      6023  \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "BoroMath2 = BoroMath.drop(['Level3&4'], inplace=True,axis=1)\n",
      "#Got rid of the Level3&4 here for practice, could've deleted in file like the others. "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Creating sorted groups:     \n",
      "\n",
      "_GroupedMath_ stores all entries for that borough (grades 3-8, all grades)  \n",
      "_AllMath_ stores all entries for a particular borough for \"All Grades\"  \n",
      "_AllStudents_ stores all the entries for \"All Students\" for all of the boroughs\n",
      "\n",
      "_Q: what form does this become? List? _   \n",
      "_Q: When would I care?_  \n",
      "_Q: Can I do math operations with this form?_   "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "GroupedBronxMath = BoroMath[BoroMath['Borough']=='BRONX']\n",
      "#This con\n",
      "AllBronxMath = GroupedBronxMath[GroupedBronxMath['Grade']=='All Grades']\n",
      "#AllBronxMath is all grades for a borough\n",
      "\n",
      "GroupedBrooklynMath = BoroMath[BoroMath['Borough']=='BROOKLYN']\n",
      "AllBrooklynMath = GroupedBrooklynMath[GroupedBrooklynMath['Grade']=='All Grades']\n",
      "\n",
      "GroupedManhattanMath = BoroMath[BoroMath['Borough']=='MANHATTAN']\n",
      "AllManhattanMath = GroupedManhattanMath[GroupedManhattanMath['Grade']=='All Grades']\n",
      "\n",
      "GroupedQueensMath = BoroMath[BoroMath['Borough']=='QUEENS']\n",
      "AllQueensMath = GroupedQueensMath[GroupedQueensMath['Grade']=='All Grades']\n",
      "\n",
      "GroupedSIMath = BoroMath[BoroMath['Borough']=='STATEN ISLAND']\n",
      "AllSIMath = GroupedSIMath[GroupedSIMath['Grade']=='All Grades']\n",
      "\n",
      "AllStudents = BoroMath[BoroMath['Grade']=='All Grades']\n",
      "\n",
      "#There's 12 per borough\n",
      "#6 per gender\n",
      "#2 per year"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### All Students   \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AllStudents.head(12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Borough</th>\n",
        "      <th>Grade</th>\n",
        "      <th>Year</th>\n",
        "      <th>Category</th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>72</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2006</td>\n",
        "      <td> Female</td>\n",
        "      <td> 48244</td>\n",
        "      <td> 644</td>\n",
        "      <td> 26437</td>\n",
        "      <td> 18403</td>\n",
        "      <td> 3404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2006</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 49616</td>\n",
        "      <td> 642</td>\n",
        "      <td> 27282</td>\n",
        "      <td> 18682</td>\n",
        "      <td> 3652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>74</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2007</td>\n",
        "      <td> Female</td>\n",
        "      <td> 47011</td>\n",
        "      <td> 654</td>\n",
        "      <td> 21301</td>\n",
        "      <td> 21034</td>\n",
        "      <td> 4676</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2007</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 50061</td>\n",
        "      <td> 651</td>\n",
        "      <td> 23866</td>\n",
        "      <td> 21195</td>\n",
        "      <td> 5000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2008</td>\n",
        "      <td> Female</td>\n",
        "      <td> 45661</td>\n",
        "      <td> 662</td>\n",
        "      <td> 15303</td>\n",
        "      <td> 25117</td>\n",
        "      <td> 5241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2008</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 48700</td>\n",
        "      <td> 659</td>\n",
        "      <td> 18005</td>\n",
        "      <td> 25219</td>\n",
        "      <td> 5476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>78</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2009</td>\n",
        "      <td> Female</td>\n",
        "      <td> 45423</td>\n",
        "      <td> 671</td>\n",
        "      <td> 10599</td>\n",
        "      <td> 27585</td>\n",
        "      <td> 7239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>79</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2009</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 48521</td>\n",
        "      <td> 668</td>\n",
        "      <td> 13256</td>\n",
        "      <td> 27851</td>\n",
        "      <td> 7414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2010</td>\n",
        "      <td> Female</td>\n",
        "      <td> 45466</td>\n",
        "      <td> 670</td>\n",
        "      <td> 26240</td>\n",
        "      <td> 13502</td>\n",
        "      <td> 5724</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2010</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 48687</td>\n",
        "      <td> 668</td>\n",
        "      <td> 28920</td>\n",
        "      <td> 13910</td>\n",
        "      <td> 5857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>82</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2011</td>\n",
        "      <td> Female</td>\n",
        "      <td> 45598</td>\n",
        "      <td> 672</td>\n",
        "      <td> 24794</td>\n",
        "      <td> 16042</td>\n",
        "      <td> 4762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>83</th>\n",
        "      <td> BRONX</td>\n",
        "      <td> All Grades</td>\n",
        "      <td> 2011</td>\n",
        "      <td>   Male</td>\n",
        "      <td> 48423</td>\n",
        "      <td> 669</td>\n",
        "      <td> 27535</td>\n",
        "      <td> 15843</td>\n",
        "      <td> 5045</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "   Borough       Grade  Year Category  Number Tested  Mean Scale Score  \\\n",
        "72   BRONX  All Grades  2006   Female          48244               644   \n",
        "73   BRONX  All Grades  2006     Male          49616               642   \n",
        "74   BRONX  All Grades  2007   Female          47011               654   \n",
        "75   BRONX  All Grades  2007     Male          50061               651   \n",
        "76   BRONX  All Grades  2008   Female          45661               662   \n",
        "77   BRONX  All Grades  2008     Male          48700               659   \n",
        "78   BRONX  All Grades  2009   Female          45423               671   \n",
        "79   BRONX  All Grades  2009     Male          48521               668   \n",
        "80   BRONX  All Grades  2010   Female          45466               670   \n",
        "81   BRONX  All Grades  2010     Male          48687               668   \n",
        "82   BRONX  All Grades  2011   Female          45598               672   \n",
        "83   BRONX  All Grades  2011     Male          48423               669   \n",
        "\n",
        "    BelowAverage  Average  AboveAverage  \n",
        "72         26437    18403          3404  \n",
        "73         27282    18682          3652  \n",
        "74         21301    21034          4676  \n",
        "75         23866    21195          5000  \n",
        "76         15303    25117          5241  \n",
        "77         18005    25219          5476  \n",
        "78         10599    27585          7239  \n",
        "79         13256    27851          7414  \n",
        "80         26240    13502          5724  \n",
        "81         28920    13910          5857  \n",
        "82         24794    16042          4762  \n",
        "83         27535    15843          5045  \n",
        "\n",
        "[12 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AllStudents.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>   60.000000</td>\n",
        "      <td>    60.000000</td>\n",
        "      <td>  60.000000</td>\n",
        "      <td>    60.000000</td>\n",
        "      <td>    60.000000</td>\n",
        "      <td>    60.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 2008.500000</td>\n",
        "      <td> 42922.066667</td>\n",
        "      <td> 672.683333</td>\n",
        "      <td> 15044.266667</td>\n",
        "      <td> 18976.133333</td>\n",
        "      <td>  8901.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    1.722237</td>\n",
        "      <td> 20169.577332</td>\n",
        "      <td>  11.342872</td>\n",
        "      <td>  9136.840989</td>\n",
        "      <td>  9767.739463</td>\n",
        "      <td>  5341.662627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 2006.000000</td>\n",
        "      <td> 12431.000000</td>\n",
        "      <td> 642.000000</td>\n",
        "      <td>  1639.000000</td>\n",
        "      <td>  4452.000000</td>\n",
        "      <td>  2493.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 2007.000000</td>\n",
        "      <td> 26797.000000</td>\n",
        "      <td> 665.750000</td>\n",
        "      <td>  6785.250000</td>\n",
        "      <td>  9237.000000</td>\n",
        "      <td>  4414.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 2008.500000</td>\n",
        "      <td> 48333.500000</td>\n",
        "      <td> 673.500000</td>\n",
        "      <td> 13360.000000</td>\n",
        "      <td> 19191.500000</td>\n",
        "      <td>  6629.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 2010.000000</td>\n",
        "      <td> 60109.250000</td>\n",
        "      <td> 682.000000</td>\n",
        "      <td> 22884.500000</td>\n",
        "      <td> 27596.750000</td>\n",
        "      <td> 13857.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 2011.000000</td>\n",
        "      <td> 70730.000000</td>\n",
        "      <td> 688.000000</td>\n",
        "      <td> 32470.000000</td>\n",
        "      <td> 36905.000000</td>\n",
        "      <td> 19667.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "              Year  Number Tested  Mean Scale Score  BelowAverage  \\\n",
        "count    60.000000      60.000000         60.000000     60.000000   \n",
        "mean   2008.500000   42922.066667        672.683333  15044.266667   \n",
        "std       1.722237   20169.577332         11.342872   9136.840989   \n",
        "min    2006.000000   12431.000000        642.000000   1639.000000   \n",
        "25%    2007.000000   26797.000000        665.750000   6785.250000   \n",
        "50%    2008.500000   48333.500000        673.500000  13360.000000   \n",
        "75%    2010.000000   60109.250000        682.000000  22884.500000   \n",
        "max    2011.000000   70730.000000        688.000000  32470.000000   \n",
        "\n",
        "            Average  AboveAverage  \n",
        "count     60.000000     60.000000  \n",
        "mean   18976.133333   8901.666667  \n",
        "std     9767.739463   5341.662627  \n",
        "min     4452.000000   2493.000000  \n",
        "25%     9237.000000   4414.000000  \n",
        "50%    19191.500000   6629.000000  \n",
        "75%    27596.750000  13857.250000  \n",
        "max    36905.000000  19667.000000  \n",
        "\n",
        "[8 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_This doesn't tell much because of the data type_"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AllStudentsYr = AllStudents.groupby(['Year','Category']).sum()\n",
      "\n",
      "AllStudentsYr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Year</th>\n",
        "      <th>Category</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2006</th>\n",
        "      <th>Female</th>\n",
        "      <td> 216807</td>\n",
        "      <td> 3289</td>\n",
        "      <td>  90995</td>\n",
        "      <td>  93221</td>\n",
        "      <td> 32591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 223781</td>\n",
        "      <td> 3282</td>\n",
        "      <td>  96436</td>\n",
        "      <td>  93633</td>\n",
        "      <td> 33712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2007</th>\n",
        "      <th>Female</th>\n",
        "      <td> 211962</td>\n",
        "      <td> 3335</td>\n",
        "      <td>  71104</td>\n",
        "      <td>  99512</td>\n",
        "      <td> 41346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 222919</td>\n",
        "      <td> 3322</td>\n",
        "      <td>  80398</td>\n",
        "      <td> 100880</td>\n",
        "      <td> 41641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
        "      <th>Female</th>\n",
        "      <td> 206949</td>\n",
        "      <td> 3370</td>\n",
        "      <td>  49665</td>\n",
        "      <td> 111877</td>\n",
        "      <td> 45407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 217858</td>\n",
        "      <td> 3357</td>\n",
        "      <td>  59549</td>\n",
        "      <td> 112195</td>\n",
        "      <td> 46114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
        "      <th>Female</th>\n",
        "      <td> 206320</td>\n",
        "      <td> 3408</td>\n",
        "      <td>  34282</td>\n",
        "      <td> 116928</td>\n",
        "      <td> 55110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 217072</td>\n",
        "      <td> 3394</td>\n",
        "      <td>  42827</td>\n",
        "      <td> 119581</td>\n",
        "      <td> 54664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
        "      <th>Female</th>\n",
        "      <td> 207155</td>\n",
        "      <td> 3404</td>\n",
        "      <td>  92805</td>\n",
        "      <td>  66925</td>\n",
        "      <td> 47425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 218583</td>\n",
        "      <td> 3393</td>\n",
        "      <td> 103001</td>\n",
        "      <td>  68683</td>\n",
        "      <td> 46899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">2011</th>\n",
        "      <th>Female</th>\n",
        "      <td> 207503</td>\n",
        "      <td> 3409</td>\n",
        "      <td>  85770</td>\n",
        "      <td>  77753</td>\n",
        "      <td> 43980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td> 218415</td>\n",
        "      <td> 3398</td>\n",
        "      <td>  95824</td>\n",
        "      <td>  77380</td>\n",
        "      <td> 45211</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "               Number Tested  Mean Scale Score  BelowAverage  Average  \\\n",
        "Year Category                                                           \n",
        "2006 Female           216807              3289         90995    93221   \n",
        "     Male             223781              3282         96436    93633   \n",
        "2007 Female           211962              3335         71104    99512   \n",
        "     Male             222919              3322         80398   100880   \n",
        "2008 Female           206949              3370         49665   111877   \n",
        "     Male             217858              3357         59549   112195   \n",
        "2009 Female           206320              3408         34282   116928   \n",
        "     Male             217072              3394         42827   119581   \n",
        "2010 Female           207155              3404         92805    66925   \n",
        "     Male             218583              3393        103001    68683   \n",
        "2011 Female           207503              3409         85770    77753   \n",
        "     Male             218415              3398         95824    77380   \n",
        "\n",
        "               AboveAverage  \n",
        "Year Category                \n",
        "2006 Female           32591  \n",
        "     Male             33712  \n",
        "2007 Female           41346  \n",
        "     Male             41641  \n",
        "2008 Female           45407  \n",
        "     Male             46114  \n",
        "2009 Female           55110  \n",
        "     Male             54664  \n",
        "2010 Female           47425  \n",
        "     Male             46899  \n",
        "2011 Female           43980  \n",
        "     Male             45211  \n",
        "\n",
        "[12 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###All FEMALE Students"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Allgirls = AllStudents[AllStudents['Category']=='Female']\n",
      "Allgirls.groupby(['Year']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 216807</td>\n",
        "      <td> 3289</td>\n",
        "      <td> 90995</td>\n",
        "      <td>  93221</td>\n",
        "      <td> 32591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 211962</td>\n",
        "      <td> 3335</td>\n",
        "      <td> 71104</td>\n",
        "      <td>  99512</td>\n",
        "      <td> 41346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 206949</td>\n",
        "      <td> 3370</td>\n",
        "      <td> 49665</td>\n",
        "      <td> 111877</td>\n",
        "      <td> 45407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 206320</td>\n",
        "      <td> 3408</td>\n",
        "      <td> 34282</td>\n",
        "      <td> 116928</td>\n",
        "      <td> 55110</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 207155</td>\n",
        "      <td> 3404</td>\n",
        "      <td> 92805</td>\n",
        "      <td>  66925</td>\n",
        "      <td> 47425</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 207503</td>\n",
        "      <td> 3409</td>\n",
        "      <td> 85770</td>\n",
        "      <td>  77753</td>\n",
        "      <td> 43980</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "      Number Tested  Mean Scale Score  BelowAverage  Average  AboveAverage\n",
        "Year                                                                      \n",
        "2006         216807              3289         90995    93221         32591\n",
        "2007         211962              3335         71104    99512         41346\n",
        "2008         206949              3370         49665   111877         45407\n",
        "2009         206320              3408         34282   116928         55110\n",
        "2010         207155              3404         92805    66925         47425\n",
        "2011         207503              3409         85770    77753         43980\n",
        "\n",
        "[6 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###All MALE Students"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Allboys = AllStudents[AllStudents['Category']=='Male']\n",
      "Allboys.groupby(['Year']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Number Tested</th>\n",
        "      <th>Mean Scale Score</th>\n",
        "      <th>BelowAverage</th>\n",
        "      <th>Average</th>\n",
        "      <th>AboveAverage</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 223781</td>\n",
        "      <td> 3282</td>\n",
        "      <td>  96436</td>\n",
        "      <td>  93633</td>\n",
        "      <td> 33712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 222919</td>\n",
        "      <td> 3322</td>\n",
        "      <td>  80398</td>\n",
        "      <td> 100880</td>\n",
        "      <td> 41641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 217858</td>\n",
        "      <td> 3357</td>\n",
        "      <td>  59549</td>\n",
        "      <td> 112195</td>\n",
        "      <td> 46114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 217072</td>\n",
        "      <td> 3394</td>\n",
        "      <td>  42827</td>\n",
        "      <td> 119581</td>\n",
        "      <td> 54664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 218583</td>\n",
        "      <td> 3393</td>\n",
        "      <td> 103001</td>\n",
        "      <td>  68683</td>\n",
        "      <td> 46899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 218415</td>\n",
        "      <td> 3398</td>\n",
        "      <td>  95824</td>\n",
        "      <td>  77380</td>\n",
        "      <td> 45211</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "      Number Tested  Mean Scale Score  BelowAverage  Average  AboveAverage\n",
        "Year                                                                      \n",
        "2006         223781              3282         96436    93633         33712\n",
        "2007         222919              3322         80398   100880         41641\n",
        "2008         217858              3357         59549   112195         46114\n",
        "2009         217072              3394         42827   119581         54664\n",
        "2010         218583              3393        103001    68683         46899\n",
        "2011         218415              3398         95824    77380         45211\n",
        "\n",
        "[6 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Looking at Boroughs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "AllBronxMath.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "Borough             BRONXBRONXBRONXBRONXBRONXBRONXBRONXBRONXBRONXB...\n",
        "Grade               All GradesAll GradesAll GradesAll GradesAll Gr...\n",
        "Year                                                            24102\n",
        "Category            FemaleMaleFemaleMaleFemaleMaleFemaleMaleFemale...\n",
        "Number Tested                                                  571411\n",
        "Mean Scale Score                                                 7930\n",
        "BelowAverage                                                   263538\n",
        "Average                                                        244383\n",
        "AboveAverage                                                    63490\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Data sets sorted for 2011 and Boroughs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Bx_below = AllBronxMath[AllBronxMath['Year']==2011].sum().BelowAverage\n",
      "Bx_avg = AllBronxMath[AllBronxMath['Year']==2011].sum().Average\n",
      "Bx_above = AllBronxMath[AllBronxMath['Year']==2011].sum().AboveAverage\n",
      "\n",
      "M_below = AllManhattanMath[AllManhattanMath['Year']==2011].sum().BelowAverage\n",
      "M_avg = AllManhattanMath[AllManhattanMath['Year']==2011].sum().Average\n",
      "M_above = AllManhattanMath[AllManhattanMath['Year']==2011].sum().AboveAverage\n",
      "\n",
      "Bk_below = AllBrooklynMath[AllBrooklynMath['Year']==2011].sum().BelowAverage\n",
      "Bk_avg = AllBrooklynMath[AllBrooklynMath['Year']==2011].sum().Average\n",
      "Bk_above = AllBrooklynMath[AllBrooklynMath['Year']==2011].sum().AboveAverage\n",
      "\n",
      "Q_below = AllQueensMath[AllQueensMath['Year']==2011].sum().BelowAverage\n",
      "Q_avg = AllQueensMath[AllQueensMath['Year']==2011].sum().Average\n",
      "Q_above = AllQueensMath[AllQueensMath['Year']==2011].sum().AboveAverage\n",
      "\n",
      "SI_below = AllSIMath[AllSIMath['Year']==2011].sum().BelowAverage\n",
      "SI_avg = AllSIMath[AllSIMath['Year']==2011].sum().Average\n",
      "SI_above = AllSIMath[AllSIMath['Year']==2011].sum().AboveAverage"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "NYC_Below = [int(Bx_below), int(M_below), int(Bk_below), int(Q_below), int(SI_below)]\n",
      "NYC_Avg = [int(Bx_avg), int(M_avg), int(Bk_avg), int(Q_avg), int(SI_avg)]\n",
      "NYC_Above = [int(Bx_above), int(M_above), int(Bk_above), int(Q_above), int(SI_above)]\n",
      "\n",
      "NYC_Bxtot= [float(Bx_below) + float(Bx_avg) + float(Bx_above)]\n",
      "NYC_Mtot= [int(M_below) + int(M_avg) + int(M_above)]\n",
      "NYC_Bktot= [int(Bk_below) + int(Bk_avg) + int(Bk_above)]\n",
      "NYC_Qtot= [int(Q_below) + int(Q_avg) + int(Q_above)]\n",
      "NYC_SItot= [int(SI_below) + int(SI_avg) + int(SI_above)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "**____________________________________________________________________________________________________________________________**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "\n",
      "##Proficiency Level Changes over Time (2006-2011)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Scores... proficiency Level...\n",
      "\n",
      "plt.figure(figsize=(10,5))\n",
      "plt.scatter(AllStudents.Year, AllStudents.BelowAverage, lw=10, alpha=.5, color='m')\n",
      "plt.scatter(AllStudents.Year, AllStudents.Average, lw=10, alpha=.5, color='c')\n",
      "plt.scatter(AllStudents.Year, AllStudents.AboveAverage, lw=10, alpha=.5, color='g')\n",
      "plt.xlabel(\"Year\")\n",
      "\n",
      "#plt.set_xticklabels(('2006', '2007', '2008', '2009', '2010', '2011') )\n",
      "#Tried this from above plot, didn't work\n",
      "\n",
      "plt.ylabel(\"Students at Proficiency\")\n",
      "plt.title(\"Proficiency Over Time\",fontsize='15')\n",
      "\n",
      "plt.legend(('Below Average', 'Average', 'Above Average'), bbox_to_anchor = (1.3, 1))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.legend.Legend at 0x10729f7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zgtPn4z8WCwWtzB7aVF+zmUERESKIaMLh9TLr0CG+byX4OuYSvZ6/pKYSqdGc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\nCCGEEGLC/j/h7sjIV8ddtwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10723d390>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Q: Why did I have to rename the column from two words to one word in order to sum it? Ex: It was Below Average, now it's BelowAverage_\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "\n",
      "##Female vs Male G3-8 Students at AVERAGE Math proficiency (2006-2011)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 6\n",
      "BoysLevel3 = Allboys.groupby(['Year']).sum().Average\n",
      "GirlsLevel3 = Allgirls.groupby(['Year']).sum().Average\n",
      "\n",
      "ind = np.arange(N)\n",
      "width = 0.35\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "rectsl = ax.bar(ind, BoysLevel3, width, color='b')\n",
      "rects2 = ax.bar(ind+width, GirlsLevel3, width, color='y')\n",
      "\n",
      "\n",
      "\n",
      "# add some\n",
      "ax.set_ylabel('Number of Students')\n",
      "ax.set_title('Number of Students at Average Proficiency in Math')\n",
      "ax.set_xticks(ind+width)\n",
      "ax.set_xticklabels( ('2006', '2007', '2008', '2009', '2010', '2011') )\n",
      "\n",
      "ax.legend( ('rects1'[0], rects2[0]), ('Boys', 'Girls') )\n",
      "\n",
      "ax.legend(('Boys', 'Girls'), loc='upper right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/aribajahan/anaconda/lib/python2.7/site-packages/matplotlib/legend.py:613: UserWarning: Legend does not support r\n",
        "Use proxy artist instead.\n",
        "\n",
        "http://matplotlib.sourceforge.net/users/legend_guide.html#using-proxy-artist\n",
        "\n",
        "  (str(orig_handle),))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.legend.Legend at 0x10783f1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SCTs7OyQmJkptMjMz8eyzzzYY04oVK6S/PT094enpKU+yRETNMHToUHTo0AE7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\nKRSKBttr3UPZvXs3Tp06BX1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       "text": [
        "<matplotlib.figure.Figure at 0x1072ae390>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "##Female vs Male G3-8 Students at BELOW AVERAGE proficiency (2006-2011)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 6\n",
      "BoysLevel12 = Allboys.groupby(['Year']).sum().BelowAverage\n",
      "GirlsLevel12 = Allgirls.groupby(['Year']).sum().BelowAverage\n",
      "\n",
      "ind = np.arange(N)\n",
      "width = 0.35\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "rectsl = ax.bar(ind, BoysLevel12, width, color='b')\n",
      "rects2 = ax.bar(ind+width, GirlsLevel12, width, color='y')\n",
      "\n",
      "\n",
      "\n",
      "# add some\n",
      "ax.set_ylabel('Number of Students')\n",
      "ax.set_title('Number of Students at Below Average Proficiency in Math')\n",
      "ax.set_xticks(ind+width)\n",
      "ax.set_xticklabels( ('2006', '2007', '2008', '2009', '2010', '2011') )\n",
      "\n",
      "ax.legend( ('rects1'[0], rects2[0]), ('Boys', 'Girls'))\n",
      "\n",
      "ax.legend(('Boys', 'Girls'), bbox_to_anchor = (1.3, 1) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.legend.Legend at 0x107a0db90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CAtC7d2+cP38eu3fvRkBAQL3xbNq0CRkZGejSpQuAquPily9fxq5duxASEoKwsDBs3boV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\nfZJufahMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072b4d10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "##Female vs Male G3-8 Students at ABOVE AVERAGE proficiency (2006-2011)##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 6\n",
      "BoysLevel4 = Allboys.groupby(['Year']).sum().AboveAverage\n",
      "GirlsLevel4 = Allgirls.groupby(['Year']).sum().AboveAverage\n",
      "\n",
      "ind = np.arange(N)\n",
      "width = 0.35\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "rectsl = ax.bar(ind, BoysLevel4, width, color='b')\n",
      "rects2 = ax.bar(ind+width, GirlsLevel4, width, color='y')\n",
      "\n",
      "\n",
      "#Labels of Axes\n",
      "ax.set_ylabel('Number of Students')\n",
      "ax.set_title('Number of Students at Above Average Proficiency in Math')\n",
      "ax.set_xticks(ind+width)\n",
      "ax.set_xticklabels( ('2006', '2007', '2008', '2009', '2010', '2011') )\n",
      "\n",
      "ax.legend( ('rects1'[0], rects2[0]), ('Boys', 'Girls') )\n",
      "\n",
      "ax.legend(('Boys', 'Girls'), loc='upper right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.legend.Legend at 0x107a53e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yOZKSkgAA0dHREIvFkMvlWLx4MZYvX67ulAghhFelpaVYs2YNvvrqK0ydOhX6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\nKiqEEEJ4Q0WFEEIIb/4Pk/X0cL9HkYoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072384d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Q: But how do I fix the years... can I modify the tick names??_"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "##2011 Math Proficiency in NYC (G3-8)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PIE1 = [181594, 155133, 89191] \n",
      "# NYC_Avg, NYC_Above]\n",
      "labelsP = ('Below Average', 'Average', 'Above Average')\n",
      "plt.subplot(aspect=True)\n",
      "plt.pie(PIE1, labels=labelsP, colors = ('y', 'm', 'b'),autopct='%i%%')\n",
      "plt.title(\"NYC Math Proficiency Level Grade 3-8, 2011\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x107a711d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F7du3CQ72p1WrWFq31isdRzIhy5Zp2LbNnf37j+Hk5KR0nEJBFlUO0tPTadiw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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107232310>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Q: How do I do percentage?!?!_  \n",
      "_I need to do: Bx_below/NYC_Bxtot *100_"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "##2011 Math Proficiency Breakdown by Boroughs (G3-8)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#NYC_Below = [Bx_below, M_below, Bk_below, Q_below, SI_below]\n",
      "#NYC_Avg = [Bx_avg, M_avg, Bk_avg, Q_avg, SI_avg]\n",
      "#NYC_Above = [Bx_above, M_above, Bk_above, Q_above, SI_above]\n",
      "\n",
      "N = 5\n",
      "\n",
      "ind = np.arange(N)\n",
      "#width = 0.35\n",
      "\n",
      "margin = 0.8\n",
      "width = (1.-2.*margin)/N\n",
      "fig, ax = plt.subplots(figsize=(10,5))\n",
      "\n",
      "\n",
      "rects1 = ax.bar(ind+width+width, NYC_Below, width, color='y')\n",
      "rects2 = ax.bar(ind+width, NYC_Avg, width, color='m')\n",
      "rects3 = ax.bar(ind, NYC_Above, width, color='b')\n",
      "\n",
      "\n",
      "ax.set_ylabel('Students')\n",
      "ax.set_title(('Students Proficiency Levels by Borough 2011'), fontsize = 15)\n",
      "ax.set_xticks(ind+width)\n",
      "ax.set_xticklabels(('Bronx', 'Manhattan', 'Brooklyn', 'Queens', 'Staten sland'))\n",
      "\n",
      "\n",
      "ax.legend( (rects1[0], rects2[0], rects3[0]), ('Below Average', 'Average', 'Above Average') )\n",
      "\n",
      "ax.legend(('Below Average', 'Average', 'Above Average'), fontsize=10, loc='upper right')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.legend.Legend at 0x107d1ce90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ESZMmIS8vD4MGDcKvv/6KgwcPwszMDN9//z2WLVuGW7duYeDAgYiMjIRC8eD7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107d08a50>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**____________________________________________________________________________________________________________________________**\n",
      "##2011 Math Proficiency in NYC (G3-8)   \n",
      "_(Stacked View)_"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#NYC_Below = [Bx_below, M_below, Bk_below, Q_below, SI_below]\n",
      "#NYC_Avg = [Bx_avg, M_avg, Bk_avg, Q_avg, SI_avg]\n",
      "#NYC_Above = [Bx_above, M_above, Bk_above, Q_above, SI_above]\n",
      "\n",
      "N = 5\n",
      "\n",
      "Boro_Below= (55.7, 40.13, 43.5, 34.5, 34.7)\n",
      "Std1= (1,1,1,1,1)\n",
      "\n",
      "Boro_Avg= (34.0, 34.7, 36, 38.6, 41.0)\n",
      "Std2= (1,1,1,1,1)\n",
      "\n",
      "Boro_Above= (10.3, 25.2, 20.6, 26.9, 24.0)\n",
      "Std3= (1,1,1,1,1)\n",
      "\n",
      "ind = np.arange(N)\n",
      "width = 0.35\n",
      "\n",
      "margin = 0.8\n",
      "#width = (1.-2.*margin)/N\n",
      "#fig, ax = plt.subplots(figsize=(10,5))\n",
      "\n",
      "\n",
      "p1 = plt.bar(ind,Boro_Below, width, color='y')\n",
      "p2 = plt.bar(ind,Boro_Avg, width, color='m', bottom=Boro_Below)\n",
      "p3 = plt.bar(ind,Boro_Above, width, color='b', bottom=[Boro_Below[j] + Boro_Avg[j] for j in range(len(Boro_Below))])\n",
      "\n",
      "\n",
      "plt.ylabel('Students %')\n",
      "plt.title(('Students Proficiency Levels by Borough 2011'), fontsize = 15)\n",
      "plt.xticks(ind+width/2., ('Bronx', 'Manhattan', 'Brooklyn', 'Queens', 'Staten sland') )\n",
      "#plt.yticks(np.arange(0,81,10))\n",
      "\n",
      "plt.legend((p1[0], p2[0], p3[0]),('Below Average', 'Average', 'Above Average'),fontsize=10, bbox_to_anchor = (1.4, 1))\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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shtDQUAwePBgAMHjwYPz+++/w8vKCt7c3wsLCMHPmTISHhyM8PBxXr17F5cuX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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107d98e10>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}