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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame it should look like below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>female</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  age  female  preTestScore  postTestScore\n",
       "0      Jason    Miller   42       0             4             25\n",
       "1      Molly  Jacobson   52       1            24             94\n",
       "2       Tina       Ali   36       1            31             57\n",
       "3       Jake    Milner   24       0             2             62\n",
       "4        Amy     Cooze   73       1             3             70"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n",
    "#### Hint: Don't forget to place the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114e89d10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sW7aS7JFTpd5Hv34HcddddxURlplZt3GS6IKmpibWrFkBrKowdwlbbLFFT4dk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fcd090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n",
    "### This time the size should be 4.5 times the postTestScore and the color determined by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11608c250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5YnKSMLOeKCJYs2YNlZWV9O3bt8f1Kl3wN9OZmVnnSWLgwJJcYCmajtokbtsq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\nyUnCzMwyOUmYmVmm/w+eYh5l4kpEgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11573c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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
}