pandas.MultiIndex#

class pandas.MultiIndex(levels=None, codes=None, sortorder=None, names=None, dtype=None, copy=False, name=None, verify_integrity=True)[source]#

A multi-level, or hierarchical, index object for pandas objects.

Parameters:

levelssequence of arrays: The unique labels for each level.
codessequence of arrays: Integers for each level designating which label at each location.
sortorderoptional int: Level of sortedness (must be lexicographically sorted by that level).
namesoptional sequence of objects: Names for each of the index levels. (name is accepted for compat).
dtypeNumpy dtype or pandas type, optional: Data type for the MultiIndex.
copybool, default False: Copy the meta-data.
nameLabel: Kept for compatibility with 1-dimensional Index. Should not be used.
verify_integritybool, default True: Check that the levels/codes are consistent and valid.

See also

MultiIndex.from_arrays: Convert list of arrays to MultiIndex.
MultiIndex.from_product: Create a MultiIndex from the cartesian product of iterables.
MultiIndex.from_tuples: Convert list of tuples to a MultiIndex.
MultiIndex.from_frame: Make a MultiIndex from a DataFrame.
Index: The base pandas Index type.

Notes

See the user guide for more.

Examples

A new MultiIndex is typically constructed using one of the helper methods MultiIndex.from_arrays(), MultiIndex.from_product() and MultiIndex.from_tuples(). For example (using .from_arrays):

>>> arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]]
>>> pd.MultiIndex.from_arrays(arrays, names=("number", "color"))
MultiIndex([(1,  'red'),
            (1, 'blue'),
            (2,  'red'),
            (2, 'blue')],
           names=['number', 'color'])

See further examples for how to construct a MultiIndex in the doc strings of the mentioned helper methods.

Attributes

`names`	Names of levels in MultiIndex.
`levels`	Levels of the MultiIndex.
`codes`	Codes of the MultiIndex.
`nlevels`	Integer number of levels in this MultiIndex.
`levshape`	A tuple with the length of each level.
`dtypes`	Return the dtypes as a Series for the underlying MultiIndex.

Methods

`from_arrays`(arrays[, sortorder, names])	Convert arrays to MultiIndex.
`from_tuples`(tuples[, sortorder, names])	Convert list of tuples to MultiIndex.
`from_product`(iterables[, sortorder, names])	Make a MultiIndex from the cartesian product of multiple iterables.
`from_frame`(df[, sortorder, names])	Make a MultiIndex from a DataFrame.
`set_levels`(levels, *[, level, verify_integrity])	Set new levels on MultiIndex.
`set_codes`(codes, *[, level, verify_integrity])	Set new codes on MultiIndex.
`to_frame`([index, name, allow_duplicates])	Create a DataFrame with the levels of the MultiIndex as columns.
`to_flat_index`()	Convert a MultiIndex to an Index of Tuples containing the level values.
`sortlevel`([level, ascending, ...])	Sort MultiIndex at the requested level.
`droplevel`([level])	Return index with requested level(s) removed.
`swaplevel`([i, j])	Swap level i with level j.
`reorder_levels`(order)	Rearrange levels using input order.
`remove_unused_levels`()	Create new MultiIndex from current that removes unused levels.
`get_level_values`(level)	Return vector of label values for requested level.
`get_indexer`(target[, method, limit, tolerance])	Compute indexer and mask for new index given the current index.
`get_loc`(key)	Get location for a label or a tuple of labels.
`get_locs`(seq)	Get location for a sequence of labels.
`get_loc_level`(key[, level, drop_level])	Get location and sliced index for requested label(s)/level(s).
`drop`(codes[, level, errors])	Make a new `pandas.MultiIndex` with the passed list of codes deleted.