# dask.dataframe.Series.groupby

Series.groupby(by=None, group_keys=True, sort=None, observed=None, dropna=None, **kwargs)

  Group Series using a mapper or by a Series of columns.

  This docstring was copied from pandas.core.series.Series.groupby.

  Some inconsistencies with the Dask version may exist.

  A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

  Parameters:
    by mapping, function, label, pd.Grouper or list of such

      Used to determine the groups for the groupby. If `by` is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see `.align()` method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in `self`. Notice that a tuple is interpreted as a (single) key.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask)

      Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

      Deprecated since version 2.1.0: Will be removed and behave like axis=0 in a future version. For `axis=1`, do `frame.T.groupby(...)` instead.

    level int, level name, or sequence of such, default None (Not supported in Dask)

      If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both `by` and `level`.

    as_index bool, default True (Not supported in Dask)

      Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations (see the filtrations in the user guide), such as `head()`, `tail()`, `nth()` and in transformations (see the transformations in the user guide).

    sort bool, default True

      Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see the filtrations in the user guide), such as `head()`, `tail()`, `nth()` and in transformations (see the transformations in the user guide).

      Changed in version 2.0.0: Specifying `sort=False` with an ordered categorical grouper will no longer sort the values.

    group_keys bool, default True

      When calling apply and the `by` argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

      Changed in version 1.5.0: Warns that `group_keys` will no longer be ignored when the result from `apply` is a like-indexed Series or DataFrame. Specify `group_keys` explicitly to include the group keys or not.

      Changed in version 2.0.0: `group_keys` now defaults to `True`.

    observed bool, default False

      This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

      Deprecated since version 2.1.0: The default value will change to True in a future version of pandas.

    dropna bool, default True

      If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

  Returns:
    pandas.api.typing.SeriesGroupBy

      Returns a groupby object that contains information about the groups.

  See also

  `resample`

    Convenience method for frequency conversion and resampling of time series.

  Notes

  See the user guide for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

  Examples

      >>> ser = pd.Series([390., 350., 30., 20.],
      ...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'],
      ...                 name="Max Speed")
      >>> ser
      Falcon    390.0
      Falcon    350.0
      Parrot     30.0
      Parrot     20.0
      Name: Max Speed, dtype: float64
      >>> ser.groupby(["a", "b", "a", "b"]).mean()
      a    210.0
      b    185.0
      Name: Max Speed, dtype: float64
      >>> ser.groupby(level=0).mean()
      Falcon    370.0
      Parrot     25.0
      Name: Max Speed, dtype: float64
      >>> ser.groupby(ser > 100).mean()
      Max Speed
      False     25.0
      True     370.0
      Name: Max Speed, dtype: float64

  Grouping by Indexes

  We can groupby different levels of a hierarchical index using the level parameter:

      >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
      ...           ['Captive', 'Wild', 'Captive', 'Wild']]
      >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
      >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
      >>> ser
      Animal  Type
      Falcon  Captive    390.0
              Wild       350.0
      Parrot  Captive     30.0
              Wild        20.0
      Name: Max Speed, dtype: float64
      >>> ser.groupby(level=0).mean()
      Animal
      Falcon    370.0
      Parrot     25.0
      Name: Max Speed, dtype: float64
      >>> ser.groupby(level="Type").mean()
      Type
      Captive    210.0
      Wild       185.0
      Name: Max Speed, dtype: float64

  We can also choose to include NA in group keys or not by defining dropna parameter, the default setting is True.

      >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
      >>> ser.groupby(level=0).sum()
      a    3
      b    3
      dtype: int64

      >>> ser.groupby(level=0, dropna=False).sum()
      a    3
      b    3
      NaN  3
      dtype: int64

      >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
      >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
      >>> ser.groupby(["a", "b", "a", np.nan]).mean()
      a    210.0
      b    350.0
      Name: Max Speed, dtype: float64

      >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
      a    210.0
      b    350.0
      NaN   20.0
      Name: Max Speed, dtype: float64