Optimize getitem operations by checking for same indexes

sdc/datatypes/common_functions.py

@@ -561,7 +561,7 @@ def _sdc_asarray(data):
     pass
 
 
-@sdc_overload(_sdc_asarray, jit_options={'parallel': True})
+@sdc_overload(_sdc_asarray)
 def _sdc_asarray_overload(data):
 
     # TODO: extend with other types
@@ -673,14 +673,21 @@ def sdc_reindex_series(arr, index, name, by_index):
     pass
 
 
-@sdc_overload(sdc_reindex_series, jit_options={'parallel': True})
+@sdc_overload(sdc_reindex_series)
 def sdc_reindex_series_overload(arr, index, name, by_index):
     """ Reindexes series data by new index following the logic of pandas.core.indexing.check_bool_indexer """
 
+    same_index_types = index is by_index
     data_dtype, index_dtype = arr.dtype, index.dtype
     data_is_str_arr = isinstance(arr.dtype, types.UnicodeType)
 
     def sdc_reindex_series_impl(arr, index, name, by_index):
+
+        # if index types are the same, we may not reindex if indexes are the same
+        if same_index_types == True:  # noqa
+            if index is by_index:
+                return pandas.Series(data=arr, index=index, name=name)
+
         if data_is_str_arr == True:  # noqa
             _res_data = [''] * len(by_index)
             res_data_nan_mask = numpy.zeros(len(by_index), dtype=types.bool_)
@@ -722,5 +729,3 @@ def sdc_reindex_series_impl(arr, index, name, by_index):
         return pandas.Series(data=res_data, index=by_index, name=name)
 
     return sdc_reindex_series_impl
-
-    return None

sdc/datatypes/hpat_pandas_dataframe_functions.py

@@ -29,7 +29,6 @@
 | Also, it contains Numba internal operators which are required for DataFrame type handling
 '''
 
-
 import numba
 import numpy
 import operator
@@ -66,6 +65,7 @@
 from sdc.datatypes.common_functions import _sdc_take, sdc_reindex_series
 from sdc.utilities.prange_utils import parallel_chunks
 
+
 @sdc_overload_attribute(DataFrameType, 'index')
 def hpat_pandas_dataframe_index(df):
     """
@@ -105,6 +105,7 @@ def hpat_pandas_df_index_none_impl(df):
 
         return hpat_pandas_df_index_none_impl
     else:
+
         def hpat_pandas_df_index_impl(df):
             return df._index
 
@@ -404,7 +405,6 @@ def sdc_pandas_dataframe_append_impl(df, other, _func_name, ignore_index, indexe
 
     return sdc_pandas_dataframe_append_impl(df, other, _func_name, ignore_index, indexes_comparable, args)
 
-
 # Example func_text for func_name='count' columns=('A', 'B'):
 #
 #         def _df_count_impl(df, axis=0, level=None, numeric_only=False):
@@ -1534,9 +1534,9 @@ def df_getitem_bool_series_idx_main_codelines(self, idx):
     else:
         func_lines = [f'  length = {df_length_expr(self)}',
                       f'  self_index = self.index',
-                      f'  idx_reindexed = sdc_reindex_series(idx._data, idx.index, idx._name, self_index)',
-                      f'  res_index = getitem_by_mask(self_index, idx_reindexed._data)',
-                      f'  selected_pos = getitem_by_mask(range(length), idx_reindexed._data)']
+                      f'  reindexed_idx = sdc_reindex_series(idx._data, idx.index, idx._name, self_index)',
+                      f'  res_index = getitem_by_mask(self_index, reindexed_idx._data)',
+                      f'  selected_pos = getitem_by_mask(numpy.arange(length), reindexed_idx._data)']
 
         results = []
         for i, col in enumerate(self.columns):
@@ -1835,6 +1835,7 @@ def _df_getitem_str_literal_idx_impl(self, idx):
         return _df_getitem_str_literal_idx_impl
 
     if isinstance(idx, types.UnicodeType):
+
         def _df_getitem_unicode_idx_impl(self, idx):
             # http://numba.pydata.org/numba-doc/dev/developer/literal.html#specifying-for-literal-typing
             # literally raises special exception to call getitem with literal idx value got from unicode
@@ -1886,6 +1887,7 @@ def sdc_pandas_dataframe_accessor_getitem(self, idx):
         if isinstance(idx, types.Tuple) and isinstance(idx[1], types.Literal):
             col = idx[1].literal_value
             if -1 < col < len(self.dataframe.columns):
+
                 def df_getitem_iat_tuple_impl(self, idx):
                     row, _ = idx
                     if -1 < row < len(self._dataframe.index):
@@ -2335,6 +2337,7 @@ def df_set_column_overload(self, key, value):
             return gen_df_replace_column_impl(self, key)
 
     if isinstance(key, types.UnicodeType):
+
         def _df_set_column_unicode_key_impl(self, key, value):
             # http://numba.pydata.org/numba-doc/dev/developer/literal.html#specifying-for-literal-typing
             # literally raises special exception to call df._set_column with literal idx value got from unicode

sdc/datatypes/hpat_pandas_series_functions.py

@@ -420,29 +420,27 @@ def hpat_pandas_series_getitem_idx_list_impl(self, idx):
                   ' Given: self.index={}, idx.index={}'
             raise TypingError(msg.format(_func_name, self.index, idx.index))
 
-        def hpat_pandas_series_getitem_idx_bool_indexer_impl(self, idx):
+        def _series_getitem_idx_bool_indexer_impl(self, idx):
 
             if none_indexes == True:  # noqa
                 if len(self) > len(idx):
                     msg = "Unalignable boolean Series provided as indexer " + \
                           "(index of the boolean Series and of the indexed object do not match)."
                     raise IndexingError(msg)
 
-                return pandas.Series(
-                    data=numpy_like.getitem_by_mask(self._data, idx._data),
-                    index=numpy_like.getitem_by_mask(range(len(self)), idx._data),
-                    name=self._name
-                )
+                self_index = range(len(self))
+                reindexed_idx = idx
             else:
                 self_index = self.index
-                idx_reindexed = sdc_reindex_series(idx._data, idx.index, idx._name, self_index)
-                return pandas.Series(
-                    data=numpy_like.getitem_by_mask(self._data, idx_reindexed._data),
-                    index=numpy_like.getitem_by_mask(self_index, idx_reindexed._data),
-                    name=self._name
-                )
+                reindexed_idx = sdc_reindex_series(idx._data, idx.index, idx._name, self_index)
+
+            return pandas.Series(
+                data=numpy_like.getitem_by_mask(self._data, reindexed_idx._data),
+                index=numpy_like.getitem_by_mask(self_index, reindexed_idx._data),
+                name=self._name
+            )
 
-        return hpat_pandas_series_getitem_idx_bool_indexer_impl
+        return _series_getitem_idx_bool_indexer_impl
 
     # idx is Series and it's index is None, idx.dtype is not Boolean
     if (isinstance(idx, SeriesType) and index_is_none

sdc/str_arr_ext.py

@@ -1543,3 +1543,14 @@ def _sdc_str_arr_operator_mul_impl(self, other):
         return res_arr
 
     return _sdc_str_arr_operator_mul_impl
+
+
+@lower_builtin(operator.is_, StringArrayType, StringArrayType)
+def sdc_str_arr_operator_is(context, builder, sig, args):
+
+    # meminfo ptr uniquely identifies each StringArray allocation
+    a = context.make_helper(builder, string_array_type, args[0])
+    b = context.make_helper(builder, string_array_type, args[1])
+    ma = builder.ptrtoint(a.meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b.meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)

sdc/tests/test_series.py

@@ -6984,6 +6984,31 @@ def test_impl(A, idx):
         sdc_exception = context.exception
         self.assertIn(str(sdc_exception), str(pandas_exception))
 
+    @skip_sdc_jit('Not implemented in old-pipeline')
+    def test_series_getitem_idx_bool_series3(self):
+        """ Verifies Series.getitem by mask indicated by a Boolean Series with the same object as index """
+        def test_impl(A, mask, index):
+            S = pd.Series(A, index)
+            idx = pd.Series(mask, S.index)
+            return S[idx]
+        hpat_func = self.jit(test_impl)
+
+        n = 11
+        np.random.seed(0)
+
+        idxs_to_test = [
+            np.arange(n),
+            np.arange(n, dtype='float'),
+            gen_strlist(n, 2, 'abcd123 ')
+        ]
+        series_data = np.arange(n)
+        mask = np.random.choice([True, False], n)
+        for index in idxs_to_test:
+            with self.subTest(series_index=index):
+                result = hpat_func(series_data, mask, index)
+                result_ref = test_impl(series_data, mask, index)
+                pd.testing.assert_series_equal(result, result_ref)
+
     @skip_sdc_jit('Not implemented in old-pipeline')
     def test_series_getitem_idx_bool_series_reindex(self):
         """ Verifies Series.getitem with reindexing by mask indicated by a Boolean Series

sdc/tests/tests_perf/data_generator.py

@@ -28,9 +28,12 @@ def gen_series_fixed_str(data_num, data_length, input_data, data_width):
     return results
 
 
-def gen_arr_from_input(input_data, data_length, random=True):
+def gen_arr_from_input(data_length, input_data, random=True, repeat=True, seed=None):
+    if seed is not None:
+        np.random.seed(seed)
+
     if random:
-        return np.random.choice(input_data, data_length)
+        return np.random.choice(input_data, data_length, replace=repeat)
     else:
         return np.asarray(multiply_oneds_data(input_data, data_length))
 
@@ -50,7 +53,7 @@ def gen_arr_of_dtype(data_length, dtype='float', random=True, limits=None, nuniq
 
     # prefer generation based on input data if it's provided
     if input_data is not None:
-        return gen_arr_from_input(input_data, data_length, random=random)
+        return gen_arr_from_input(data_length, input_data, random=random)
 
     if dtype == 'float':
         return np.random.ranf(data_length)
@@ -67,6 +70,21 @@ def gen_arr_of_dtype(data_length, dtype='float', random=True, limits=None, nuniq
     return None
 
 
+def gen_unique_values(data_length, dtype='int', seed=None):
+    """
+    data_length: result length of array of unique values,
+    dtype: dtype of generated array,
+    seed: seed to initialize random state
+    """
+
+    if dtype in ('float', 'int'):
+        values = np.arange(data_length, dtype=dtype)
+    if dtype == 'str':
+        values = gen_strlist(data_length)
+
+    return gen_arr_from_input(data_length, values, repeat=False, seed=seed)
+
+
 def gen_series(data_length, dtype='float', random=True, limits=None, nunique=1000, input_data=None, seed=None):
     """
     data_length: result series length,
@@ -82,7 +100,7 @@ def gen_series(data_length, dtype='float', random=True, limits=None, nunique=100
 
     # prefer generation based on input data if it's provided
     if input_data is not None:
-        series_data = gen_arr_from_input(input_data, data_length, random=random)
+        series_data = gen_arr_from_input(data_length, input_data, random=random)
     else:
         series_data = gen_arr_of_dtype(data_length, dtype=dtype, limits=limits, nunique=nunique)
 
@@ -98,13 +116,15 @@ def gen_df(data_length,
            limits=None,
            nunique=1000,
            input_data=None,
+           index_gen=None,
            seed=None):
     """
     data_length: result series length,
     dtype: dtype of generated series,
     limits: a tuple of (min, max) limits for numeric series,
     nunique: number of unique values in generated series,
     input_data: 2D sequence of values used for generation of dataframe columns,
+    index_gen: callable that will generate index of needed size,
     seed: seed to initialize random state
     """
 
@@ -116,10 +136,10 @@ def gen_df(data_length,
     for i in range(columns):
         # prefer generation based on input data if it's provided
         if (input_data is not None and i < len(input_data)):
-            col_data = gen_arr_from_input(input_data[i], data_length, random=random)
+            col_data = gen_arr_from_input(data_length, input_data[i], random=random)
         else:
             col_data = gen_arr_of_dtype(data_length, dtype=dtype, limits=limits, nunique=nunique)
         all_data.append(col_data)
 
-    # TODO: support index generation
-    return pd.DataFrame(dict(zip(col_names, all_data)))
+    index_data = index_gen(data_length) if index_gen is not None else None
+    return pd.DataFrame(dict(zip(col_names, all_data)), index=index_data)

sdc/tests/tests_perf/test_perf_df.py

@@ -36,7 +36,7 @@
 from sdc.tests.test_utils import test_global_input_data_float64
 from .generator import generate_test_cases
 from .generator import TestCase as TC
-from .data_generator import gen_df, gen_series, gen_arr_of_dtype
+from .data_generator import gen_df, gen_series, gen_arr_of_dtype, gen_unique_values
 
 
 # python -m sdc.runtests sdc.tests.tests_perf.test_perf_df.TestDataFrameMethods.test_df_{method_name}
@@ -88,6 +88,8 @@ def _test_case(self, pyfunc, name, total_data_length, input_data=None, data_num=
     TC(name='getitem_idx_bool_array', size=[10 ** 7], call_expr='df[idx]', usecase_params='df, idx',
        data_gens=(gen_df, partial(gen_arr_of_dtype, dtype='bool', random=False)),
        input_data=[None, [True, False, False, True, False, True]]),
+    TC(name='getitem_filter_by_value', size=[10 ** 7], call_expr='df[df.A > 0]', usecase_params='df',
+       data_gens=(partial(gen_df, index_gen=gen_unique_values), )),
 ]
 
 generate_test_cases(cases, TestDataFrameMethods, 'df')