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)[source]¶
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
- bymapping, function, label, or list of labels
Used to determine the groups for the groupby. If
byis 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 inself. 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).
- levelint, level name, or sequence of such, default None
If the axis is a MultiIndex (hierarchical), group by a particular level or levels.
- as_indexbool, 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.
- sortbool, 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_keysbool, default True
When calling apply, add group keys to index to identify pieces.
- squeezebool, default False
Reduce the dimensionality of the return type if possible, otherwise return a consistent type.
Deprecated since version 1.1.0.
- observedbool, 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.
- dropnabool, 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
resampleConvenience 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