pandas DataFrame cumprod

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08/08/2021 pandas.DataFrame.cumprod — pandas 1.3.1 documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumprod.html 1/2
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pandas.DataFrame.cumprod
DataFrame.cumprod(axis=None, skipna=True, *args, **kwargs)
Return cumulative product over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative product.
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 : 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: Series or DataFrame
Return cumulative product of Series or DataFrame.
core.window.Expanding.prod
Similar functionality but ignores NaN values.
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
See also
Search the docs ...
pandas.DataFrame.any
pandas.DataFrame.clip
pandas.DataFrame.corr
pandas.DataFrame.corrwith
pandas.DataFrame.count
pandas.DataFrame.cov
pandas.DataFrame.cummax
pandas.DataFrame.cummin
pandas.DataFrame.cumprod
pandas.DataFrame.cumsum
pandas.DataFrame.describe
pandas.DataFrame.diff
pandas.DataFrame.eval
pandas.DataFrame.kurt
pandas.DataFrame.kurtosis
pandas.DataFrame.mad
pandas.DataFrame.max
pandas.DataFrame.mean
pandas.DataFrame.median
pandas.DataFrame.min
pandas.DataFrame.mode
pandas.DataFrame.pct_change
pandas.DataFrame.prod
pandas.DataFrame.product
pandas.DataFrame.quantile
pandas.DataFrame.rank
pandas.DataFrame.round
pandas.DataFrame.sem
pandas.DataFrame.skew
pandas.DataFrame.sum
pandas.DataFrame.std
pandas.DataFrame.var
pandas.DataFrame.nunique
pandas.DataFrame.value_counts
pandas.DataFrame.add_prefix
pandas.DataFrame.add_suffix
pandas.DataFrame.align
pandas.DataFrame.at_time
pandas.DataFrame.between_time
pandas.DataFrame.drop
pandas.DataFrame.drop_duplicat
pandas.DataFrame.duplicated
pandas.DataFrame.equals
pandas.DataFrame.filter
pandas.DataFrame.first
https://github.com/pandas-dev/pandas/blob/v1.3.1/pandas/core/generic.py#L10665-L10675
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.prod.html#pandas.DataFrame.prod
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cummax.html#pandas.DataFrame.cummax
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cummin.html#pandas.DataFrame.cummin
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumsum.html#pandas.DataFrame.cumsum
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.any.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.clip.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corrwith.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.count.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cov.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cummax.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cummin.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumsum.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.diff.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.eval.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.kurt.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.kurtosis.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mad.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.max.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mean.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.median.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.min.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mode.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pct_change.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.prod.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.product.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rank.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.round.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sem.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.skew.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.var.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.value_counts.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.add_prefix.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.add_suffix.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.align.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at_time.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.between_time.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.equals.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.filter.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.first.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html
08/08/2021 pandas.DataFrame.cumprod — pandas 1.3.1 documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumprod.html 2/2
© Copyright 2008-2021, the pandas development team. 
Created using Sphinx 3.5.4. 
<< pandas.DataFrame.cummin pandas.DataFrame.cumsum >>
>>> 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'.
>>> 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>>> df.cumprod(axis=1) 
 A B 
0 2.0 2.0 
1 3.0 NaN 
2 1.0 0.0 
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