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

dask.dataframe.groupby.DataFrameGroupBy.ffill

dask.dataframe.groupby.DataFrameGroupBy.max