# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF 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. # from datetime import datetime from distutils.version import LooseVersion import unittest import numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.testing.pandasutils import ComparisonTestBase from pyspark.testing.sqlutils import SQLTestUtils # This file contains test cases for 'Attributes and underlying data' # https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/frame.html#attributes-and-underlying-data class FrameAttrsMixin: @property def pdf(self): return pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=np.random.rand(9), ) @property def df_pair(self): pdf = self.pdf psdf = ps.from_pandas(pdf) return pdf, psdf def test_column_names(self): pdf, psdf = self.df_pair self.assert_eq(psdf.columns, pdf.columns) self.assert_eq(psdf[["b", "a"]].columns, pdf[["b", "a"]].columns) self.assert_eq(psdf["a"].name, pdf["a"].name) self.assert_eq((psdf["a"] + 1).name, (pdf["a"] + 1).name) self.assert_eq((psdf.a + psdf.b).name, (pdf.a + pdf.b).name) self.assert_eq((psdf.a + psdf.b.rename("a")).name, (pdf.a + pdf.b.rename("a")).name) self.assert_eq((psdf.a + psdf.b.rename()).name, (pdf.a + pdf.b.rename()).name) self.assert_eq((psdf.a.rename() + psdf.b).name, (pdf.a.rename() + pdf.b).name) self.assert_eq( (psdf.a.rename() + psdf.b.rename()).name, (pdf.a.rename() + pdf.b.rename()).name ) def test_rename_columns(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7], "b": [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7) ) psdf = ps.from_pandas(pdf) psdf.columns = ["x", "y"] pdf.columns = ["x", "y"] self.assert_eq(psdf.columns, pd.Index(["x", "y"])) self.assert_eq(psdf, pdf) self.assert_eq(psdf._internal.data_spark_column_names, ["x", "y"]) self.assert_eq(psdf.to_spark().columns, ["x", "y"]) self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "x", "y"]) columns = pdf.columns columns.name = "lvl_1" psdf.columns = columns self.assert_eq(psdf.columns.names, ["lvl_1"]) self.assert_eq(psdf, pdf) msg = "Length mismatch: Expected axis has 2 elements, new values have 4 elements" with self.assertRaisesRegex(ValueError, msg): psdf.columns = [1, 2, 3, 4] # Multi-index columns pdf = pd.DataFrame( {("A", "0"): [1, 2, 2, 3], ("B", "1"): [1, 2, 3, 4]}, index=np.random.rand(4) ) psdf = ps.from_pandas(pdf) columns = pdf.columns self.assert_eq(psdf.columns, columns) self.assert_eq(psdf, pdf) pdf.columns = ["x", "y"] psdf.columns = ["x", "y"] self.assert_eq(psdf.columns, pd.Index(["x", "y"])) self.assert_eq(psdf, pdf) self.assert_eq(psdf._internal.data_spark_column_names, ["x", "y"]) self.assert_eq(psdf.to_spark().columns, ["x", "y"]) self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "x", "y"]) pdf.columns = columns psdf.columns = columns self.assert_eq(psdf.columns, columns) self.assert_eq(psdf, pdf) self.assert_eq(psdf._internal.data_spark_column_names, ["(A, 0)", "(B, 1)"]) self.assert_eq(psdf.to_spark().columns, ["(A, 0)", "(B, 1)"]) self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "(A, 0)", "(B, 1)"]) columns.names = ["lvl_1", "lvl_2"] psdf.columns = columns self.assert_eq(psdf.columns.names, ["lvl_1", "lvl_2"]) self.assert_eq(psdf, pdf) self.assert_eq(psdf._internal.data_spark_column_names, ["(A, 0)", "(B, 1)"]) self.assert_eq(psdf.to_spark().columns, ["(A, 0)", "(B, 1)"]) self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "(A, 0)", "(B, 1)"]) def test_multi_index_dtypes(self): # SPARK-36930: Support ps.MultiIndex.dtypes arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]] pmidx = pd.MultiIndex.from_arrays(arrays, names=("number", "color")) psmidx = ps.from_pandas(pmidx) if LooseVersion(pd.__version__) >= LooseVersion("1.3"): self.assert_eq(psmidx.dtypes, pmidx.dtypes) else: expected = pd.Series([np.dtype("int64"), np.dtype("O")], index=["number", "color"]) self.assert_eq(psmidx.dtypes, expected) # multiple labels pmidx = pd.MultiIndex.from_arrays(arrays, names=[("zero", "first"), ("one", "second")]) psmidx = ps.from_pandas(pmidx) if LooseVersion(pd.__version__) >= LooseVersion("1.3"): if LooseVersion(pd.__version__) not in (LooseVersion("1.4.1"), LooseVersion("1.4.2")): self.assert_eq(psmidx.dtypes, pmidx.dtypes) else: expected = pd.Series( [np.dtype("int64"), np.dtype("O")], index=pd.Index([("zero", "first"), ("one", "second")]), ) self.assert_eq(psmidx.dtypes, expected) def test_multi_index_dtypes_not_unique_name(self): # Regression test for https://github.com/pandas-dev/pandas/issues/45174 pmidx = pd.MultiIndex.from_arrays([[1], [2]], names=[1, 1]) psmidx = ps.from_pandas(pmidx) if LooseVersion(pd.__version__) < LooseVersion("1.4"): expected = pd.Series( [np.dtype("int64"), np.dtype("int64")], index=[1, 1], ) self.assert_eq(psmidx.dtypes, expected) else: self.assert_eq(psmidx.dtypes, pmidx.dtypes) def test_dtype(self): pdf = pd.DataFrame( { "a": list("abc"), "b": list(range(1, 4)), "c": np.arange(3, 6).astype("i1"), "d": np.arange(4.0, 7.0, dtype="float64"), "e": [True, False, True], "f": pd.date_range("20130101", periods=3), }, index=np.random.rand(3), ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf, pdf) self.assertTrue((psdf.dtypes == pdf.dtypes).all()) # multi-index columns columns = pd.MultiIndex.from_tuples(zip(list("xxxyyz"), list("abcdef"))) pdf.columns = columns psdf.columns = columns self.assertTrue((psdf.dtypes == pdf.dtypes).all()) def test_axes(self): pdf = self.pdf psdf = ps.from_pandas(pdf) self.assert_eq(pdf.axes, psdf.axes) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")]) pdf.columns = columns psdf.columns = columns self.assert_eq(pdf.axes, psdf.axes) def test_inplace(self): pdf, psdf = self.df_pair pser = pdf.a psser = psdf.a pdf["a"] = pdf["a"] + 10 psdf["a"] = psdf["a"] + 10 self.assert_eq(psdf, pdf) # SPARK-38946: Since Spark 3.4, df.__setitem__ generate a new dataframe to follow # pandas 1.4 behaviors if LooseVersion(pd.__version__) >= LooseVersion("1.4.0"): self.assert_eq(psser, pser) else: # Follow pandas latest behavior with self.assertRaisesRegex(AssertionError, "Series are different"): self.assert_eq(psser, pser) def test_dataframe_multiindex_columns(self): pdf = pd.DataFrame( { ("x", "a", "1"): [1, 2, 3], ("x", "b", "2"): [4, 5, 6], ("y.z", "c.d", "3"): [7, 8, 9], ("x", "b", "4"): [10, 11, 12], }, index=np.random.rand(3), ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf, pdf) self.assert_eq(psdf["x"], pdf["x"]) self.assert_eq(psdf["y.z"], pdf["y.z"]) self.assert_eq(psdf["x"]["b"], pdf["x"]["b"]) self.assert_eq(psdf["x"]["b"]["2"], pdf["x"]["b"]["2"]) self.assert_eq(psdf.x, pdf.x) self.assert_eq(psdf.x.b, pdf.x.b) self.assert_eq(psdf.x.b["2"], pdf.x.b["2"]) self.assertRaises(KeyError, lambda: psdf["z"]) self.assertRaises(AttributeError, lambda: psdf.z) self.assert_eq(psdf[("x",)], pdf[("x",)]) self.assert_eq(psdf[("x", "a")], pdf[("x", "a")]) self.assert_eq(psdf[("x", "a", "1")], pdf[("x", "a", "1")]) def test_dataframe_column_level_name(self): column = pd.Index(["A", "B", "C"], name="X") pdf = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=column, index=np.random.rand(2)) psdf = ps.from_pandas(pdf) self.assert_eq(psdf, pdf) self.assert_eq(psdf.columns.names, pdf.columns.names) self.assert_eq(psdf._to_pandas().columns.names, pdf.columns.names) def test_dataframe_multiindex_names_level(self): columns = pd.MultiIndex.from_tuples( [("X", "A", "Z"), ("X", "B", "Z"), ("Y", "C", "Z"), ("Y", "D", "Z")], names=["lvl_1", "lvl_2", "lv_3"], ) pdf = pd.DataFrame( [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20]], columns=columns, index=np.random.rand(5), ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf.columns.names, pdf.columns.names) self.assert_eq(psdf._to_pandas().columns.names, pdf.columns.names) psdf1 = ps.from_pandas(pdf) self.assert_eq(psdf1.columns.names, pdf.columns.names) self.assertRaises( AssertionError, lambda: ps.DataFrame(psdf1._internal.copy(column_label_names=("level",))), ) self.assert_eq(psdf["X"], pdf["X"]) self.assert_eq(psdf["X"].columns.names, pdf["X"].columns.names) self.assert_eq(psdf["X"]._to_pandas().columns.names, pdf["X"].columns.names) self.assert_eq(psdf["X"]["A"], pdf["X"]["A"]) self.assert_eq(psdf["X"]["A"].columns.names, pdf["X"]["A"].columns.names) self.assert_eq(psdf["X"]["A"]._to_pandas().columns.names, pdf["X"]["A"].columns.names) self.assert_eq(psdf[("X", "A")], pdf[("X", "A")]) self.assert_eq(psdf[("X", "A")].columns.names, pdf[("X", "A")].columns.names) self.assert_eq(psdf[("X", "A")]._to_pandas().columns.names, pdf[("X", "A")].columns.names) self.assert_eq(psdf[("X", "A", "Z")], pdf[("X", "A", "Z")]) def test_repr_cache_invalidation(self): # If there is any cache, inplace operations should invalidate it. df = ps.range(10) df.__repr__() df["a"] = df["id"] self.assertEqual(df.__repr__(), df._to_pandas().__repr__()) def test_repr_html_cache_invalidation(self): # If there is any cache, inplace operations should invalidate it. df = ps.range(10) df._repr_html_() df["a"] = df["id"] self.assertEqual(df._repr_html_(), df._to_pandas()._repr_html_()) def test_assign(self): pdf, psdf = self.df_pair psdf["w"] = 1.0 pdf["w"] = 1.0 self.assert_eq(psdf, pdf) psdf.w = 10.0 pdf.w = 10.0 self.assert_eq(psdf, pdf) psdf[1] = 1.0 pdf[1] = 1.0 self.assert_eq(psdf, pdf) psdf = psdf.assign(a=psdf["a"] * 2) pdf = pdf.assign(a=pdf["a"] * 2) self.assert_eq(psdf, pdf) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "w"), ("y", "v")]) pdf.columns = columns psdf.columns = columns psdf[("a", "c")] = "def" pdf[("a", "c")] = "def" self.assert_eq(psdf, pdf) psdf = psdf.assign(Z="ZZ") pdf = pdf.assign(Z="ZZ") self.assert_eq(psdf, pdf) psdf["x"] = "ghi" pdf["x"] = "ghi" self.assert_eq(psdf, pdf) def test_attributes(self): psdf = self.psdf self.assertIn("a", dir(psdf)) self.assertNotIn("foo", dir(psdf)) self.assertRaises(AttributeError, lambda: psdf.foo) psdf = ps.DataFrame({"a b c": [1, 2, 3]}) self.assertNotIn("a b c", dir(psdf)) psdf = ps.DataFrame({"a": [1, 2], 5: [1, 2]}) self.assertIn("a", dir(psdf)) self.assertNotIn(5, dir(psdf)) def test_empty_timestamp(self): pdf = pd.DataFrame( { "t": [ datetime(2019, 1, 1, 0, 0, 0), datetime(2019, 1, 2, 0, 0, 0), datetime(2019, 1, 3, 0, 0, 0), ] }, index=np.random.rand(3), ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf[psdf["t"] != psdf["t"]], pdf[pdf["t"] != pdf["t"]]) self.assert_eq(psdf[psdf["t"] != psdf["t"]].dtypes, pdf[pdf["t"] != pdf["t"]].dtypes) class FrameAttrsTests(FrameAttrsMixin, ComparisonTestBase, SQLTestUtils): pass if __name__ == "__main__": from pyspark.pandas.tests.frame.test_attrs import * # noqa: F401 try: import xmlrunner testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)