Pandas group cumsum keep colums

Question

I spent several hours trying to make a “cumulative total group” on the pandas framework. I looked through all the answers on stackoverflow and, surprisingly, none of them can solve my (very elementary) problem:

I have a dataframe:

df1 Out[8]: Name Date Amount 0 Jack 2016-01-31 10 1 Jack 2016-02-29 5 2 Jack 2016-02-29 8 3 Jill 2016-01-31 10 4 Jill 2016-02-29 5

I'm trying to

• by ['Name', 'Date'] and
• cumsum 'Amount'.
• That's all.

Thus, the desired result:

df1 Out[10]: Name Date Cumsum 0 Jack 2016-01-31 10 1 Jack 2016-02-29 23 2 Jill 2016-01-31 10 3 Jill 2016-02-29 15

EDIT: . "" cumsum. , "10, 23, 10, 15". . NB: , , .

/ .

Answer 1

, Amount drop:

df1['Cumsum'] = df1.groupby(by=['Name','Date'])['Amount'].cumsum()
df1 = df1.drop('Amount', axis=1)
print (df1)
   Name        Date  Cumsum
0  Jack  2016-01-31      10
1  Jack  2016-02-29       5
2  Jack  2016-02-29      13
3  Jill  2016-01-31      10
4  Jill  2016-02-29       5

assign:

df1 = df1.assign(Cumsum=df1.groupby(by=['Name','Date'])['Amount'].cumsum())
         .drop('Amount', axis=1)
print (df1)
   Name        Date  Cumsum
0  Jack  2016-01-31      10
1  Jack  2016-02-29       5
2  Jack  2016-02-29      13
3  Jill  2016-01-31      10
4  Jill  2016-02-29       5

:

groupby Name Date sum, groupby level Name cumsum.

df = df1.groupby(by=['Name','Date'])['Amount'].sum()
        .groupby(level='Name').cumsum().reset_index(name='Cumsum')
print (df)
   Name        Date  Cumsum
0  Jack  2016-01-31      10
1  Jack  2016-02-29      23
2  Jill  2016-01-31      10
3  Jill  2016-02-29      15

Answer 2

, groupby.

df.set_index(['Name', 'Date']).groupby(level=[0, 1]).Amount.cumsum().reset_index()

---

, OP , .

df1.groupby(
    ['Name','Date']
)Amount.sum().groupby(
    level='Name'
).cumsum()

, jezrael