xorbits.pandas.DataFrame.min#

DataFrame.min(axis=None, skipna=True, level=None, numeric_only=None, combine_size=None, method=None)#

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters

  • axis ({index (0), columns (1)}) –

    Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

    For DataFrames, specifying axis=None will apply the aggregation across both axes.

    New in version 2.0.0(pandas).

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Return type

Series or scalar

See also

Series.sum

Return the sum.

Series.min

Return the minimum.

Series.max

Return the maximum.

Series.idxmin

Return the index of the minimum.

Series.idxmax

Return the index of the maximum.

DataFrame.sum

Return the sum over the requested axis.

DataFrame.min

Return the minimum over the requested axis.

DataFrame.max

Return the maximum over the requested axis.

DataFrame.idxmin

Return the index of the minimum over the requested axis.

DataFrame.idxmax

Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([  
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)  
>>> s  
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()  
0

This docstring was copied from pandas.core.frame.DataFrame.