min(self, axis: 'int | None | lib.NoDefault' = <no_default>, skipna=True, level=None, numeric_only=None, **kwargs)

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

Parameters

axis : {index (0)}

Axis for the function to be applied on.

skipna : bool, default True

Exclude NA/null values when computing the result.

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.

numeric_only : bool, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

**kwargs :

Additional keyword arguments to be passed to the function.

Returns

scalar or Series (if level specified)

Return the minimum of the values over the requested axis.

See Also

DataFrame.idxmax

Return the index of the maximum over the requested axis.

DataFrame.idxmin

Return the index of the minimum over the requested axis.

DataFrame.max

Return the maximum over the requested axis.

DataFrame.min

Return the minimum over the requested axis.

DataFrame.sum

Return the sum over the requested axis.

Series.idxmax

Return the index of the maximum.

Series.idxmin

Return the index of the minimum.

Series.max

Return the maximum.

Series.min

Return the minimum.

Series.sum

Return the sum.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> s.min()
0

See :

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File: /pandas/core/generic.py#11217
type: <class 'function'>
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