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  3. Importing and Managing Financial Data in Python

Exercise

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.

Instructions

100 XP
  • 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().