dask.dataframe.groupby.DataFrameGroupBy.get_group

dask.dataframe.groupby.DataFrameGroupBy.get_group

DataFrameGroupBy.get_group(key)

Construct DataFrame from group with provided name.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.get_group.

Some inconsistencies with the Dask version may exist.

Known inconsistencies:

If the group is not present, Dask will return an empty Series/DataFrame.

Parameters
nameobject (Not supported in Dask)

The name of the group to get as a DataFrame.

objDataFrame, default None (Not supported in Dask)

The DataFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used.

Deprecated since version 2.1.0: The obj is deprecated and will be removed in a future version. Do df.iloc[gb.indices.get(name)] instead of gb.get_group(name, obj=df).

Returns
same type as obj

Examples

For SeriesGroupBy:

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

For DataFrameGroupBy:

>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]  
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],  
...                   index=["owl", "toucan", "eagle"])
>>> df  
        a  b  c
owl     1  2  3
toucan  1  5  6
eagle   7  8  9
>>> df.groupby(by=["a"]).get_group((1,))  
        a  b  c
owl     1  2  3
toucan  1  5  6

For Resampler:

>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(  
...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser  
2023-01-01    1
2023-01-15    2
2023-02-01    3
2023-02-15    4
dtype: int64
>>> ser.resample('MS').get_group('2023-01-01')  
2023-01-01    1
2023-01-15    2
dtype: int64