pandas.DataFrame.cummin — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

DataFrame.cummin(axis=0, skipna=True, numeric_only=False, *args, **kwargs)[source]#

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

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.

numeric_onlybool, default False

Include only float, int, boolean columns.

*args, **kwargs

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame

Return cumulative minimum of Series or DataFrame.

See also

core.window.expanding.Expanding.min

Similar functionality but ignores NaN values.

Return the minimum over DataFrame axis.

Return cumulative maximum over DataFrame axis.

Return cumulative minimum over DataFrame axis.

Return cumulative sum over DataFrame axis.

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.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64

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

s.cummin(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 minimum in each column. This is equivalent to axis=None or axis='index'.

df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0

To iterate over columns and find the minimum in each row, use axis=1

df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0