Summarizing the data

You'll continue your EDA in this exercise by computing summary statistics for the numeric data in the dataset. The data has been pre-loaded into a DataFrame called df.

You can use df.info() in the IPython Shell to determine which columns of the data are numeric, specifically type float64. You'll notice that there are two numeric columns, called FTE and Total.

FTE: Stands for "full-time equivalent". If the budget item is associated to an employee, this number tells us the percentage of full-time that the employee works. A value of 1 means the associated employee works for the school full-time. A value close to 0 means the item is associated to a part-time or contracted employee.
Total: Stands for the total cost of the expenditure. This number tells us how much the budget item cost.

After printing summary statistics for the numeric data, your job is to plot a histogram of the non-null FTE column to see the distribution of part-time and full-time employees in the dataset.

This course touches on a lot of concepts you may have forgotten, so if you ever need a quick refresher, download the Scikit-Learn Cheat Sheet and keep it handy!

Print summary statistics of the numeric columns in the DataFrame df using the .describe() method.
Import matplotlib.pyplot as plt.
Create a histogram of the non-null 'FTE' column. You can do this by passing df['FTE'].dropna() to plt.hist().
The title has been specified and axes have been labeled, so hit submit to see how often school employees work full-time!

Exercise

Summarizing the data

Instructions