We have already imported pandas as pd and matplotlib.pyplot as plt. We have also loaded Yahoo stock prices for the years 2013 to 2015, set the frequency to business daily, and assigned the result to yahoo.

Create a new column called shifted_30 that contains the 'price' shifted by 30 business days into the future.
Subtract 'shifted_30' from 'price', and assign the result to a new column, 'change_30'.
Apply .diff(), setting periods to 30, and assign the result to a new column, 'diff_30'.
Inspect the last five rows of yahoo to verify the calculation.
Subtract diff_30 from change_30 using the .sub() method and print the .value_counts() of the result to show both columns are equal.

Lags, changes, and returns for stock price series

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