4.7.7.1.2.3. statsmodels.tools.grouputils.Grouping¶

class statsmodels.tools.grouputils.Grouping(index, names=None)[source]¶

index: Can be pandas MultiIndex or Index or array-like. If array-like and is a MultipleIndex (more than one grouping variable), groups are expected to be in each row. E.g., [(‘red’, 1), (‘red’, 2), (‘green’, 1), (‘green’, 2)]
names: The names to use for the groups. Should be a str if only one grouping variable is used.

Notes

If index is already a pandas Index then there is no copy.

__init__(index, names=None)[source]¶

index: Can be pandas MultiIndex or Index or array-like. If array-like and is a MultipleIndex (more than one grouping variable), groups are expected to be in each row. E.g., [(‘red’, 1), (‘red’, 2), (‘green’, 1), (‘green’, 2)]
names: The names to use for the groups. Should be a str if only one grouping variable is used.

Notes

If index is already a pandas Index then there is no copy.

4.7.7.1.2.3.1. Methods¶

`__init__`(index[, names])	index : index-like
`check_index`([is_sorted, unique, index])	Sanity checks
`count_categories`([level])	Sets the attribute counts to equal the bincount of the (integer-valued) labels.
`dummies_groups`([level])
`dummies_time`()
`dummy_sparse`([level])	create a sparse indicator from a group array with integer labels
`get_slices`([level])	Sets the slices attribute to be a list of indices of the sorted groups for the first index level.
`reindex`([index, names])	Resets the index in-place.
`sort`(data[, index])	Applies a (potentially hierarchical) sort operation on a numpy array or pandas series/dataframe based on the grouping index or a user-supplied index.
`transform_array`(array, function[, level])	Apply function to each column, by group
`transform_dataframe`(dataframe, function[, level])	Apply function to each column, by group
`transform_slices`(array, function[, level])	Apply function to each group.