dask.dataframe.DataFrame.min
dask.dataframe.DataFrame.min¶
- DataFrame.min(axis=0, skipna=True, split_every=False, out=None, numeric_only=None)¶
Return the minimum of the values over the requested axis.
This docstring was copied from pandas.core.frame.DataFrame.min.
Some inconsistencies with the Dask version may exist.
If you want the index of the minimum, use
idxmin
. This is the equivalent of thenumpy.ndarray
methodargmin
.- 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.
- skipnabool, default True
Exclude NA/null values when computing the result.
- numeric_onlybool, default False
Include only float, int, boolean columns. Not implemented for Series.
- **kwargs
Additional keyword arguments to be passed to the function.
- Returns
- 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