PERF: high memory in MI

closes #13904

Creates an efficient MultiIndexHashTable in cython.
This allows us to efficiently store a multi-index for fast indexing
(.get_loc() and .get_indexer()), with the current tuple-based
(and gil holding) use of the PyObject Hash Table.

This uses the pandas.tools.hashing routines to hash each of the 'values' of a MI
to a single uint64. So this makes MI more memory friendly and much
more efficient. You get these speedups, because the creation of the
hashtable is now much more efficient.

Author: Jeff Reback <jeff@reback.net>

Closes #15245 from jreback/mi and squashes the following commits:

7df6c34 [Jeff Reback] PERF: high memory in MI

asv_bench/benchmarks/indexing.py

@@ -88,7 +88,7 @@ def setup(self):
 
     def time_getitem_scalar(self):
         self.ts[self.dt]
-    
+
 
 class DataFrameIndexing(object):
     goal_time = 0.2
@@ -189,6 +189,15 @@ def setup(self):
         self.eps_C = 5
         self.eps_D = 5000
         self.mdt2 = self.mdt.set_index(['A', 'B', 'C', 'D']).sortlevel()
+        self.miint = MultiIndex.from_product(
+            [np.arange(1000),
+             np.arange(1000)], names=['one', 'two'])
+
+        import string
+        self.mistring = MultiIndex.from_product(
+            [np.arange(1000),
+             np.arange(20), list(string.ascii_letters)],
+            names=['one', 'two', 'three'])
 
     def time_series_xs_mi_ix(self):
         self.s.ix[999]
@@ -197,7 +206,24 @@ def time_frame_xs_mi_ix(self):
         self.df.ix[999]
 
     def time_multiindex_slicers(self):
-        self.mdt2.loc[self.idx[(self.test_A - self.eps_A):(self.test_A + self.eps_A), (self.test_B - self.eps_B):(self.test_B + self.eps_B), (self.test_C - self.eps_C):(self.test_C + self.eps_C), (self.test_D - self.eps_D):(self.test_D + self.eps_D)], :]
+        self.mdt2.loc[self.idx[
+            (self.test_A - self.eps_A):(self.test_A + self.eps_A),
+            (self.test_B - self.eps_B):(self.test_B + self.eps_B),
+            (self.test_C - self.eps_C):(self.test_C + self.eps_C),
+            (self.test_D - self.eps_D):(self.test_D + self.eps_D)], :]
+
+    def time_multiindex_get_indexer(self):
+        self.miint.get_indexer(
+            np.array([(0, 10), (0, 11), (0, 12),
+                      (0, 13), (0, 14), (0, 15),
+                      (0, 16), (0, 17), (0, 18),
+                      (0, 19)], dtype=object))
+
+    def time_multiindex_string_get_loc(self):
+        self.mistring.get_loc((999, 19, 'Z'))
+
+    def time_is_monotonic(self):
+        self.miint.is_monotonic
 
 
 class PanelIndexing(object):

asv_bench/benchmarks/reindex.py

@@ -16,8 +16,8 @@ def setup(self):
                              data=np.random.rand(10000, 30), columns=range(30))
 
         # multi-index
-        N = 1000
-        K = 20
+        N = 5000
+        K = 200
         level1 = tm.makeStringIndex(N).values.repeat(K)
         level2 = np.tile(tm.makeStringIndex(K).values, N)
         index = MultiIndex.from_arrays([level1, level2])

doc/source/whatsnew/v0.20.0.txt

@@ -472,7 +472,7 @@ Performance Improvements
 - Improved performance of timeseries plotting with an irregular DatetimeIndex
   (or with ``compat_x=True``) (:issue:`15073`).
 - Improved performance of ``groupby().cummin()`` and ``groupby().cummax()`` (:issue:`15048`, :issue:`15109`)
-
+- Improved performance and reduced memory when indexing with a ``MultiIndex`` (:issue:`15245`)
 - When reading buffer object in ``read_sas()`` method without specified format, filepath string is inferred rather than buffer object.
 
 
@@ -502,6 +502,8 @@ Bug Fixes
 - Bug in ``DataFrame.loc`` with indexing a ``MultiIndex`` with a ``Series`` indexer (:issue:`14730`)
 
 
+
+- Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`)
 - Bug in ``pd.read_msgpack()`` in which ``Series`` categoricals were being improperly processed (:issue:`14901`)
 - Bug in ``Series.ffill()`` with mixed dtypes containing tz-aware datetimes. (:issue:`14956`)
 

pandas/core/algorithms.py

@@ -1250,7 +1250,7 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None,
         indexer = np.arange(arr.shape[axis], dtype=np.int64)
         dtype, fill_value = arr.dtype, arr.dtype.type()
     else:
-        indexer = _ensure_int64(indexer)
+        indexer = _ensure_int64(indexer, copy=False)
         if not allow_fill:
             dtype, fill_value = arr.dtype, arr.dtype.type()
             mask_info = None, False
@@ -1303,7 +1303,6 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None,
 
     func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis,
                                  mask_info=mask_info)
-    indexer = _ensure_int64(indexer)
     func(arr, indexer, out, fill_value)
 
     if flip_order:

pandas/core/frame.py

@@ -1752,7 +1752,8 @@ def _sizeof_fmt(num, size_qualifier):
                 # all cases (e.g., it misses categorical data even with object
                 # categories)
                 deep = False
-                if 'object' in counts or is_object_dtype(self.index):
+                if ('object' in counts or
+                        self.index._is_memory_usage_qualified()):
                     size_qualifier = '+'
             mem_usage = self.memory_usage(index=True, deep=deep).sum()
             lines.append("memory usage: %s\n" %

pandas/hashtable.pxd

@@ -31,6 +31,14 @@ cdef class PyObjectHashTable(HashTable):
     cpdef get_item(self, object val)
     cpdef set_item(self, object key, Py_ssize_t val)
 
+cdef class MultiIndexHashTable(HashTable):
+    cdef:
+        kh_uint64_t *table
+        object mi
+
+    cpdef get_item(self, object val)
+    cpdef set_item(self, object key, Py_ssize_t val)
+
 cdef class StringHashTable(HashTable):
     cdef kh_str_t *table
 

pandas/index.pyx

@@ -182,7 +182,7 @@ cdef class IndexEngine:
             Py_ssize_t i, n
             int last_true
 
-        values = self._get_index_values()
+        values = np.array(self._get_index_values(), copy=False)
         n = len(values)
 
         result = np.empty(n, dtype=bool)
@@ -284,7 +284,6 @@ cdef class IndexEngine:
         if not self.is_mapping_populated:
 
             values = self._get_index_values()
-
             self.mapping = self._make_hash_table(len(values))
             self.mapping.map_locations(values)
 
@@ -322,7 +321,7 @@ cdef class IndexEngine:
             Py_ssize_t i, j, n, n_t, n_alloc
 
         self._ensure_mapping_populated()
-        values = self._get_index_values()
+        values = np.array(self._get_index_values(), copy=False)
         stargets = set(targets)
         n = len(values)
         n_t = len(targets)
@@ -554,5 +553,39 @@ cdef inline bint _is_utc(object tz):
     return tz is UTC or isinstance(tz, _du_utc)
 
 
+cdef class MultiIndexEngine(IndexEngine):
+
+    def _call_monotonic(self, object mi):
+        # defer these back to the mi iteself
+        return (mi.is_monotonic_increasing,
+                mi.is_monotonic_decreasing,
+                mi.is_unique)
+
+    def get_backfill_indexer(self, other, limit=None):
+        # we coerce to ndarray-of-tuples
+        values = np.array(self._get_index_values())
+        return algos.backfill_object(values, other, limit=limit)
+
+    def get_pad_indexer(self, other, limit=None):
+        # we coerce to ndarray-of-tuples
+        values = np.array(self._get_index_values())
+        return algos.pad_object(values, other, limit=limit)
+
+    cpdef get_loc(self, object val):
+        if is_definitely_invalid_key(val):
+            raise TypeError("'{val}' is an invalid key".format(val=val))
+
+        self._ensure_mapping_populated()
+        if not self.unique:
+            return self._get_loc_duplicates(val)
+
+        try:
+            return self.mapping.get_item(val)
+        except TypeError:
+            raise KeyError(val)
+
+    cdef _make_hash_table(self, n):
+        return _hash.MultiIndexHashTable(n)
+
 # Generated from template.
 include "index_class_helper.pxi"

pandas/indexes/base.py

@@ -1431,6 +1431,10 @@ def inferred_type(self):
         """ return a string of the type inferred from the values """
         return lib.infer_dtype(self)
 
+    def _is_memory_usage_qualified(self):
+        """ return a boolean if we need a qualified .info display """
+        return self.is_object()
+
     def is_type_compatible(self, kind):
         return kind == self.inferred_type
 
@@ -2446,7 +2450,6 @@ def _get_fill_indexer_searchsorted(self, target, method, limit=None):
                              'if index and target are monotonic' % method)
 
         side = 'left' if method == 'pad' else 'right'
-        target = np.asarray(target)
 
         # find exact matches first (this simplifies the algorithm)
         indexer = self.get_indexer(target)