pandas.Series.cummax¶
-
Series.
cummax
(axis=None, skipna=True, *args, **kwargs)[source]¶ Return cumulative maximum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative maximum.
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 : boolean, 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: - cummax : scalar or Series
See also
pandas.core.window.Expanding.max
- Similar functionality but ignores
NaN
values. Series.max
- Return the maximum 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.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
oraxis='index'
.>>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0
To iterate over columns and find the maximum in each row, use
axis=1
>>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0