04a8d2cbcf
### What changes were proposed in this pull request? Make the conversion from/to pandas (for non-ExtensionDtype) data-type-based. NOTE: Ops class per ExtensionDtype and its data-type-based from/to pandas will be implemented in a separate PR as https://issues.apache.org/jira/browse/SPARK-35614. ### Why are the changes needed? The conversion from/to pandas includes logic for checking data types and behaving accordingly. That makes code hard to change or maintain. Since we have introduced the Ops class per non-ExtensionDtype data type, we ought to make the conversion from/to pandas data-type-based for non-ExtensionDtypes. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Unit tests. Closes #32592 from xinrong-databricks/datatypeop_pd_conversion. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
121 lines
4.8 KiB
Python
121 lines
4.8 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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import pandas as pd
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from pyspark.pandas.internal import (
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InternalFrame,
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SPARK_DEFAULT_INDEX_NAME,
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SPARK_INDEX_NAME_FORMAT,
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)
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from pyspark.pandas.utils import spark_column_equals
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from pyspark.testing.pandasutils import PandasOnSparkTestCase
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from pyspark.testing.sqlutils import SQLTestUtils
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class InternalFrameTest(PandasOnSparkTestCase, SQLTestUtils):
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def test_from_pandas(self):
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pdf = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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internal = InternalFrame.from_pandas(pdf)
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sdf = internal.spark_frame
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self.assert_eq(internal.index_spark_column_names, [SPARK_DEFAULT_INDEX_NAME])
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self.assert_eq(internal.index_names, [None])
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self.assert_eq(internal.column_labels, [("a",), ("b",)])
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self.assert_eq(internal.data_spark_column_names, ["a", "b"])
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self.assertTrue(spark_column_equals(internal.spark_column_for(("a",)), sdf["a"]))
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self.assertTrue(spark_column_equals(internal.spark_column_for(("b",)), sdf["b"]))
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self.assert_eq(internal.to_pandas_frame, pdf)
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# non-string column name
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pdf1 = pd.DataFrame({0: [1, 2, 3], 1: [4, 5, 6]})
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internal = InternalFrame.from_pandas(pdf1)
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sdf = internal.spark_frame
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self.assert_eq(internal.index_spark_column_names, [SPARK_DEFAULT_INDEX_NAME])
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self.assert_eq(internal.index_names, [None])
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self.assert_eq(internal.column_labels, [(0,), (1,)])
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self.assert_eq(internal.data_spark_column_names, ["0", "1"])
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self.assertTrue(spark_column_equals(internal.spark_column_for((0,)), sdf["0"]))
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self.assertTrue(spark_column_equals(internal.spark_column_for((1,)), sdf["1"]))
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self.assert_eq(internal.to_pandas_frame, pdf1)
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# categorical column
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pdf2 = pd.DataFrame({0: [1, 2, 3], 1: pd.Categorical([4, 5, 6])})
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internal = InternalFrame.from_pandas(pdf2)
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sdf = internal.spark_frame
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self.assert_eq(internal.index_spark_column_names, [SPARK_DEFAULT_INDEX_NAME])
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self.assert_eq(internal.index_names, [None])
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self.assert_eq(internal.column_labels, [(0,), (1,)])
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self.assert_eq(internal.data_spark_column_names, ["0", "1"])
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self.assertTrue(spark_column_equals(internal.spark_column_for((0,)), sdf["0"]))
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self.assertTrue(spark_column_equals(internal.spark_column_for((1,)), sdf["1"]))
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self.assert_eq(internal.to_pandas_frame, pdf2)
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# multi-index
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pdf.set_index("a", append=True, inplace=True)
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internal = InternalFrame.from_pandas(pdf)
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sdf = internal.spark_frame
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self.assert_eq(
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internal.index_spark_column_names,
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[SPARK_INDEX_NAME_FORMAT(0), SPARK_INDEX_NAME_FORMAT(1)],
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)
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self.assert_eq(internal.index_names, [None, ("a",)])
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self.assert_eq(internal.column_labels, [("b",)])
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self.assert_eq(internal.data_spark_column_names, ["b"])
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self.assertTrue(spark_column_equals(internal.spark_column_for(("b",)), sdf["b"]))
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self.assert_eq(internal.to_pandas_frame, pdf)
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# multi-index columns
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pdf.columns = pd.MultiIndex.from_tuples([("x", "b")])
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internal = InternalFrame.from_pandas(pdf)
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sdf = internal.spark_frame
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self.assert_eq(
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internal.index_spark_column_names,
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[SPARK_INDEX_NAME_FORMAT(0), SPARK_INDEX_NAME_FORMAT(1)],
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)
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self.assert_eq(internal.index_names, [None, ("a",)])
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self.assert_eq(internal.column_labels, [("x", "b")])
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self.assert_eq(internal.data_spark_column_names, ["(x, b)"])
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self.assertTrue(spark_column_equals(internal.spark_column_for(("x", "b")), sdf["(x, b)"]))
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self.assert_eq(internal.to_pandas_frame, pdf)
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if __name__ == "__main__":
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import unittest
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from pyspark.pandas.tests.test_internal import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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