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{
"cells": [
{
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"# 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"
]
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"### Step 2. Create the DataFrame that should look like the one below."
]
},
{
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"execution_count": 2,
"metadata": {
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"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"
}
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"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": []
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"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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