pandas.core.groupby.DataFrameGroupBy.get_group — pandas 2.2.3 documentation (original) (raw)

DataFrameGroupBy.get_group(name, obj=None)[source]#

Construct DataFrame from group with provided name.

Parameters:

nameobject

The name of the group to get as a DataFrame.

objDataFrame, default None

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