Cumulative sum of a column in Pandas – Python

Cumulative sum of a column in Pandas can be easily calculated with the use of a pre-defined function cumsum(). 

Syntax:  cumsum(axis=None, skipna=True, *args, **kwargs)
Parameters:
axis: {index (0), columns (1)} 
skipna: Exclude NA/null values. If an entire row/column is NA, the result will be NA
Returns: Cumulative sum of the column

Example 1:

Python3

import pandas as pd
import numpy as np

# Create a dataframe
df1 = pd.DataFrame({"A":[2, 3, 8, 14],
                   "B":[1, 2, 4, 3],
                   "C":[5, 3, 9,2]})

# Computing sum over Index axis
print(df1.cumsum(axis = 0))

Output:

    A   B   C
0   2   1   5
1   5   3   8
2  13   7  17
3  27  10  19

Example 2:

Python3

import pandas as pd
import numpy as np

# Create a dataframe
df1 = pd.DataFrame({"A":[None, 3, 8, 14],
                   "B":[1, None, 4, 3],
                   "C":[5, 3, 9,None]})

# Computing sum over Index axis
print(df1.cumsum(axis = 0, skipna = True))

Output:

      A    B     C
0   NaN  1.0   5.0
1   3.0  NaN   8.0
2  11.0  5.0  17.0
3  25.0  8.0   NaN

Example 3:

Python3

import pandas as pd
import numpy as np

# Create a dataframe
df1 = pd.DataFrame({"A":[2, 3, 8, 14],
                   "B":[1, 2, 4, 3],
                   "C":[5, 3, 9,2]})

# Computing sum over Index axis
print(df1.cumsum(axis = 1))

Output:

    A   B   C
0   2   3   8
1   3   5   8
2   8  12  21
3  14  17  19