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# pandas.MultiIndex

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

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

  A MultiIndex allows indexing with multiple keys, effectively enabling multiple dimensions of indexing on a single axis. It can be thought of as an array of tuples where each tuple is unique.

  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.

    name Label

      Kept for compatibility with 1-dimensional Index. Should not be used.

    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.
  `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.