chore: import upstream snapshot with attribution

python/ray/tests/modin/modin_test_utils.py

@@ -0,0 +1,151 @@
+# Licensed to Modin Development Team under one or more contributor license
+# agreements. See the NOTICE file distributed with this work for additional
+# information regarding copyright ownership.  The Modin Development Team
+# licenses this file to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+#
+# This file is copied and adapted from
+# http://github.com/modin-project/modin/master/modin/pandas/test/utils.py
+
+from typing import Any
+
+import modin.pandas as pd
+import numpy as np
+import pandas
+
+# to_pandas moved from modin.utils to modin.pandas.io in modin 0.26.0,
+from modin.pandas.io import to_pandas
+from pandas.testing import (
+    assert_extension_array_equal,
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
+
+
+def categories_equals(left, right):
+    assert (left.ordered and right.ordered) or (not left.ordered and not right.ordered)
+    assert_extension_array_equal(left, right)
+
+
+def df_categories_equals(df1, df2):
+    if not hasattr(df1, "select_dtypes"):
+        if isinstance(df1, pandas.CategoricalDtype):
+            return categories_equals(df1, df2)
+        elif isinstance(df1.dtype, pandas.CategoricalDtype) and isinstance(
+            df1.dtype, pandas.CategoricalDtype
+        ):
+            return categories_equals(df1.dtype, df2.dtype)
+        else:
+            return True
+
+    categories_columns = df1.select_dtypes(include="category").columns
+    for column in categories_columns:
+        assert_extension_array_equal(
+            df1[column].values,
+            df2[column].values,
+            check_dtype=False,
+        )
+
+
+def df_equals(df1: Any, df2: Any) -> bool:
+    """Tests if df1 and df2 are equal.
+
+    Args:
+        df1: (pandas or modin DataFrame or series) dataframe to test if equal.
+        df2: (pandas or modin DataFrame or series) dataframe to test if equal.
+
+    Returns:
+        True if df1 is equal to df2.
+    """
+    # Gets AttributError if modin's groupby object is not import like this
+    from modin.pandas.groupby import DataFrameGroupBy
+
+    groupby_types = (pandas.core.groupby.DataFrameGroupBy, DataFrameGroupBy)
+
+    # The typing behavior of how pandas treats its index is not consistent when
+    # the length of the DataFrame or Series is 0, so we just verify that the
+    # contents are the same.
+    if (
+        hasattr(df1, "index")
+        and hasattr(df2, "index")
+        and len(df1) == 0
+        and len(df2) == 0
+    ):
+        if type(df1).__name__ == type(df2).__name__:
+            if hasattr(df1, "name") and hasattr(df2, "name") and df1.name == df2.name:
+                return
+            if (
+                hasattr(df1, "columns")
+                and hasattr(df2, "columns")
+                and df1.columns.equals(df2.columns)
+            ):
+                return
+        assert False
+
+    if isinstance(df1, (list, tuple)) and all(
+        isinstance(d, (pd.DataFrame, pd.Series, pandas.DataFrame, pandas.Series))
+        for d in df1
+    ):
+        assert isinstance(df2, type(df1)), "Different type of collection"
+        assert len(df1) == len(df2), "Different length result"
+        return (df_equals(d1, d2) for d1, d2 in zip(df1, df2))
+
+    # Convert to pandas
+    if isinstance(df1, (pd.DataFrame, pd.Series)):
+        df1 = to_pandas(df1)
+    if isinstance(df2, (pd.DataFrame, pd.Series)):
+        df2 = to_pandas(df2)
+
+    if isinstance(df1, pandas.DataFrame) and isinstance(df2, pandas.DataFrame):
+        if (df1.empty and not df2.empty) or (df2.empty and not df1.empty):
+            assert False, "One of the passed frames is empty, when other isn't"
+        elif df1.empty and df2.empty and type(df1) is not type(df2):
+            assert (
+                False
+            ), f"Empty frames have different types: {type(df1)} != {type(df2)}"
+
+    if isinstance(df1, pandas.DataFrame) and isinstance(df2, pandas.DataFrame):
+        assert_frame_equal(
+            df1,
+            df2,
+            check_dtype=False,
+            check_datetimelike_compat=True,
+            check_index_type=False,
+            check_column_type=False,
+            check_categorical=False,
+        )
+        df_categories_equals(df1, df2)
+    elif isinstance(df1, pandas.Index) and isinstance(df2, pandas.Index):
+        assert_index_equal(df1, df2)
+    elif isinstance(df1, pandas.Series) and isinstance(df2, pandas.Series):
+        assert_series_equal(df1, df2, check_dtype=False, check_series_type=False)
+    elif isinstance(df1, groupby_types) and isinstance(df2, groupby_types):
+        for g1, g2 in zip(df1, df2):
+            assert g1[0] == g2[0]
+            df_equals(g1[1], g2[1])
+    elif (
+        isinstance(df1, pandas.Series)
+        and isinstance(df2, pandas.Series)
+        and df1.empty
+        and df2.empty
+    ):
+        assert all(df1.index == df2.index)
+        assert df1.dtypes == df2.dtypes
+    elif isinstance(df1, pandas.core.arrays.numpy_.PandasArray):
+        assert isinstance(df2, pandas.core.arrays.numpy_.PandasArray)
+        assert df1 == df2
+    elif isinstance(df1, np.recarray) and isinstance(df2, np.recarray):
+        np.testing.assert_array_equal(df1, df2)
+    else:
+        if df1 != df2:
+            np.testing.assert_almost_equal(df1, df2)