Calculate several metrics by sector and exchange

The .agg() function allows you to aggregate your data in even more ways. Providing a list of names of statistical methods calculates more than one summary statistic at once. You can provide new names for the aggregated columns using the rename method, which takes a dictionary argument where the keys are the names of the metrics you computes and the values are your desired new names.

In this exercise, you will calculate the mean, median, and standard deviation of market capitalizations in millions of USD. pandas as pd and matplotlib.pyplot as plt have been imported, and the listings DataFrame, with reference column 'Exchange' is available in your workspace.

With broadcasting and .div(), create a new column 'market_cap_m' that contains the market capitalization data in millions of USD.
Group your data by both 'Sector' and 'Exchange', assigning the result to by_sector_exchange.
Assign the market_cap_m column of by_sector_exchange to a variable bse_mcm.
Use .agg() to calculate the mean, median, and standard deviation for market_cap_m, and call the rename method with a dictionary argument for the keyword parameter columns storing the results in 'Average', 'Median', and 'Standard Deviation', respectively, and assign to summary.
Print the result to your console.

Exercise

Calculate several metrics by sector and exchange

Instructions