pandas.MultiIndex

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

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.

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 representing the length of each level in the MultiIndex.
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.

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.