Series.min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)[source]

Return the minimum of the values for the requested axis.

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

axis{index (0)}

Axis for the function to be applied on.

skipnabool, default True

Exclude NA/null values when computing the result.

levelint or level name, default None

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

numeric_onlybool, default None

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


Additional keyword arguments to be passed to the function.

scalar or Series (if level specified)
See Also
Series.sumReturn the sum.
Series.minReturn the minimum.
Series.maxReturn the maximum.
Series.idxminReturn the index of the minimum.
Series.idxmaxReturn the index of the maximum.
DataFrame.sumReturn the sum over the requested axis.
DataFrame.minReturn the minimum over the requested axis.
DataFrame.maxReturn the maximum over the requested axis.
DataFrame.idxminReturn the index of the minimum over the requested axis.
DataFrame.idxmaxReturn the index of the maximum over the requested axis.


>>> 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()

Min using level names, as well as indices.

>>> s.min(level='blooded')
warm    2
cold    0
Name: legs, dtype: int64
>>> s.min(level=0)
warm    2
cold    0
Name: legs, dtype: int64
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