All summary statistics by sector

You can apply the various summary statistics that you have learned about in the last chapter to a groupby object to obtain the result on a per-category basis. This includes the .describe() function, which provides several insights all at once!

Here, you will practice this with the NASDAQ listings. pandas has been imported as pd, and the NASDAQ stock exchange listings data is available in your workspace in the nasdaq DataFrame.

This exercise is part of the course

Importing and Managing Financial Data in Python

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Exercise instructions

Inspect the nasdaq data using .info().
Create a new column market_cap_m that contains the market cap in millions of USD. On the next line, drop the column 'Market Capitalization'.
Group your nasdaq data by 'Sector' and assign to nasdaq_by_sector.
Call the method .describe() on nasdaq_by_sector, assign to summary, and print the result.
This works, but result is in long format and uses a pd.MultiIndex() that you saw earlier. Convert summary to wide format by calling .unstack().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Inspect NASDAQ data
nasdaq.____()

# Create market_cap_m
nasdaq['market_cap_m'] = ____[____].div(1e6)

# Drop the Market Capitalization column
nasdaq.drop('Market Capitalization', axis=1, inplace=True)

# Group nasdaq by Sector
nasdaq_by_sector = ____.____(____)

# Create summary statistics by sector
summary = ____.____()

# Print the summary
print(summary)

# Unstack 
summary = ____.____()

# Print the summary again
print(summary)

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