get_groupby(obj: 'NDFrame', by: '_KeysArgType | None' = None, axis: 'int' = 0, level=None, grouper: 'ops.BaseGrouper | None' = None, exclusions=None, selection=None, as_index: 'bool' = True, sort: 'bool' = True, group_keys: 'bool' = True, squeeze: 'bool' = False, observed: 'bool' = False, mutated: 'bool' = False, dropna: 'bool' = True) -> 'GroupBy'
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, ...)
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, axis=axis)
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:
df.groupby(mapper).std()
rather than
df.groupby(mapper).aggregate(np.std)
You can pass arguments to these "wrapped" functions, too.
See the online documentation for full exposition on these topics and much more
Level of MultiIndex
Most users should ignore this
List of columns to exclude
Most users should ignore this
{group name -> group labels}
Number of groups
Class for grouping and aggregating relational data.
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