{ "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": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2. Create the DataFrame that should look like the one below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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first_namelast_nameagefemalepreTestScorepostTestScore
0JasonMiller420425
1MollyJacobson5212494
2TinaAli3613157
3JakeMilner240262
4AmyCooze731370
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" ], "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": 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": [] } ], "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 }