cudf.DataFrame.groupby#
- DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=False, group_keys=True, squeeze=False, 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
- bymapping, 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 a cupy array is passed, the values are used as-is 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.
- 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 False
Sort result by group key. Differ from Pandas, cudf defaults to
False
for better performance. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.- dropnabool, optional
If True (default), do not include the “null” group.
- Returns
- DataFrameGroupBy
Returns a groupby object that contains information about the groups.
Examples
>>> import cudf >>> import pandas as pd >>> df = cudf.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
>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'], ... ['Captive', 'Wild', 'Captive', 'Wild']] >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type')) >>> df = cudf.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 Wild 185.0 Captive 210.0