import pandas as pd

# Print the values of homelessness
print(homelessness.values)

[['South Atlantic' 'West Virginia' 1021.0 222.0 1804291]
     ['East North Central' 'Wisconsin' 2740.0 2167.0 5807406]
     ['Mountain' 'Wyoming' 434.0 205.0 577601]]

# Print the column index of homelessness
print(homelessness.columns)

    Index(['region', 'state', 'individuals', 'family_members', 'state_pop'], 
                dtype='object')

# Print the row index of homelessness
print(homelessness.index)

    Int64Index([0, 1, 2, 3, 4, ..., 49, 50], dtype='int64')



print(sales['date'].max())

# 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]))

            temperature_c  fuel_price_usd_per_l  unemployment
    iqr            16.583                 0.073         0.565
    median         16.967                 0.743         8.099