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.
skipnabool, 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()
  2.0
  NaN
  7.0
  6.0
  6.0
dtype: float64

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

>>> s.cumsum(skipna=False)
  2.0
  NaN
  NaN
  NaN
  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

pandas.Series.cumprod

pandas.Series.describe