BPt.Dataset.min

Dataset.min(axis=_NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)

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.

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 Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

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.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.

**kwargs

Additional keyword arguments to be passed to the function.

Returns

Series or DataFrame (if level specified)

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