Calculate mean YoY percentage change - Pandas DataFrame

Question

I have a Pandas DataFrame with Monthly observations. I'd like to calculate a couple of metrics - MoM and YoY pct change.

import pandas as pd
import numpy as np

df = pd.DataFrame({
                   'c': ['A','A','A','B','B','B','C','C'],
                   'z': [1, 2, 3, 4, 5, 6, 7, 8],
                   '2018-01': [10, 12, 14, 16, 18, 20, 22, 24],
                   '2018-02': [12, 14, 16, 18, 20, 22, 24, 26],
                   '2019-01': [8, 10, 12, 14, 16, 18, 20, 22],
                   '2019-02': [10, 12, 14, 16, 18, 20, 22, 24]
                 })

For each z in c, I'd like to calculate the MoM and YoY change in percentage. This is would be pct different between observations in month column and aggregate percent change in year.

I am looking for a solution that is generalizable across several monthly columns and year.

Expected output:

c  z  2018-01 2018-02 2019-01 2019-02 Avg_YoY_pct

A  1    10                              -18.18
A  2    12
A  3    14
B  4    .............................
B  5
B  6
C  7
C  8

Avg_YoY_pct is calculated as percentage difference between sum of all monthly values of the year.

Answer 1

Thanks for providing example input so nicely. Here's an approach that first melts the table into long form and then permforms a groupby to get average YoY for each month, and then another groupby to get average YoY over all months. I think it is flexible to more months and years columns

#melt the wide table into a long table
long_df = df.melt(
    id_vars=['c','z'],
    var_name='date',
    value_name='val',
)

#extract the year and month from the date column
long_df[['year','month']] = long_df['date'].str.split('-', expand=True)
long_df['year'] = long_df['year'].astype(int)
long_df['month'] = long_df['month'].astype(int)

#group by c/z/month and shift to get avg yoy for each month
avg_month_yoy = long_df.groupby(['c','z','month'])['val'].apply(
    lambda v: v.sub(v.shift(1)).div(v.shift(1)).multiply(100).mean()
).reset_index()

#group by just c/z to get avg yoy over all months
avg_yoy = avg_month_yoy.groupby(['c','z'])['val'].mean()

#Add the avg_yoy back into the original table
df = df.set_index(['c','z'])
df['Avg_YoY_pct'] = avg_yoy
df = df.reset_index()

print(df)

Output