pandas.MultiIndex

classpandas.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:

levels:sequence of arrays

The unique labels for each level.

codes:sequence of arrays

Integers for each level designating which label at each location.

sortorder:optional int

Level of sortedness (must be lexicographically sorted by that level).

names:optional sequence of objects

Names for each of the index levels. (name is accepted for compat).

copy:bool, default False

Copy the meta-data.

verify_integrity:bool, default True

Check that the levels/codes are consistent and valid.

Attributes

names	Names of levels in MultiIndex.
levels	Levels 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.

codes

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.