'''
While pandas and NumPy have tons of functions, sometimes you may need a different function to summarize your data.

The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super efficient.

In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains outliers.
'''
# Import NumPy and create custom IQR function
import numpy as np
def iqr(column):
    return column.quantile(0.75) - column.quantile(0.25)

# Update to print IQR and median of temperature_c, fuel_price_usd_per_l, & unemployment
print(sales[["temperature_c", "fuel_price_usd_per_l", "unemployment"]].agg([iqr, np.median]))