python/pyspark/pandas/tests/computation/test_pivot_table_adv.py

#
# 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.
#
import unittest

import numpy as np
import pandas as pd

from pyspark import pandas as ps
from pyspark.testing.pandasutils import PandasOnSparkTestCase
from pyspark.testing.sqlutils import SQLTestUtils


class PivotTableAdvMixin:
    def test_pivot_table(self):
        pdf = pd.DataFrame(
            {
                "a": [4, 2, 3, 4, 8, 6],
                "b": [1, 2, 2, 4, 2, 4],
                "e": [10, 20, 20, 40, 20, 40],
                "c": [1, 2, 9, 4, 7, 4],
                "d": [-1, -2, -3, -4, -5, -6],
            },
            index=np.random.rand(6),
        )
        psdf = ps.from_pandas(pdf)

        self.assert_eq(
            psdf.pivot_table(
                index=["c"], columns="a", values=["b", "e", "d"], aggfunc="sum"
            ).sort_index(),
            pdf.pivot_table(
                index=["c"], columns="a", values=["b", "e", "d"], aggfunc="sum"
            ).sort_index(),
            almost=True,
        )

        self.assert_eq(
            psdf.pivot_table(
                index=["c"], columns="a", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"}
            ).sort_index(),
            pdf.pivot_table(
                index=["c"], columns="a", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"}
            ).sort_index(),
            almost=True,
        )

        self.assert_eq(
            psdf.pivot_table(index=["e", "c"], columns="a", values="b").sort_index(),
            pdf.pivot_table(index=["e", "c"], columns="a", values="b").sort_index(),
            almost=True,
        )

        self.assert_eq(
            psdf.pivot_table(
                index=["e", "c"], columns="a", values="b", fill_value=999
            ).sort_index(),
            pdf.pivot_table(index=["e", "c"], columns="a", values="b", fill_value=999).sort_index(),
            almost=True,
        )


class PivotTableAdvTests(
    PivotTableAdvMixin,
    PandasOnSparkTestCase,
    SQLTestUtils,
):
    pass


if __name__ == "__main__":
    from pyspark.pandas.tests.computation.test_pivot_table_adv 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)