As usual, we have imported pandas as pd and matplotlib.pyplot as plt for you.

Use pd.read_csv() to import 'gdp_growth.csv' and 'djia.csv', for both set a DateTimeIndex based on the 'date' column using parse_dates and index_col, and assign the results to gdp_growth and djia respectively, then inspect using .info().
Resample djia using frequency alias 'QS', aggregate using .first(), and assign to djia_quarterly.
Apply .pct_change() to djia_quarterly and .mul() by 100 to obtain djia_quarterly_return.
Use pd.concat() to concatenate gdp_growth and djia_quarterly_return along axis=1, and assign to data. Rename the columns using .columns and the new labels 'gdp' and 'djia', then .plot() the results.

Compare time series growth rates