pandas.DataFrame.groupby

Group DataFrame or Series 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.

param 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 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.

param axis

{0 or ‘index’, 1 or ‘columns’}, default 0

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

param level

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

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

param 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.

param 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.

param group_keys

bool, default True

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

param squeeze

bool, default False

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

param 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.

New in version 0.23.0.

**kwargs Optional, only accepts keyword argument ‘mutated’ and is passed to groupby.

return

DataFrameGroupBy or SeriesGroupBy Depends on the calling object and returns groupby object that contains information about the groups.

Limitations

• Parameters axis, level, as_index, group_keys, squeeze and observed are currently unsupported by Intel Scalable Dataframe Compiler

• Parameter by is supported as single literal column name only

• Mutating the contents of a DataFrame between creating a groupby object and calling it’s methods is unsupported

Examples

Groupby and calculate the minimum in each group.

import pandas as pd
from numba import njit


@njit
def df_groupby_min():
    df = pd.DataFrame({'A': [1, 2, 3, 1, 2, 3, 3, 3, 2],
                       'B': [0, 1, 5, 0, 2, 4, 3, 2, 3],
                       'C': [1, 2, 3, 4, 5, 6, 7, 8, 9]})
    out_df = df.groupby('A').min()

    # Expect DataFrame of
    # {'B': [0, 1, 2], 'C': [1, 2, 3} with index=[1, 2, 3]
    return out_df


print(df_groupby_min())

$ python ./dataframe/groupby/dataframe_groupby_min.py
   B  C
1  0  1
2  1  2
3  2  3

See also

resample

Resample time-series data.