pandas.Series.cumprod#

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

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulativeproduct.

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’.ForSeries this parameter is unused and defaults to 0.

skipnabool, default True

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

*args, **kwargs

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

Returns:

scalar or Series

Return cumulative product of scalar or Series.

See also

core.window.expanding.Expanding.prod

Similar functionality but ignoresNaN values.

Series.prod

Return the product 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])>>>s0    2.01    NaN2    5.03   -1.04    0.0dtype: float64

By default, NA values are ignored.

>>>s.cumprod()0     2.01     NaN2    10.03   -10.04    -0.0dtype: float64

To include NA values in the operation, useskipna=False

>>>s.cumprod(skipna=False)0    2.01    NaN2    NaN3    NaN4    NaNdtype: float64

DataFrame

>>>df=pd.DataFrame([[2.0,1.0],...[3.0,np.nan],...[1.0,0.0]],...columns=list('AB'))>>>df     A    B0  2.0  1.01  3.0  NaN2  1.0  0.0

By default, iterates over rows and finds the productin each column. This is equivalent toaxis=None oraxis='index'.

>>>df.cumprod()     A    B0  2.0  1.01  6.0  NaN2  6.0  0.0

To iterate over columns and find the product in each row,useaxis=1

>>>df.cumprod(axis=1)     A    B0  2.0  2.01  3.0  NaN2  1.0  0.0