PERF: don't call RangeIndex._data unnecessarily (#26565)

Author: topper-123

asv_bench/benchmarks/index_object.py

@@ -94,6 +94,12 @@ def time_min(self):
     def time_min_trivial(self):
         self.idx_inc.min()
 
+    def time_get_loc_inc(self):
+        self.idx_inc.get_loc(900000)
+
+    def time_get_loc_dec(self):
+        self.idx_dec.get_loc(100000)
+
 
 class IndexAppend:
 

doc/source/whatsnew/v0.25.0.rst

@@ -493,6 +493,7 @@ Performance Improvements
 - Improved performance of :meth:`Series.searchsorted`. The speedup is especially large when the dtype is
   int8/int16/int32 and the searched key is within the integer bounds for the dtype (:issue:`22034`)
 - Improved performance of :meth:`pandas.core.groupby.GroupBy.quantile` (:issue:`20405`)
+- Improved performance when slicing :class:`RangeIndex` (:issue:`26565`)
 - Improved performance of :meth:`read_csv` by faster tokenizing and faster parsing of small float numbers (:issue:`25784`)
 - Improved performance of :meth:`read_csv` by faster parsing of N/A and boolean values (:issue:`25804`)
 - Improved performance of :meth:`IntervalIndex.is_monotonic`, :meth:`IntervalIndex.is_monotonic_increasing` and :meth:`IntervalIndex.is_monotonic_decreasing` by removing conversion to :class:`MultiIndex` (:issue:`24813`)

pandas/core/indexes/range.py

@@ -22,6 +22,8 @@
 from pandas.core.indexes.base import Index, _index_shared_docs
 from pandas.core.indexes.numeric import Int64Index
 
+from pandas.io.formats.printing import pprint_thing
+
 
 class RangeIndex(Int64Index):
     """
@@ -64,6 +66,8 @@ class RangeIndex(Int64Index):
     _typ = 'rangeindex'
     _engine_type = libindex.Int64Engine
 
+    # check whether self._data has benn called
+    _cached_data = None  # type: np.ndarray
     # --------------------------------------------------------------------
     # Constructors
 
@@ -164,6 +168,8 @@ def _simple_new(cls, start, stop=None, step=None, name=None,
         for k, v in kwargs.items():
             setattr(result, k, v)
 
+        result._range = range(result._start, result._stop, result._step)
+
         result._reset_identity()
         return result
 
@@ -180,9 +186,19 @@ def _constructor(self):
         """ return the class to use for construction """
         return Int64Index
 
-    @cache_readonly
+    @property
     def _data(self):
-        return np.arange(self._start, self._stop, self._step, dtype=np.int64)
+        """
+        An int array that for performance reasons is created only when needed.
+
+        The constructed array is saved in ``_cached_data``. This allows us to
+        check if the array has been created without accessing ``_data`` and
+        triggering the construction.
+        """
+        if self._cached_data is None:
+            self._cached_data = np.arange(self._start, self._stop, self._step,
+                                          dtype=np.int64)
+        return self._cached_data
 
     @cache_readonly
     def _int64index(self):
@@ -215,6 +231,9 @@ def _format_data(self, name=None):
         # we are formatting thru the attributes
         return None
 
+    def _format_with_header(self, header, na_rep='NaN', **kwargs):
+        return header + list(map(pprint_thing, self._range))
+
     # --------------------------------------------------------------------
     @property
     def start(self):
@@ -296,6 +315,15 @@ def is_monotonic_decreasing(self):
     def has_duplicates(self):
         return False
 
+    @Appender(_index_shared_docs['get_loc'])
+    def get_loc(self, key, method=None, tolerance=None):
+        if is_integer(key) and method is None and tolerance is None:
+            try:
+                return self._range.index(key)
+            except ValueError:
+                raise KeyError(key)
+        return super().get_loc(key, method=method, tolerance=tolerance)
+
     def tolist(self):
         return list(range(self._start, self._stop, self._step))
 

pandas/tests/indexes/test_range.py

@@ -241,6 +241,42 @@ def test_view(self):
     def test_dtype(self):
         assert self.index.dtype == np.int64
 
+    def test_cached_data(self):
+        # GH 26565
+        # Calling RangeIndex._data caches an int64 array of the same length at
+        # self._cached_data. This tests whether _cached_data has been set.
+        idx = RangeIndex(0, 100, 10)
+
+        assert idx._cached_data is None
+
+        repr(idx)
+        assert idx._cached_data is None
+
+        str(idx)
+        assert idx._cached_data is None
+
+        idx.get_loc(20)
+        assert idx._cached_data is None
+
+        df = pd.DataFrame({'a': range(10)}, index=idx)
+
+        df.loc[50]
+        assert idx._cached_data is None
+
+        with pytest.raises(KeyError):
+            df.loc[51]
+        assert idx._cached_data is None
+
+        df.loc[10:50]
+        assert idx._cached_data is None
+
+        df.iloc[5:10]
+        assert idx._cached_data is None
+
+        # actually calling data._data
+        assert isinstance(idx._data, np.ndarray)
+        assert isinstance(idx._cached_data, np.ndarray)
+
     def test_is_monotonic(self):
         assert self.index.is_monotonic is True
         assert self.index.is_monotonic_increasing is True