dask_expr._collection.Index.cummax
dask_expr._collection.Index.cummax¶
- Index.cummax(axis=0, skipna=True)¶
Return cumulative maximum over a DataFrame or Series axis.
This docstring was copied from pandas.core.frame.DataFrame.cummax.
Some inconsistencies with the Dask version may exist.
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’. For Series this parameter is unused and defaults to 0.
- skipnabool, 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 maximum of Series or DataFrame.
See also
core.window.expanding.Expanding.max
Similar functionality but ignores
NaN
values.DataFrame.max
Return the maximum 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.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