{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matplotlib Practice\n", "\n", "\n", "In this practice, we will analyze various statistics from MBA applicants across 22 different industries. The data is contained in the file \"MBA_Stat.csv\". \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, read in the data and use the head method to double check that everything has been read in correctly. Make sure to add you \"%matplotlib inline\" statement at the top of the code block as well importing pandas and matplotlib.pyplot." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", " | Industry | \n", "Avg_GMAT | \n", "Avg_Age | \n", "Avg_Job_Months | \n", "
---|---|---|---|---|
0 | \n", "Accounting | \n", "657.555379 | \n", "28.445946 | \n", "60.108108 | \n", "
1 | \n", "Advertising/Marketing Services | \n", "633.750000 | \n", "29.625000 | \n", "71.750000 | \n", "
2 | \n", "Aerospace | \n", "674.683303 | \n", "28.635116 | \n", "60.818182 | \n", "
3 | \n", "Agribusiness | \n", "636.666667 | \n", "28.611111 | \n", "52.444444 | \n", "
4 | \n", "Auto/Transportation | \n", "643.480908 | \n", "30.118421 | \n", "69.500000 | \n", "