4d2b559d92
### What changes were proposed in this pull request? Consolidate PySpark testing utils by removing `python/pyspark/pandas/testing`, and then creating a file `pandasutils` under `python/pyspark/testing` for test utilities used in `pyspark/pandas`. ### Why are the changes needed? `python/pyspark/pandas/testing` hold test utilites for pandas-on-spark, and `python/pyspark/testing` contain test utilities for pyspark. Consolidating them makes code cleaner and easier to maintain. Updated import statements are as shown below: - from pyspark.testing.sqlutils import SQLTestUtils - from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils (PandasOnSparkTestCase is the original ReusedSQLTestCase in `python/pyspark/pandas/testing/utils.py`) Minor improvements include: - Usage of missing library's requirement_message - `except ImportError` rather than `except` - import pyspark.pandas alias as `ps` rather than `pp` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Unit tests under python/pyspark/pandas/tests. Closes #32177 from xinrong-databricks/port.merge_utils. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
476 lines
16 KiB
Python
476 lines
16 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from distutils.version import LooseVersion
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import numpy as np
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import pandas as pd
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from pandas.api.types import CategoricalDtype
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import pyspark.pandas as ps
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from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
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class CategoricalTest(PandasOnSparkTestCase, TestUtils):
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@property
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def pdf(self):
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return pd.DataFrame(
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{
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"a": pd.Categorical([1, 2, 3, 1, 2, 3]),
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"b": pd.Categorical(
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["b", "a", "c", "c", "b", "a"], categories=["c", "b", "d", "a"]
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),
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},
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)
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@property
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def kdf(self):
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return ps.from_pandas(self.pdf)
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@property
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def df_pair(self):
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return self.pdf, self.kdf
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def test_categorical_frame(self):
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pdf, kdf = self.df_pair
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self.assert_eq(kdf, pdf)
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self.assert_eq(kdf.a, pdf.a)
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self.assert_eq(kdf.b, pdf.b)
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self.assert_eq(kdf.index, pdf.index)
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self.assert_eq(kdf.sort_index(), pdf.sort_index())
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self.assert_eq(kdf.sort_values("b"), pdf.sort_values("b"))
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def test_categorical_series(self):
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pser = pd.Series([1, 2, 3], dtype="category")
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kser = ps.Series([1, 2, 3], dtype="category")
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self.assert_eq(kser, pser)
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self.assert_eq(kser.cat.categories, pser.cat.categories)
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self.assert_eq(kser.cat.codes, pser.cat.codes)
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self.assert_eq(kser.cat.ordered, pser.cat.ordered)
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def test_astype(self):
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pser = pd.Series(["a", "b", "c"])
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kser = ps.from_pandas(pser)
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self.assert_eq(kser.astype("category"), pser.astype("category"))
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self.assert_eq(
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kser.astype(CategoricalDtype(["c", "a", "b"])),
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pser.astype(CategoricalDtype(["c", "a", "b"])),
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)
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pcser = pser.astype(CategoricalDtype(["c", "a", "b"]))
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kcser = kser.astype(CategoricalDtype(["c", "a", "b"]))
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self.assert_eq(kcser.astype("category"), pcser.astype("category"))
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if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
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self.assert_eq(
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kcser.astype(CategoricalDtype(["b", "c", "a"])),
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pcser.astype(CategoricalDtype(["b", "c", "a"])),
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)
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else:
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self.assert_eq(
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kcser.astype(CategoricalDtype(["b", "c", "a"])),
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pser.astype(CategoricalDtype(["b", "c", "a"])),
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)
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self.assert_eq(kcser.astype(str), pcser.astype(str))
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def test_factorize(self):
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pser = pd.Series(["a", "b", "c", None], dtype=CategoricalDtype(["c", "a", "d", "b"]))
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kser = ps.from_pandas(pser)
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pcodes, puniques = pser.factorize()
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kcodes, kuniques = kser.factorize()
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self.assert_eq(kcodes.tolist(), pcodes.tolist())
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self.assert_eq(kuniques, puniques)
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pcodes, puniques = pser.factorize(na_sentinel=-2)
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kcodes, kuniques = kser.factorize(na_sentinel=-2)
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self.assert_eq(kcodes.tolist(), pcodes.tolist())
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self.assert_eq(kuniques, puniques)
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def test_frame_apply(self):
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pdf, kdf = self.df_pair
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self.assert_eq(kdf.apply(lambda x: x).sort_index(), pdf.apply(lambda x: x).sort_index())
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self.assert_eq(
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kdf.apply(lambda x: x, axis=1).sort_index(), pdf.apply(lambda x: x, axis=1).sort_index()
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)
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def test_frame_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_apply()
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c"])
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def categorize(ser) -> ps.Series[dtype]:
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return ser.astype(dtype)
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self.assert_eq(
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kdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
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pdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
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)
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def test_frame_transform(self):
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pdf, kdf = self.df_pair
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self.assert_eq(kdf.transform(lambda x: x), pdf.transform(lambda x: x))
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self.assert_eq(kdf.transform(lambda x: x.cat.codes), pdf.transform(lambda x: x.cat.codes))
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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kdf.transform(lambda x: x.astype(dtype)).sort_index(),
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pdf.transform(lambda x: x.astype(dtype)).sort_index(),
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)
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def test_frame_transform_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_transform()
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pdf, kdf = self.df_pair
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def codes(pser) -> ps.Series[np.int8]:
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return pser.cat.codes
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self.assert_eq(kdf.transform(codes), pdf.transform(codes))
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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def to_category(pser) -> ps.Series[dtype]:
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return pser.astype(dtype)
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self.assert_eq(
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kdf.transform(to_category).sort_index(), pdf.transform(to_category).sort_index()
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)
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def test_series_apply(self):
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pdf, kdf = self.df_pair
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self.assert_eq(kdf.a.apply(lambda x: x).sort_index(), pdf.a.apply(lambda x: x).sort_index())
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def test_series_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_series_apply()
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pdf, kdf = self.df_pair
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ret = kdf.a.dtype
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def identity(pser) -> ret:
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return pser
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self.assert_eq(kdf.a.apply(identity).sort_index(), pdf.a.apply(identity).sort_index())
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# TODO: The return type is still category.
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# def to_str(x) -> str:
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# return str(x)
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#
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# self.assert_eq(
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# kdf.a.apply(to_str).sort_index(), pdf.a.apply(to_str).sort_index()
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# )
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def test_groupby_apply(self):
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pdf, kdf = self.df_pair
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self.assert_eq(
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kdf.groupby("a").apply(lambda df: df).sort_index(),
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pdf.groupby("a").apply(lambda df: df).sort_index(),
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)
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self.assert_eq(
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kdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
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pdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
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)
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self.assert_eq(
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kdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
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pdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
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)
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self.assert_eq(
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kdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
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pdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
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)
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self.assert_eq(
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kdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
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pdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
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)
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# TODO: grouping by a categorical type sometimes preserves unused categories.
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# self.assert_eq(
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# kdf.groupby("a").apply(len).sort_index(), pdf.groupby("a").apply(len).sort_index(),
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# )
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def test_groupby_apply_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_groupby_apply()
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pdf, kdf = self.df_pair
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def identity(df) -> ps.DataFrame[zip(kdf.columns, kdf.dtypes)]:
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return df
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self.assert_eq(
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kdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
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pdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
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)
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def test_groupby_transform(self):
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pdf, kdf = self.df_pair
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self.assert_eq(
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kdf.groupby("a").transform(lambda x: x).sort_index(),
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pdf.groupby("a").transform(lambda x: x).sort_index(),
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)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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kdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
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pdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
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)
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def test_groupby_transform_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_groupby_transform()
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pdf, kdf = self.df_pair
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def identity(x) -> ps.Series[kdf.b.dtype]: # type: ignore
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return x
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self.assert_eq(
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kdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
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pdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
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)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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def astype(x) -> ps.Series[dtype]:
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return x.astype(dtype)
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if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
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self.assert_eq(
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kdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
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pdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
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)
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else:
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expected = pdf.groupby("a").transform(astype)
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expected["b"] = dtype.categories.take(expected["b"].cat.codes).astype(dtype)
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self.assert_eq(
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kdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
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expected.sort_values("b").reset_index(drop=True),
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)
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def test_frame_apply_batch(self):
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pdf, kdf = self.df_pair
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self.assert_eq(
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kdf.koalas.apply_batch(lambda pdf: pdf.astype(str)).sort_index(),
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pdf.astype(str).sort_index(),
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)
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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kdf.koalas.apply_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
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pdf.astype(dtype).sort_index(),
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)
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def test_frame_apply_batch_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_apply_batch()
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pdf, kdf = self.df_pair
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def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
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return pdf.astype(str)
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self.assert_eq(
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kdf.koalas.apply_batch(to_str).sort_values(["a", "b"]).reset_index(drop=True),
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to_str(pdf).sort_values(["a", "b"]).reset_index(drop=True),
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)
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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ret = ps.DataFrame["a":dtype, "b":dtype]
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def to_category(pdf) -> ret:
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return pdf.astype(dtype)
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self.assert_eq(
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kdf.koalas.apply_batch(to_category).sort_values(["a", "b"]).reset_index(drop=True),
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to_category(pdf).sort_values(["a", "b"]).reset_index(drop=True),
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)
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def test_frame_transform_batch(self):
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pdf, kdf = self.df_pair
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self.assert_eq(
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kdf.koalas.transform_batch(lambda pdf: pdf.astype(str)).sort_index(),
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pdf.astype(str).sort_index(),
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)
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self.assert_eq(
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kdf.koalas.transform_batch(lambda pdf: pdf.b.cat.codes).sort_index(),
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pdf.b.cat.codes.sort_index(),
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)
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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kdf.koalas.transform_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
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pdf.astype(dtype).sort_index(),
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)
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self.assert_eq(
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kdf.koalas.transform_batch(lambda pdf: pdf.b.astype(dtype)).sort_index(),
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pdf.b.astype(dtype).sort_index(),
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)
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def test_frame_transform_batch_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_frame_transform_batch()
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pdf, kdf = self.df_pair
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def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
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return pdf.astype(str)
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self.assert_eq(
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kdf.koalas.transform_batch(to_str).sort_index(), to_str(pdf).sort_index(),
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)
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def to_codes(pdf) -> ps.Series[np.int8]:
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return pdf.b.cat.codes
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self.assert_eq(
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kdf.koalas.transform_batch(to_codes).sort_index(), to_codes(pdf).sort_index(),
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)
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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ret = ps.DataFrame["a":dtype, "b":dtype]
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def to_category(pdf) -> ret:
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return pdf.astype(dtype)
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self.assert_eq(
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kdf.koalas.transform_batch(to_category).sort_index(), to_category(pdf).sort_index(),
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)
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def to_category(pdf) -> ps.Series[dtype]:
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return pdf.b.astype(dtype)
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self.assert_eq(
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kdf.koalas.transform_batch(to_category).sort_index(),
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to_category(pdf).rename().sort_index(),
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)
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def test_series_transform_batch(self):
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pdf, kdf = self.df_pair
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self.assert_eq(
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kdf.a.koalas.transform_batch(lambda pser: pser.astype(str)).sort_index(),
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pdf.a.astype(str).sort_index(),
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)
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pdf = pd.DataFrame(
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{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
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)
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kdf = ps.from_pandas(pdf)
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dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
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self.assert_eq(
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kdf.a.koalas.transform_batch(lambda pser: pser.astype(dtype)).sort_index(),
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pdf.a.astype(dtype).sort_index(),
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)
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def test_series_transform_batch_without_shortcut(self):
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with ps.option_context("compute.shortcut_limit", 0):
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self.test_series_transform_batch()
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|
|
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pdf, kdf = self.df_pair
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|
|
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def to_str(pser) -> ps.Series[str]:
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return pser.astype(str)
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|
|
|
self.assert_eq(
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|
kdf.a.koalas.transform_batch(to_str).sort_index(), to_str(pdf.a).sort_index()
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|
)
|
|
|
|
pdf = pd.DataFrame(
|
|
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
|
|
)
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|
kdf = ps.from_pandas(pdf)
|
|
|
|
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
|
|
|
|
def to_category(pser) -> ps.Series[dtype]:
|
|
return pser.astype(dtype)
|
|
|
|
self.assert_eq(
|
|
kdf.a.koalas.transform_batch(to_category).sort_index(), to_category(pdf.a).sort_index()
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
from pyspark.pandas.tests.test_categorical import * # noqa: F401
|
|
|
|
try:
|
|
import xmlrunner # type: ignore[import]
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|