[FEA] Groupby pct_change

[beckernick]

For time series analysis and pandas API compatibility, it would be useful to implement Groupby.pct_change. It may be feasible to implement Groupby.pct_change reasonably efficiently in the Python layer by using existing groupby functionality (diff and shift). As a starting point, it would make sense to limit the supported parameters/configuration to those currently supported by Series.pct_change.

Per the pandas documentation, "Calculate pct_change of each value to previous entry in group."

import pandas as pd
​
df = pd.DataFrame({
    "key": [0, 1, 1, 0, 0, 1],
    "val": [1, 8, 3, 9, -3, 8],
})
print(df, "\n")
print(df.groupby("key").pct_change())
   key  val
0    0    1
1    1    8
2    1    3
3    0    9
4    0   -3
5    1    8 

        val
0       NaN
1       NaN
2 -0.625000
3  8.000000
4 -1.333333
5  1.666667

[github-actions]

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[beckernick]

I believe this could be expressed fairly smoothly from Python with two separate groupby aggregations with a loose structure of:

• Handle nulls
• Groupby diff (with supported arguments)
• Groupby shift (with supported arguments)
• Binary op on resultants of diff and shift

Using internal APIs, is it possible to calculate the diff and shift in a single call to groupby aggregation?

[github-actions]

This issue has been labeled inactive-30d due to no recent activity in the past 30 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. This issue will be labeled inactive-90d if there is no activity in the next 60 days.

[github-actions]

This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.