Pandas dataframe pct_change function arguments
Typically financial data is ordered by columns in reverse chronology ie columns '2020', '2019', '2018', '2017'.. But we need the pct_change function in reverse order. Looking at the Pandas reference (last example): https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pct_change.html?highlight=pct_change#pandas.DataFrame.pct_change

df.pct_change(axis='columns')

           2016    2015         2014

GOOG NaN -0.151997 -0.086016

APPL NaN 0.337604 0.012002

Such a calculation makes no sense or at least is not useful. We need to look at change from today over past period, not past period over future period. Other than iterating in reverse order or transposing, can the pct_change function be augmented with a new argument to reverse the calculation to the form: (A0-A1)/A1 instead of (A1-A0)/A0 ??