ENH: add MultiIndex.to_dataframe

ENH: allow hashing of MultiIndex closes #12397

Author: jreback

doc/source/api.rst

@@ -1460,6 +1460,7 @@ MultiIndex Components
  MultiIndex.set_levels
  MultiIndex.set_labels
  MultiIndex.to_hierarchical
+ MultiIndex.to_dataframe
  MultiIndex.is_lexsorted
  MultiIndex.droplevel
  MultiIndex.swaplevel

doc/source/whatsnew/v0.20.0.txt

@@ -131,7 +131,7 @@ Other enhancements
 - New ``UnsortedIndexError`` (subclass of ``KeyError``) raised when indexing/slicing into an
  unsorted MultiIndex (:issue:`11897`). This allows differentiation between errors due to lack
  of sorting or an incorrect key. See :ref:`here <advanced.unsorted>`
-
+- ``MultiIndex`` has gained a ``.to_dataframe()`` method to convert to a ``DataFrame`` (:issue:`12397`)
 - ``pd.cut`` and ``pd.qcut`` now support datetime64 and timedelta64 dtypes (:issue:`14714`, :issue:`14798`)
 - ``pd.qcut`` has gained the ``duplicates='raise'|'drop'`` option to control whether to raise on duplicated edges (:issue:`7751`)
 - ``Series`` provides a ``to_excel`` method to output Excel files (:issue:`8825`)

pandas/indexes/multi.py

@@ -827,6 +827,30 @@ def _to_safe_for_reshape(self):
  """ convert to object if we are a categorical """
  return self.set_levels([i._to_safe_for_reshape() for i in self.levels])
 
+ def to_dataframe(self, index=True):
+ """
+ Create a DataFrame with the columns the levels of the MultiIndex
+
+ .. versionadded:: 0.20.0
+
+ Parameters
+ ----------
+ index : boolean, default True
+ return this MultiIndex as the index
+
+ Returns
+ -------
+ DataFrame
+ """
+
+ from pandas import DataFrame
+ result = DataFrame({(name or level): self.get_level_values(level)
+ for name, level in
+ zip(self.names, range(len(self.levels)))})
+ if index:
+ result.index = self
+ return result
+
  def to_hierarchical(self, n_repeat, n_shuffle=1):
  """
  Return a MultiIndex reshaped to conform to the

pandas/tests/indexes/test_multi.py

@@ -1348,6 +1348,43 @@ def test_format_sparse_config(self):
 
  warnings.filters = warn_filters
 
+ def test_to_dataframe(self):
+ tuples = [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')]
+
+ index = MultiIndex.from_tuples(tuples)
+ result = index.to_dataframe(index=False)
+ expected = DataFrame(tuples)
+ tm.assert_frame_equal(result, expected)
+
+ result = index.to_dataframe()
+ expected.index = index
+ tm.assert_frame_equal(result, expected)
+
+ tuples = [(1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')]
+ index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
+ result = index.to_dataframe(index=False)
+ expected = DataFrame(tuples)
+ expected.columns = ['first', 'second']
+ tm.assert_frame_equal(result, expected)
+
+ result = index.to_dataframe()
+ expected.index = index
+ tm.assert_frame_equal(result, expected)
+
+ index = MultiIndex.from_product([range(5),
+ pd.date_range('20130101', periods=3)])
+ result = index.to_dataframe(index=False)
+ expected = DataFrame(
+ {0: np.repeat(np.arange(5, dtype='int64'), 3),
+ 1: np.tile(pd.date_range('20130101', periods=3), 5)})
+ tm.assert_frame_equal(result, expected)
+
+ index = MultiIndex.from_product([range(5),
+ pd.date_range('20130101', periods=3)])
+ result = index.to_dataframe()
+ expected.index = index
+ tm.assert_frame_equal(result, expected)
+
  def test_to_hierarchical(self):
  index = MultiIndex.from_tuples([(1, 'one'), (1, 'two'), (2, 'one'), (
  2, 'two')])

pandas/tools/hashing.py

@@ -3,7 +3,7 @@
 """
 
 import numpy as np
-from pandas import _hash, Series, factorize, Categorical, Index
+from pandas import _hash, Series, factorize, Categorical, Index, MultiIndex
 from pandas.lib import is_bool_array
 from pandas.types.generic import ABCIndexClass, ABCSeries, ABCDataFrame
 from pandas.types.common import (is_categorical_dtype, is_numeric_dtype,
@@ -45,6 +45,12 @@ def adder(h, hashed_to_add):
  h = np.multiply(h, np.uint(3), h)
  return np.add(h, hashed_to_add, h)
 
+ if isinstance(obj, MultiIndex):
+ # efficiently turn us into a DataFrame and hash
+ return hash_pandas_object(obj.to_dataframe(index=False),
+ index=False, encoding=encoding,
+ hash_key=hash_key, categorize=categorize)
+
  if isinstance(obj, ABCIndexClass):
  h = hash_array(obj.values, encoding, hash_key,
  categorize).astype('uint64')

pandas/tools/tests/test_hashing.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from pandas import DataFrame, Series, Index
+from pandas import DataFrame, Series, Index, MultiIndex
 from pandas.tools.hashing import hash_array, hash_pandas_object
 import pandas.util.testing as tm
 
@@ -72,7 +72,11 @@ def test_hash_pandas_object(self):
  tm.makeMixedDataFrame(),
  tm.makeTimeDataFrame(),
  tm.makeTimeSeries(),
- tm.makeTimedeltaIndex()]:
+ tm.makeTimedeltaIndex(),
+ MultiIndex.from_product(
+ [range(5),
+ ['foo', 'bar', 'baz'],
+ pd.date_range('20130101', periods=2)])]:
  self.check_equal(obj)
  self.check_not_equal_with_index(obj)
 
@@ -140,14 +144,6 @@ def f():
  hash_pandas_object(obj)
  self.assertRaises(TypeError, f)
 
- # MultiIndex are represented as tuples
- obj = Series([1, 2, 3], index=pd.MultiIndex.from_tuples(
- [('a', 1), ('a', 2), ('b', 1)]))
-
- def f():
- hash_pandas_object(obj)
- self.assertRaises(TypeError, f)
-
  def test_alread_encoded(self):
  # if already encoded then ok