pandas.core.groupby.DataFrameGroupBy.get_group#

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