pandas.core.groupby.SeriesGroupBy.__iter__

SeriesGroupBy.__iter__()

Groupby iterator.

Returns:

Generator yielding sequence of (name, subsetted object)

for each group

Examples

For SeriesGroupBy:

>>> lst = ['a', 'a', 'b']
>>> ser = pd.Series([1, 2, 3], index=lst)
>>> ser
a    1
a    2
b    3
dtype: int64
>>> for x, y in ser.groupby(level=0):
...     print(f'{x}\n{y}\n')
a
a    1
a    2
dtype: int64
b
b    3
dtype: int64

For DataFrameGroupBy:

>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
>>> df = pd.DataFrame(data, columns=["a", "b", "c"])
>>> df
   a  b  c
0  1  2  3
1  1  5  6
2  7  8  9
>>> for x, y in df.groupby(by=["a"]):
...     print(f'{x}\n{y}\n')
(1,)
   a  b  c
0  1  2  3
1  1  5  6
(7,)
   a  b  c
2  7  8  9

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
>>> for x, y in ser.resample('MS'):
...     print(f'{x}\n{y}\n')
2023-01-01 00:00:00
2023-01-01    1
2023-01-15    2
dtype: int64
2023-02-01 00:00:00
2023-02-01    3
2023-02-15    4
dtype: int64