Lets say I have the following data frame:
d = {'store': [a, a, a, b, b], 'date': [2020-1-30, 2020-1-30, 2020-2-28,
2020-1-30, 2020-3-30], 'amount': [1, 2, 3, 5, 2]}
df = pd.DataFrame(data=d)
df
store date amount
0 a 2020-1-30 1
1 a 2020-1-30 2
2 a 2020-2-28 3
3 b 2020-1-30 5
4 b 2020-3-30 2
I would like to have a column that is an incrementing integer that specifies what period the dates corresponds to for a specific store, as well aa flag column that notes if the date is the highest date, the output would be the following:
store date amount period is_max_period
0 a 2020-1-30 1 1 0
1 a 2020-1-30 2 1 0
2 a 2020-2-28 3 2 1
3 b 2020-1-30 5 1 0
4 b 2020-3-30 2 2 1
Would would be the bets way to go about this?
Try with transform
with factorize
and max
g = df.groupby(['store'])['date']
df['period'] = g.transform(lambda x : x.factorize()[0]+1)
df['is_max_period'] = df.date.eq(g.transform('max')).astype(int)
df
store date amount period is_max_period
0 a 2020-1-30 1 1 0
1 a 2020-1-30 2 1 0
2 a 2020-2-28 3 2 1
3 b 2020-1-30 5 1 0
4 b 2020-3-30 2 2 1
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