modin.pandas.Series.cummax

Series.cummax(axis=None, skipna=True, *args, **kwargs) → Self[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.47.0/.tox/docs/lib/python3.9/site-packages/modin/pandas/base.py#L1328-L1343)

Return cumulative maximum over a BasePandasDataset axis.

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.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • *args – Additional keywords have no effect but might be accepted for compatibility with NumPy.

  • **kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Return cumulative maximum of Series or DataFrame.

Return type:

Series or DataFrame

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.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

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

>>> s.cummax(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 maximum in each column. This is equivalent to axis=None or axis=’index’.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0