pandas.Panel.min¶
-
Panel.
min
(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, useidxmin
. This is the equivalent of thenumpy.ndarray
methodargmin
.Parameters: - axis : {items (0), major_axis (1), minor_axis (2)}
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 DataFrame.
- 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: - min : DataFrame or Panel (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.min
- 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
Min using level names, as well as indices.
>>> s.min(level='blooded') blooded warm 2 cold 0 Name: legs, dtype: int64
>>> s.min(level=0) blooded warm 2 cold 0 Name: legs, dtype: int64