Evaluate monthly fraction of yearly data - Python [duplicate]

Question

I have a pandas dataframe as:

ID	Date	Value
A	1/1/2000	5
A	2/1/2000	10
A	3/1/2000	20
A	4/1/2000	10
B	1/1/2000	100
B	2/1/2000	200
B	3/1/2000	300
B	4/1/2000	400

How do I evaluate the monthly fraction of the total yearly value for each ID as the fourth column?

ID	Date	Value	Fraction
A	1/1/2000	5	0.11
A	2/1/2000	10	0.22
A	3/1/2000	20	0.44
A	4/1/2000	10	0.11
B	1/1/2000	100	0.11
B	2/1/2000	200	0.22
B	3/1/2000	300	0.33
B	4/1/2000	400	0.44

I guess I could use groupby?

Answer 1

I think your data is missing another year to be representative, if you do not have just a single year in the DataFrame.

I just added one line for 2001:

import pandas as pd

df['Date'] = pd.to_datetime(df['Date'])

print(df)

  ID       Date  Value
0  A 2000-01-01      5
1  A 2000-02-01     10
2  A 2000-03-01     20
3  A 2000-04-01     10
4  B 2000-01-01    100
5  B 2000-02-01    200
6  B 2000-03-01    300
7  B 2000-04-01    400
8  B 2001-04-01     20

If I understood correctly you can do it like this:

df['Fraction'] = (df['Value'] / df.groupby(['ID', df['Date'].dt.year])['Value'].transform('sum')).round(2)

print(df)

  ID       Date  Value  Fraction
0  A 2000-01-01      5      0.11
1  A 2000-02-01     10      0.22
2  A 2000-03-01     20      0.44
3  A 2000-04-01     10      0.22
4  B 2000-01-01    100      0.10
5  B 2000-02-01    200      0.20
6  B 2000-03-01    300      0.30
7  B 2000-04-01    400      0.40
8  B 2001-04-01     20      1.00

Answer 2

You can divide the Value column by the result of a groupby.transform sum, followed by round(2) to match your expected output:

df['Fraction'] = df['Value'] / df.groupby('ID')['Value'].transform('sum')
df['Fraction'] = df['Fraction'].round(2)

print(df)

  ID      Date  Value  Fraction
0  A  1/1/2000      5      0.11
1  A  2/1/2000     10      0.22
2  A  3/1/2000     20      0.44
3  A  4/1/2000     10      0.22
4  B  1/1/2000    100      0.10
5  B  2/1/2000    200      0.20
6  B  3/1/2000    300      0.30
7  B  4/1/2000    400      0.40