xorbits.pandas.groupby.DataFrameGroupBy.min#

DataFrameGroupBy.min(**kw)#

Compute min of group values.

Parameters
  • numeric_only (bool, default False (Not supported yet)) –

    Include only float, int, boolean columns.

    Changed in version 2.0.0(pandas): numeric_only no longer accepts None.

  • min_count (int, default -1 (Not supported yet)) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

  • engine (str, default None None (Not supported yet)) –

    • 'cython' : Runs rolling apply through C-extensions from cython.

    • 'numba'Runs rolling apply through JIT compiled code from numba.

      Only available when raw is set to True.

    • None : Defaults to 'cython' or globally setting compute.use_numba

  • engine_kwargs (dict, default None None (Not supported yet)) –

    • For 'cython' engine, there are no accepted engine_kwargs

    • For 'numba' engine, the engine can accept nopython, nogil

      and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} and will be applied to both the func and the apply groupby aggregation.

Returns

Computed min of values within each group.

Return type

Series or DataFrame

Examples

For SeriesGroupBy:

>>> lst = ['a', 'a', 'b', 'b']  
>>> ser = pd.Series([1, 2, 3, 4], index=lst)  
>>> ser  
a    1
a    2
b    3
b    4
dtype: int64
>>> ser.groupby(level=0).min()  
a    1
b    3
dtype: int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]  
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],  
...                   index=["tiger", "leopard", "cheetah", "lion"])
>>> df  
          a  b  c
  tiger   1  8  2
leopard   1  2  5
cheetah   2  5  8
   lion   2  6  9
>>> df.groupby("a").min()  
    b  c
a
1   2  2
2   5  8

This docstring was copied from pandas.core.groupby.generic.DataFrameGroupBy.