Cumulative value counts of categorical data, with group by - pandas

Question

In my data frame, I have a text column group with the group name, and column drop_week, holding a categorical value in range [1,4]. I want to store, for each group, the cumulative count of values 1 to 4 of drop week. I'm doing this:
drop_data = all_data[['group', 'drop_week']].groupby('group')['drop_week'] \
.value_counts().unstack().transpose().fillna(0).cumsum().transpose()
and it works. But since it took me 2 hours of googling to come up with this solution, I was wondering if there is a better way to do it.

Answer 1

You could use pd.crosstab to create the frequency table. Then use cumsum(axis=1) to compute the cumulative sum across each row:
pd.crosstab(index=all_data['group'], columns=all_data['drop_week']).cumsum(axis=1)
# drop_week 1 2 3 4
# group
# 0 12 17 21 27
# 1 7 13 18 25
# 2 9 14 22 26
# 3 5 11 16 22
which agrees with
drop_data = (all_data[['group', 'drop_week']].groupby('group')['drop_week']
.value_counts().unstack().transpose().fillna(0).cumsum().transpose())
# drop_week 1 2 3 4
# group
# 0 12 17 21 27
# 1 7 13 18 25
# 2 9 14 22 26
# 3 5 11 16 22
The setup I used for this was:
import numpy as np
import pandas as pd
np.random.seed(2019)
N = 100
all_data = pd.DataFrame({'group':np.random.randint(4, size=N),
'drop_week':np.random.randint(1,5, size=N)})
drop_data = (all_data[['group', 'drop_week']].groupby('group')['drop_week']
.value_counts().unstack().transpose().fillna(0).cumsum().transpose())