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.
Este exercício faz parte do curso
Importing and Managing Financial Data in Python
Instruções do exercício
- 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 toby_sector_exchange. - Assign the
market_cap_mcolumn ofby_sector_exchangeto a variablebse_mcm. - Use
.agg()to calculate the mean, median, and standard deviation formarket_cap_m, and call the rename method with a dictionary argument for the keyword parametercolumnsstoring the results in'Average','Median', and'Standard Deviation', respectively, and assign tosummary. - Print the result to your console.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Create market_cap_m
listings['market_cap_m'] = ____[____].div(1e6)
# Group listing by both Sector and Exchange
by_sector_exchange = ____.____(['Sector', 'Exchange'])
# Subset market_cap_m of by_sector_exchange
bse_mcm = ____[____]
# Calculate mean, median, and std in summary
summary = ____.____(['____', '____', '____']).rename(columns={'mean': ____, 'median': ____, 'std':____})
# Print the summary
print(summary)