Method groupby

pandas/core/frame.py:10613–10844

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

(
        self,
        by=None,
        level: IndexLabel | None = None,
        as_index: bool = True,
        sort: bool = True,
        group_keys: bool = True,
        observed: bool = True,
        dropna: bool = True,
    )

        Pandas4Warning, allowed_args=[class="st">"self", class="st">"by", class="st">"level"], name=class="st">"groupby"
    )
    def groupby(
        self,
        by=None,
        level: IndexLabel | None = None,
        as_index: bool = True,
        sort: bool = True,
        group_keys: bool = True,
        observed: bool = True,
        dropna: bool = True,
    ) -> DataFrameGroupBy:
        class="st">"""
        Group DataFrame using a mapper or by a Series of columns.

        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&class="cm">#x27;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&class="cm">#x27; values are first
            aligned; see ``.align()`` method). If a list or ndarray of length
            equal to the number of rows is passed (see the `groupby user guide
            <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.htmlclass="cm">#splitting-an-object-into-groups>`_),
            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.
        level : int, level name, or sequence of such, default None
            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
            Return object with group labels as the
            index. Only relevant for DataFrame input. as_index=False is
            effectively class="st">"SQL-style" grouped output. This argument has no effect
            on filtrations (see the `filtrations in the user guide
            <https://pandas.pydata.org/docs/dev/user_guide/groupby.htmlclass="cm">#filtration>`_),
            such as ``head()``, ``tail()``, ``nth()`` and in transformations
            (see the `transformations in the user guide
            <https://pandas.pydata.org/docs/dev/user_guide/groupby.htmlclass="cm">#transformation>`_).
        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
            <https://pandas.pydata.org/docs/dev/user_guide/groupby.htmlclass="cm">#filtration>`_),
            such as ``head()``, ``tail()``, ``nth()`` and in transformations
            (see the `transformations in the user guide
            <https://pandas.pydata.org/docs/dev/user_guide/groupby.htmlclass="cm">#transformation>`_).

            .. versionchanged:: 2.0.0