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pandas.DataFrame.cumsum

DataFrame.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’.

skipna : boolean, 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:
cumsum : Series or DataFrame

See also

core.window.Expanding.sum
Similar functionality but ignores NaN values.
DataFrame.sum
Return the sum over DataFrame axis.
DataFrame.cummax
Return cumulative maximum over DataFrame axis.
DataFrame.cummin
Return cumulative minimum over DataFrame axis.
DataFrame.cumsum
Return cumulative sum over DataFrame axis.
DataFrame.cumprod
Return cumulative product over DataFrame 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
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