6b912e4179
### What changes were proposed in this pull request? There are still naming related to Koalas in test and function name. This PR addressed them to fit pandas-on-spark. - kdf -> psdf - kser -> psser - kidx -> psidx - kmidx -> psmidx - to_koalas() -> to_pandas_on_spark() ### Why are the changes needed? This is because the name Koalas is no longer used in PySpark. ### Does this PR introduce _any_ user-facing change? `to_koalas()` function is renamed to `to_pandas_on_spark()` ### How was this patch tested? Tested in local manually. After changing the related naming, I checked them one by one. Closes #32516 from itholic/SPARK-35364. Authored-by: itholic <haejoon.lee@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
421 lines
18 KiB
Python
421 lines
18 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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try:
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from pandas._testing import makeMissingDataframe
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except ImportError:
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from pandas.util.testing import makeMissingDataframe
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from pyspark import pandas as ps
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from pyspark.pandas.config import option_context
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from pyspark.testing.pandasutils import PandasOnSparkTestCase, SPARK_CONF_ARROW_ENABLED
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from pyspark.testing.sqlutils import SQLTestUtils
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class StatsTest(PandasOnSparkTestCase, SQLTestUtils):
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def _test_stat_functions(self, pdf_or_pser, psdf_or_psser):
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functions = ["max", "min", "mean", "sum", "count"]
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for funcname in functions:
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self.assert_eq(getattr(psdf_or_psser, funcname)(), getattr(pdf_or_pser, funcname)())
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functions = ["std", "var", "product", "sem"]
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for funcname in functions:
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self.assert_eq(
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getattr(psdf_or_psser, funcname)(),
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getattr(pdf_or_pser, funcname)(),
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check_exact=False,
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)
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functions = ["std", "var", "sem"]
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for funcname in functions:
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self.assert_eq(
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getattr(psdf_or_psser, funcname)(ddof=0),
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getattr(pdf_or_pser, funcname)(ddof=0),
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check_exact=False,
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)
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# NOTE: To test skew, kurt, and median, just make sure they run.
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# The numbers are different in spark and pandas.
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functions = ["skew", "kurt", "median"]
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for funcname in functions:
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getattr(psdf_or_psser, funcname)()
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def test_stat_functions(self):
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pdf = pd.DataFrame({"A": [1, 2, 3, 4], "B": [1, 2, 3, 4], "C": [1, np.nan, 3, np.nan]})
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psdf = ps.from_pandas(pdf)
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self._test_stat_functions(pdf.A, psdf.A)
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self._test_stat_functions(pdf, psdf)
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# empty
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self._test_stat_functions(pdf.A.loc[[]], psdf.A.loc[[]])
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self._test_stat_functions(pdf.loc[[]], psdf.loc[[]])
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def test_stat_functions_multiindex_column(self):
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arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
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pdf = pd.DataFrame(np.random.randn(3, 4), index=["A", "B", "C"], columns=arrays)
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psdf = ps.from_pandas(pdf)
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self._test_stat_functions(pdf.A, psdf.A)
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self._test_stat_functions(pdf, psdf)
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def test_stat_functions_with_no_numeric_columns(self):
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pdf = pd.DataFrame(
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{
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"A": ["a", None, "c", "d", None, "f", "g"],
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"B": ["A", "B", "C", None, "E", "F", None],
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}
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)
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psdf = ps.from_pandas(pdf)
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self._test_stat_functions(pdf, psdf)
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def test_sum(self):
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pdf = pd.DataFrame({"a": [1, 2, 3, np.nan], "b": [0.1, np.nan, 0.3, np.nan]})
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.sum(), pdf.sum())
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self.assert_eq(psdf.sum(axis=1), pdf.sum(axis=1))
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self.assert_eq(psdf.sum(min_count=3), pdf.sum(min_count=3))
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self.assert_eq(psdf.sum(axis=1, min_count=1), pdf.sum(axis=1, min_count=1))
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self.assert_eq(psdf.loc[[]].sum(), pdf.loc[[]].sum())
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self.assert_eq(psdf.loc[[]].sum(min_count=1), pdf.loc[[]].sum(min_count=1))
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self.assert_eq(psdf["a"].sum(), pdf["a"].sum())
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self.assert_eq(psdf["a"].sum(min_count=3), pdf["a"].sum(min_count=3))
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self.assert_eq(psdf["b"].sum(min_count=3), pdf["b"].sum(min_count=3))
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self.assert_eq(psdf["a"].loc[[]].sum(), pdf["a"].loc[[]].sum())
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self.assert_eq(psdf["a"].loc[[]].sum(min_count=1), pdf["a"].loc[[]].sum(min_count=1))
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def test_product(self):
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pdf = pd.DataFrame(
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{"a": [1, -2, -3, np.nan], "b": [0.1, np.nan, -0.3, np.nan], "c": [10, 20, 0, -10]}
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)
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.product(), pdf.product(), check_exact=False)
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self.assert_eq(psdf.product(axis=1), pdf.product(axis=1))
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self.assert_eq(psdf.product(min_count=3), pdf.product(min_count=3), check_exact=False)
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self.assert_eq(psdf.product(axis=1, min_count=1), pdf.product(axis=1, min_count=1))
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self.assert_eq(psdf.loc[[]].product(), pdf.loc[[]].product())
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self.assert_eq(psdf.loc[[]].product(min_count=1), pdf.loc[[]].product(min_count=1))
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self.assert_eq(psdf["a"].product(), pdf["a"].product(), check_exact=False)
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self.assert_eq(
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psdf["a"].product(min_count=3), pdf["a"].product(min_count=3), check_exact=False
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)
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self.assert_eq(psdf["b"].product(min_count=3), pdf["b"].product(min_count=3))
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self.assert_eq(psdf["c"].product(min_count=3), pdf["c"].product(min_count=3))
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self.assert_eq(psdf["a"].loc[[]].product(), pdf["a"].loc[[]].product())
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self.assert_eq(
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psdf["a"].loc[[]].product(min_count=1), pdf["a"].loc[[]].product(min_count=1)
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)
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def test_abs(self):
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pdf = pd.DataFrame(
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{
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"A": [1, -2, np.nan, -4, 5],
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"B": [1.0, -2, np.nan, -4, 5],
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"C": [-6.0, -7, -8, np.nan, 10],
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"D": ["a", "b", "c", "d", np.nan],
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"E": [True, np.nan, False, True, True],
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}
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)
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.A.abs(), pdf.A.abs())
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self.assert_eq(psdf.B.abs(), pdf.B.abs())
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self.assert_eq(psdf.E.abs(), pdf.E.abs())
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# pandas' bug?
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# self.assert_eq(psdf[["B", "C", "E"]].abs(), pdf[["B", "C", "E"]].abs())
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self.assert_eq(psdf[["B", "C"]].abs(), pdf[["B", "C"]].abs())
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self.assert_eq(psdf[["E"]].abs(), pdf[["E"]].abs())
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with self.assertRaisesRegex(
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TypeError, "bad operand type for abs\\(\\): object \\(string\\)"
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):
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psdf.abs()
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with self.assertRaisesRegex(
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TypeError, "bad operand type for abs\\(\\): object \\(string\\)"
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):
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psdf.D.abs()
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def test_axis_on_dataframe(self):
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# The number of each count is intentionally big
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# because when data is small, it executes a shortcut.
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# Less than 'compute.shortcut_limit' will execute a shortcut
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# by using collected pandas dataframe directly.
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# now we set the 'compute.shortcut_limit' as 1000 explicitly
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with option_context("compute.shortcut_limit", 1000):
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pdf = pd.DataFrame(
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{
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"A": [1, -2, 3, -4, 5] * 300,
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"B": [1.0, -2, 3, -4, 5] * 300,
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"C": [-6.0, -7, -8, -9, 10] * 300,
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"D": [True, False, True, False, False] * 300,
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},
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index=range(10, 15001, 10),
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)
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.count(axis=1), pdf.count(axis=1))
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self.assert_eq(psdf.var(axis=1), pdf.var(axis=1))
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self.assert_eq(psdf.var(axis=1, ddof=0), pdf.var(axis=1, ddof=0))
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self.assert_eq(psdf.std(axis=1), pdf.std(axis=1))
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self.assert_eq(psdf.std(axis=1, ddof=0), pdf.std(axis=1, ddof=0))
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self.assert_eq(psdf.max(axis=1), pdf.max(axis=1))
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self.assert_eq(psdf.min(axis=1), pdf.min(axis=1))
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self.assert_eq(psdf.sum(axis=1), pdf.sum(axis=1))
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self.assert_eq(psdf.product(axis=1), pdf.product(axis=1))
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self.assert_eq(psdf.kurtosis(axis=1), pdf.kurtosis(axis=1))
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self.assert_eq(psdf.skew(axis=1), pdf.skew(axis=1))
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self.assert_eq(psdf.mean(axis=1), pdf.mean(axis=1))
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self.assert_eq(psdf.sem(axis=1), pdf.sem(axis=1))
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self.assert_eq(psdf.sem(axis=1, ddof=0), pdf.sem(axis=1, ddof=0))
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self.assert_eq(
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psdf.count(axis=1, numeric_only=True), pdf.count(axis=1, numeric_only=True)
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)
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self.assert_eq(psdf.var(axis=1, numeric_only=True), pdf.var(axis=1, numeric_only=True))
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self.assert_eq(
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psdf.var(axis=1, ddof=0, numeric_only=True),
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pdf.var(axis=1, ddof=0, numeric_only=True),
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)
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self.assert_eq(psdf.std(axis=1, numeric_only=True), pdf.std(axis=1, numeric_only=True))
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self.assert_eq(
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psdf.std(axis=1, ddof=0, numeric_only=True),
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pdf.std(axis=1, ddof=0, numeric_only=True),
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)
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self.assert_eq(
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psdf.max(axis=1, numeric_only=True),
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pdf.max(axis=1, numeric_only=True).astype(float),
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)
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self.assert_eq(
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psdf.min(axis=1, numeric_only=True),
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pdf.min(axis=1, numeric_only=True).astype(float),
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)
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self.assert_eq(
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psdf.sum(axis=1, numeric_only=True),
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pdf.sum(axis=1, numeric_only=True).astype(float),
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)
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self.assert_eq(
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psdf.product(axis=1, numeric_only=True),
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pdf.product(axis=1, numeric_only=True).astype(float),
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)
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self.assert_eq(
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psdf.kurtosis(axis=1, numeric_only=True), pdf.kurtosis(axis=1, numeric_only=True)
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)
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self.assert_eq(
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psdf.skew(axis=1, numeric_only=True), pdf.skew(axis=1, numeric_only=True)
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)
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self.assert_eq(
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psdf.mean(axis=1, numeric_only=True), pdf.mean(axis=1, numeric_only=True)
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)
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self.assert_eq(psdf.sem(axis=1, numeric_only=True), pdf.sem(axis=1, numeric_only=True))
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self.assert_eq(
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psdf.sem(axis=1, ddof=0, numeric_only=True),
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pdf.sem(axis=1, ddof=0, numeric_only=True),
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)
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def test_corr(self):
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# Disable arrow execution since corr() is using UDT internally which is not supported.
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with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
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# DataFrame
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# we do not handle NaNs for now
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pdf = makeMissingDataframe(0.3, 42).fillna(0)
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.corr(), pdf.corr(), check_exact=False)
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# Series
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pser_a = pdf.A
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pser_b = pdf.B
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psser_a = psdf.A
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psser_b = psdf.B
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self.assertAlmostEqual(psser_a.corr(psser_b), pser_a.corr(pser_b))
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self.assertRaises(TypeError, lambda: psser_a.corr(psdf))
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# multi-index columns
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columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")])
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pdf.columns = columns
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psdf.columns = columns
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self.assert_eq(psdf.corr(), pdf.corr(), check_exact=False)
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# Series
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pser_xa = pdf[("X", "A")]
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pser_xb = pdf[("X", "B")]
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psser_xa = psdf[("X", "A")]
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psser_xb = psdf[("X", "B")]
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self.assert_eq(psser_xa.corr(psser_xb), pser_xa.corr(pser_xb), almost=True)
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def test_cov_corr_meta(self):
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# Disable arrow execution since corr() is using UDT internally which is not supported.
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with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
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pdf = pd.DataFrame(
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{
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"a": np.array([1, 2, 3], dtype="i1"),
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"b": np.array([1, 2, 3], dtype="i2"),
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"c": np.array([1, 2, 3], dtype="i4"),
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"d": np.array([1, 2, 3]),
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"e": np.array([1.0, 2.0, 3.0], dtype="f4"),
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"f": np.array([1.0, 2.0, 3.0]),
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"g": np.array([True, False, True]),
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"h": np.array(list("abc")),
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},
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index=pd.Index([1, 2, 3], name="myindex"),
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)
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.corr(), pdf.corr())
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def test_stats_on_boolean_dataframe(self):
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pdf = pd.DataFrame({"A": [True, False, True], "B": [False, False, True]})
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psdf = ps.from_pandas(pdf)
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self.assert_eq(psdf.min(), pdf.min())
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self.assert_eq(psdf.max(), pdf.max())
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self.assert_eq(psdf.count(), pdf.count())
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self.assert_eq(psdf.sum(), pdf.sum())
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self.assert_eq(psdf.product(), pdf.product())
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self.assert_eq(psdf.mean(), pdf.mean())
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self.assert_eq(psdf.var(), pdf.var(), check_exact=False)
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self.assert_eq(psdf.var(ddof=0), pdf.var(ddof=0), check_exact=False)
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self.assert_eq(psdf.std(), pdf.std(), check_exact=False)
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self.assert_eq(psdf.std(ddof=0), pdf.std(ddof=0), check_exact=False)
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self.assert_eq(psdf.sem(), pdf.sem(), check_exact=False)
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self.assert_eq(psdf.sem(ddof=0), pdf.sem(ddof=0), check_exact=False)
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def test_stats_on_boolean_series(self):
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pser = pd.Series([True, False, True])
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psser = ps.from_pandas(pser)
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self.assert_eq(psser.min(), pser.min())
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self.assert_eq(psser.max(), pser.max())
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self.assert_eq(psser.count(), pser.count())
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self.assert_eq(psser.sum(), pser.sum())
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self.assert_eq(psser.product(), pser.product())
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self.assert_eq(psser.mean(), pser.mean())
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self.assert_eq(psser.var(), pser.var(), almost=True)
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self.assert_eq(psser.var(ddof=0), pser.var(ddof=0), almost=True)
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self.assert_eq(psser.std(), pser.std(), almost=True)
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self.assert_eq(psser.std(ddof=0), pser.std(ddof=0), almost=True)
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self.assert_eq(psser.sem(), pser.sem(), almost=True)
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self.assert_eq(psser.sem(ddof=0), pser.sem(ddof=0), almost=True)
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def test_stats_on_non_numeric_columns_should_be_discarded_if_numeric_only_is_true(self):
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pdf = pd.DataFrame({"i": [0, 1, 2], "b": [False, False, True], "s": ["x", "y", "z"]})
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psdf = ps.from_pandas(pdf)
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self.assert_eq(
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psdf[["i", "s"]].max(numeric_only=True), pdf[["i", "s"]].max(numeric_only=True)
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)
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self.assert_eq(
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psdf[["b", "s"]].max(numeric_only=True), pdf[["b", "s"]].max(numeric_only=True)
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)
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self.assert_eq(
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psdf[["i", "s"]].min(numeric_only=True), pdf[["i", "s"]].min(numeric_only=True)
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)
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self.assert_eq(
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psdf[["b", "s"]].min(numeric_only=True), pdf[["b", "s"]].min(numeric_only=True)
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)
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self.assert_eq(psdf.count(numeric_only=True), pdf.count(numeric_only=True))
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if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
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self.assert_eq(psdf.sum(numeric_only=True), pdf.sum(numeric_only=True))
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self.assert_eq(psdf.product(numeric_only=True), pdf.product(numeric_only=True))
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else:
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self.assert_eq(psdf.sum(numeric_only=True), pdf.sum(numeric_only=True).astype(int))
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self.assert_eq(
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psdf.product(numeric_only=True), pdf.product(numeric_only=True).astype(int)
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)
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self.assert_eq(psdf.mean(numeric_only=True), pdf.mean(numeric_only=True))
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self.assert_eq(psdf.var(numeric_only=True), pdf.var(numeric_only=True), check_exact=False)
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self.assert_eq(
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psdf.var(ddof=0, numeric_only=True),
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pdf.var(ddof=0, numeric_only=True),
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check_exact=False,
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)
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self.assert_eq(psdf.std(numeric_only=True), pdf.std(numeric_only=True), check_exact=False)
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self.assert_eq(
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psdf.std(ddof=0, numeric_only=True),
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pdf.std(ddof=0, numeric_only=True),
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check_exact=False,
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)
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self.assert_eq(psdf.sem(numeric_only=True), pdf.sem(numeric_only=True), check_exact=False)
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self.assert_eq(
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psdf.sem(ddof=0, numeric_only=True),
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pdf.sem(ddof=0, numeric_only=True),
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|
check_exact=False,
|
|
)
|
|
|
|
self.assert_eq(len(psdf.median(numeric_only=True)), len(pdf.median(numeric_only=True)))
|
|
self.assert_eq(len(psdf.kurtosis(numeric_only=True)), len(pdf.kurtosis(numeric_only=True)))
|
|
self.assert_eq(len(psdf.skew(numeric_only=True)), len(pdf.skew(numeric_only=True)))
|
|
|
|
self.assert_eq(
|
|
len(psdf.quantile(q=0.5, numeric_only=True)),
|
|
len(pdf.quantile(q=0.5, numeric_only=True)),
|
|
)
|
|
self.assert_eq(
|
|
len(psdf.quantile(q=[0.25, 0.5, 0.75], numeric_only=True)),
|
|
len(pdf.quantile(q=[0.25, 0.5, 0.75], numeric_only=True)),
|
|
)
|
|
|
|
def test_numeric_only_unsupported(self):
|
|
pdf = pd.DataFrame({"i": [0, 1, 2], "b": [False, False, True], "s": ["x", "y", "z"]})
|
|
psdf = ps.from_pandas(pdf)
|
|
|
|
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
|
|
self.assert_eq(psdf.sum(numeric_only=True), pdf.sum(numeric_only=True))
|
|
self.assert_eq(
|
|
psdf[["i", "b"]].sum(numeric_only=False), pdf[["i", "b"]].sum(numeric_only=False)
|
|
)
|
|
else:
|
|
self.assert_eq(psdf.sum(numeric_only=True), pdf.sum(numeric_only=True).astype(int))
|
|
self.assert_eq(
|
|
psdf[["i", "b"]].sum(numeric_only=False),
|
|
pdf[["i", "b"]].sum(numeric_only=False).astype(int),
|
|
)
|
|
|
|
with self.assertRaisesRegex(TypeError, "Could not convert object \\(string\\) to numeric"):
|
|
psdf.sum(numeric_only=False)
|
|
|
|
with self.assertRaisesRegex(TypeError, "Could not convert object \\(string\\) to numeric"):
|
|
psdf.s.sum()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
from pyspark.pandas.tests.test_stats 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)
|