Pandas groupby 累计和

Question

I would like to add a cumulative sum column to my Pandas dataframe so that:

name	day	no
Jack	Monday	10
Jack	Tuesday	20
Jack	Tuesday	10
Jack	Wednesday	50
Jill	Monday	40
Jill	Wednesday	110

becomes:

Jack | Monday     | 10  | 10
Jack | Tuesday    | 30  | 40
Jack | Wednesday  | 50  | 90
Jill | Monday     | 40  | 40
Jill | Wednesday  | 110 | 150

I tried various combos of df.groupby and df.agg(lambda x: cumsum(x)) to no avail.

Answer 1

This should do it, need groupby() twice:

df.groupby(['name', 'day']).sum() \
  .groupby(level=0).cumsum().reset_index()

Explanation:

print(df)
   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   20
2  Jack    Tuesday   10
3  Jack  Wednesday   50
4  Jill     Monday   40
5  Jill  Wednesday  110

# sum per name/day
print( df.groupby(['name', 'day']).sum() )
                 no
name day           
Jack Monday      10
     Tuesday     30
     Wednesday   50
Jill Monday      40
      Wednesday  110

# cumulative sum per name/day
print( df.groupby(['name', 'day']).sum() \
         .groupby(level=0).cumsum() )
                 no
name day           
Jack Monday      10
     Tuesday     40
     Wednesday   90
Jill Monday      40
     Wednesday  150

The dataframe resulting from the first sum is indexed by 'name' and by 'day' . You can see it by printing

df.groupby(['name', 'day']).sum().index 

When computing the cumulative sum, you want to do so by 'name' , corresponding to the first index (level 0).

Finally, use reset_index to have the names repeated.

df.groupby(['name', 'day']).sum().groupby(level=0).cumsum().reset_index()

   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   40
2  Jack  Wednesday   90
3  Jill     Monday   40
4  Jill  Wednesday  150

Answer 2

This works in pandas 0.16.2

In[23]: print df
        name          day   no
0      Jack       Monday    10
1      Jack      Tuesday    20
2      Jack      Tuesday    10
3      Jack    Wednesday    50
4      Jill       Monday    40
5      Jill    Wednesday   110
In[24]: df['no_cumulative'] = df.groupby(['name'])['no'].apply(lambda x: x.cumsum())
In[25]: print df
        name          day   no  no_cumulative
0      Jack       Monday    10             10
1      Jack      Tuesday    20             30
2      Jack      Tuesday    10             40
3      Jack    Wednesday    50             90
4      Jill       Monday    40             40
5      Jill    Wednesday   110            150

Answer 3

Modification to @Dmitry's answer. This is simpler and works in pandas 0.19.0:

print(df) 

 name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   20
2  Jack    Tuesday   10
3  Jack  Wednesday   50
4  Jill     Monday   40
5  Jill  Wednesday  110

df['no_csum'] = df.groupby(['name'])['no'].cumsum()

print(df)
   name        day   no  no_csum
0  Jack     Monday   10       10
1  Jack    Tuesday   20       30
2  Jack    Tuesday   10       40
3  Jack  Wednesday   50       90
4  Jill     Monday   40       40
5  Jill  Wednesday  110      150

Answer 4

you should use

df['cum_no'] = df.no.cumsum()

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.DataFrame.cumsum.html

Another way of doing it

import pandas as pd
df = pd.DataFrame({'C1' : ['a','a','a','b','b'],
           'C2' : [1,2,3,4,5]})
df['cumsum'] = df.groupby(by=['C1'])['C2'].transform(lambda x: x.cumsum())
df

Answer 5

Instead of df.groupby(by=['name','day']).sum().groupby(level=[0]).cumsum() (see above) you could also do a df.set_index(['name', 'day']).groupby(level=0, as_index=False).cumsum()

• df.groupby(by=['name','day']).sum() is actually just moving both columns to a MultiIndex
• as_index=False means you do not need to call reset_index afterwards

Answer 6

data.csv:

name,day,no
Jack,Monday,10
Jack,Tuesday,20
Jack,Tuesday,10
Jack,Wednesday,50
Jill,Monday,40
Jill,Wednesday,110

Code:

import numpy as np
import pandas as pd

df = pd.read_csv('data.csv')
print(df)
df = df.groupby(['name', 'day'])['no'].sum().reset_index()
print(df)
df['cumsum'] = df.groupby(['name'])['no'].apply(lambda x: x.cumsum())
print(df)

Output:

   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   20
2  Jack    Tuesday   10
3  Jack  Wednesday   50
4  Jill     Monday   40
5  Jill  Wednesday  110
   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   30
2  Jack  Wednesday   50
3  Jill     Monday   40
4  Jill  Wednesday  110
   name        day   no  cumsum
0  Jack     Monday   10      10
1  Jack    Tuesday   30      40
2  Jack  Wednesday   50      90
3  Jill     Monday   40      40
4  Jill  Wednesday  110     150

Answer 7

as of version 1.0 pandas got a new api for window functions.

specifically, what was achieved earlier with
df.groupby(['name'])['no'].apply(lambda x: x.cumsum())
or
df.set_index(['name', 'day']).groupby(level=0, as_index=False).cumsum()

now becomes
df.groupby(['name'])['no'].expanding().sum()

i find it more intuitive for all window-related functions than groupby+level operations

although learning to use groupby is useful for general purpose.
see docs: https://pandas.pydata.org/docs/user_guide/window.html

Answer 8

If you want to write a one-liner (perhaps you want to pass the methods into a pipeline), you can do so by first setting as_index parameter of groupby method to False to return a dataframe from the aggregation step and use assign() to assign a new column to it (the cumulative sum for each person).

These chained methods return a new dataframe, so you'll need to assign it to a variable (eg agg_df ) to be able to use it later on.

agg_df = (
    # aggregate df by name and day
    df.groupby(['name','day'], as_index=False)['no'].sum()
    .assign(
        # assign the cumulative sum of each name as a new column
        cumulative_sum=lambda x: x.groupby('name')['no'].cumsum()
    )
)