Function get_groupby

pandas/core/groupby/groupby.py:6167–6257

Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas obje

(
    obj: NDFrame,
    by: _KeysArgType | None = None,
    grouper: ops.BaseGrouper | None = None,
    group_keys: bool = True,
)

def get_groupby(
    obj: NDFrame,
    by: _KeysArgType | None = None,
    grouper: ops.BaseGrouper | None = None,
    group_keys: bool = True,
) -> GroupBy:
    """
    Class for grouping and aggregating relational data.

    See aggregate, transform, and apply functions on this object.

    It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:

    ::

        grouped = groupby(obj, ...)

    Parameters
    ----------
    obj : pandas object
    level : int, default None
        Level of MultiIndex
    groupings : list of Grouping objects
        Most users should ignore this
    exclusions : array-like, optional
        List of columns to exclude
    name : str
        Most users should ignore this

    Returns
    -------
    **Attributes**
    groups : dict
        {group name -> group labels}
    len(grouped) : int
        Number of groups

    Notes
    -----
    After grouping, see aggregate, apply, and transform functions. Here are
    some other brief notes about usage. When grouping by multiple groups, the
    result index will be a MultiIndex (hierarchical) by default.

    Iteration produces (key, group) tuples, i.e. chunking the data by group. So
    you can write code like:

    ::

        grouped = obj.groupby(keys)
        for key, group in grouped:
            # do something with the data

    Function calls on GroupBy, if not specially implemented, "dispatch" to the
    grouped data. So if you group a DataFrame and wish to invoke the std()
    method on each group, you can simply do:

    ::