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# pandas.api.typing.DataFrameGroupBy.__iter__

DataFrameGroupBy.__iter__()

  Groupby iterator.

  This method provides an iterator over the groups created by the `resample` or `groupby` operation on the object. The method yields tuples where the first element is the label (group key) corresponding to each group or resampled bin, and the second element is the subset of the data that falls within that group or bin.

  Returns:
    Iterator

      Generator yielding a sequence of (name, subsetted object) for each group.

  See also

  `Series.groupby`

    Group data by a specific key or column.

  `DataFrame.groupby`

    Group DataFrame using mapper or by columns.

  `DataFrame.resample`

    Resample a DataFrame.

  `Series.resample`

    Resample a Series.

  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