pandas.core.resample.Resampler.__iter__

Resampler.__iter__()

Groupby 迭代器。

返回:

生成器生成一系列 (名称, 子集对象)

对于每个组

例子

对于 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

对于 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

对于重采样器：

>>> 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