DataFrame.
cumprod
Return cumulative product over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative product.
The index or the name of the axis. 0 is equivalent to None or ‘index’.
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
Additional keywords have no effect but might be accepted for compatibility with NumPy.
See also
core.window.Expanding.prod
Similar functionality but ignores NaN values.
NaN
DataFrame.prod
Return the product 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.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64
To include NA values in the operation, use skipna=False
skipna=False
>>> s.cumprod(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 product in each column. This is equivalent to axis=None or axis='index'.
axis=None
axis='index'
>>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0
To iterate over columns and find the product in each row, use axis=1
axis=1
>>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0