# pandas.DataFrame.groupby

DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=NoDefault.no_default, observed=False, dropna=True)

  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, or list of labels

      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 an ndarray is passed, 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

      Split along rows (0) or columns (1).

    level int, level name, or sequence of such, default None

      If the axis is a MultiIndex (hierarchical), group by a particular level or levels.

    as_index bool, default True

      For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.

    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.

    group_keys bool, default True

      When calling apply, add group keys to index to identify pieces.

    squeeze bool, default False

      Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

      Deprecated since version 1.1.0.

    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.

    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

      New in version 1.1.0.

  Returns
    DataFrameGroupBy

      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.

  Examples

      >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
      ...                               'Parrot', 'Parrot'],
      ...                    'Max Speed': [380., 370., 24., 26.]})
      >>> df
         Animal  Max Speed
      0  Falcon      380.0
      1  Falcon      370.0
      2  Parrot       24.0
      3  Parrot       26.0
      >>> df.groupby(['Animal']).mean()
              Max Speed
      Animal
      Falcon      375.0
      Parrot       25.0

  Hierarchical 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'))
      >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
      ...                   index=index)
      >>> df
                      Max Speed
      Animal Type
      Falcon Captive      390.0
             Wild         350.0
      Parrot Captive       30.0
             Wild          20.0
      >>> df.groupby(level=0).mean()
              Max Speed
      Animal
      Falcon      370.0
      Parrot       25.0
      >>> df.groupby(level="Type").mean()
               Max Speed
      Type
      Captive      210.0
      Wild         185.0

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

      >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
      >>> df = pd.DataFrame(l, columns=["a", "b", "c"])

      >>> df.groupby(by=["b"]).sum()
          a   c
      b
      1.0 2   3
      2.0 2   5

      >>> df.groupby(by=["b"], dropna=False).sum()
          a   c
      b
      1.0 2   3
      2.0 2   5
      NaN 1   4

      >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
      >>> df = pd.DataFrame(l, columns=["a", "b", "c"])

      >>> df.groupby(by="a").sum()
          b     c
      a
      a   13.0   13.0
      b   12.3  123.0

      >>> df.groupby(by="a", dropna=False).sum()
          b     c
      a
      a   13.0   13.0
      b   12.3  123.0
      NaN 12.3   33.0