pandas.Series.cumsum

Series.cumsum(axis=None, skipna=True, *args, **kwargs)[source]

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters:

axis:{0 or ‘index’, 1 or ‘columns’}, default 0

The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.

skipna:bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

*args, **kwargs

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

scalar or Series

Return cumulative sum of scalar or Series.

See also

core.window.expanding.Expanding.sum

Similar functionality but ignores NaN values.

Series.sum

Return the sum over Series axis.

Series.cummax

Return cumulative maximum over Series axis.

Series.cummin

Return cumulative minimum over Series axis.

Series.cumsum

Return cumulative sum over Series axis.

Series.cumprod

Return cumulative product over Series axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                   columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0