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test_stats.py
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test_stats.py
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#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed 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 distutils.version import LooseVersion
import numpy as np
import pandas as pd
from databricks import koalas as ks
from databricks.koalas.config import option_context
from databricks.koalas.testing.utils import (
ReusedSQLTestCase,
SQLTestUtils,
SPARK_CONF_ARROW_ENABLED,
)
class StatsTest(ReusedSQLTestCase, SQLTestUtils):
def _test_stat_functions(self, pdf_or_pser, kdf_or_kser):
functions = ["max", "min", "mean", "sum", "count"]
for funcname in functions:
self.assert_eq(getattr(kdf_or_kser, funcname)(), getattr(pdf_or_pser, funcname)())
functions = ["std", "var", "product"]
for funcname in functions:
self.assert_eq(
getattr(kdf_or_kser, funcname)(),
getattr(pdf_or_pser, funcname)(),
check_exact=False,
)
functions = ["std", "var"]
for funcname in functions:
self.assert_eq(
getattr(kdf_or_kser, funcname)(ddof=0),
getattr(pdf_or_pser, funcname)(ddof=0),
check_exact=False,
)
# NOTE: To test skew, kurt, and median, just make sure they run.
# The numbers are different in spark and pandas.
functions = ["skew", "kurt", "median"]
for funcname in functions:
getattr(kdf_or_kser, funcname)()
def test_stat_functions(self):
pdf = pd.DataFrame({"A": [1, 2, 3, 4], "B": [1, 2, 3, 4], "C": [1, np.nan, 3, np.nan]})
kdf = ks.from_pandas(pdf)
self._test_stat_functions(pdf.A, kdf.A)
self._test_stat_functions(pdf, kdf)
# empty
self._test_stat_functions(pdf.A.loc[[]], kdf.A.loc[[]])
self._test_stat_functions(pdf.loc[[]], kdf.loc[[]])
def test_stat_functions_multiindex_column(self):
arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
pdf = pd.DataFrame(np.random.randn(3, 4), index=["A", "B", "C"], columns=arrays)
kdf = ks.from_pandas(pdf)
self._test_stat_functions(pdf.A, kdf.A)
self._test_stat_functions(pdf, kdf)
def test_stat_functions_with_no_numeric_columns(self):
pdf = pd.DataFrame(
{
"A": pd.date_range("2020-01-01", periods=3),
"B": pd.date_range("2021-01-01", periods=3),
}
)
kdf = ks.from_pandas(pdf)
self._test_stat_functions(pdf, kdf)
def test_sum(self):
pdf = pd.DataFrame({"a": [1, 2, 3, np.nan], "b": [0.1, np.nan, 0.3, np.nan]})
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.sum(), pdf.sum())
self.assert_eq(kdf.sum(axis=1), pdf.sum(axis=1))
self.assert_eq(kdf.sum(min_count=3), pdf.sum(min_count=3))
self.assert_eq(kdf.sum(axis=1, min_count=1), pdf.sum(axis=1, min_count=1))
self.assert_eq(kdf.loc[[]].sum(), pdf.loc[[]].sum())
self.assert_eq(kdf.loc[[]].sum(min_count=1), pdf.loc[[]].sum(min_count=1))
self.assert_eq(kdf["a"].sum(), pdf["a"].sum())
self.assert_eq(kdf["a"].sum(min_count=3), pdf["a"].sum(min_count=3))
self.assert_eq(kdf["b"].sum(min_count=3), pdf["b"].sum(min_count=3))
self.assert_eq(kdf["a"].loc[[]].sum(), pdf["a"].loc[[]].sum())
self.assert_eq(kdf["a"].loc[[]].sum(min_count=1), pdf["a"].loc[[]].sum(min_count=1))
def test_product(self):
pdf = pd.DataFrame(
{"a": [1, -2, -3, np.nan], "b": [0.1, np.nan, -0.3, np.nan], "c": [10, 20, 0, -10]}
)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.product(), pdf.product(), check_exact=False)
self.assert_eq(kdf.product(axis=1), pdf.product(axis=1))
self.assert_eq(kdf.product(min_count=3), pdf.product(min_count=3), check_exact=False)
self.assert_eq(kdf.product(axis=1, min_count=1), pdf.product(axis=1, min_count=1))
self.assert_eq(kdf.loc[[]].product(), pdf.loc[[]].product())
self.assert_eq(kdf.loc[[]].product(min_count=1), pdf.loc[[]].product(min_count=1))
self.assert_eq(kdf["a"].product(), pdf["a"].product(), check_exact=False)
self.assert_eq(
kdf["a"].product(min_count=3), pdf["a"].product(min_count=3), check_exact=False
)
self.assert_eq(kdf["b"].product(min_count=3), pdf["b"].product(min_count=3))
self.assert_eq(kdf["c"].product(min_count=3), pdf["c"].product(min_count=3))
self.assert_eq(kdf["a"].loc[[]].product(), pdf["a"].loc[[]].product())
self.assert_eq(kdf["a"].loc[[]].product(min_count=1), pdf["a"].loc[[]].product(min_count=1))
def test_abs(self):
pdf = pd.DataFrame(
{
"A": [1, -2, np.nan, -4, 5],
"B": [1.0, -2, np.nan, -4, 5],
"C": [-6.0, -7, -8, np.nan, 10],
"D": ["a", "b", "c", "d", np.nan],
"E": [True, np.nan, False, True, True],
}
)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.A.abs(), pdf.A.abs())
self.assert_eq(kdf.B.abs(), pdf.B.abs())
self.assert_eq(kdf.E.abs(), pdf.E.abs())
# pandas' bug?
# self.assert_eq(kdf[["B", "C", "E"]].abs(), pdf[["B", "C", "E"]].abs())
self.assert_eq(kdf[["B", "C"]].abs(), pdf[["B", "C"]].abs())
self.assert_eq(kdf[["E"]].abs(), pdf[["E"]].abs())
with self.assertRaisesRegex(
TypeError, "bad operand type for abs\\(\\): object \\(string\\)"
):
kdf.abs()
with self.assertRaisesRegex(
TypeError, "bad operand type for abs\\(\\): object \\(string\\)"
):
kdf.D.abs()
def test_axis_on_dataframe(self):
# The number of each count is intentionally big
# because when data is small, it executes a shortcut.
# Less than 'compute.shortcut_limit' will execute a shortcut
# by using collected pandas dataframe directly.
# now we set the 'compute.shortcut_limit' as 1000 explicitly
with option_context("compute.shortcut_limit", 1000):
pdf = pd.DataFrame(
{
"A": [1, -2, 3, -4, 5] * 300,
"B": [1.0, -2, 3, -4, 5] * 300,
"C": [-6.0, -7, -8, -9, 10] * 300,
"D": [True, False, True, False, False] * 300,
}
)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.count(axis=1), pdf.count(axis=1))
self.assert_eq(kdf.var(axis=1), pdf.var(axis=1))
self.assert_eq(kdf.var(axis=1, ddof=0), pdf.var(axis=1, ddof=0))
self.assert_eq(kdf.std(axis=1), pdf.std(axis=1))
self.assert_eq(kdf.std(axis=1, ddof=0), pdf.std(axis=1, ddof=0))
self.assert_eq(kdf.max(axis=1), pdf.max(axis=1))
self.assert_eq(kdf.min(axis=1), pdf.min(axis=1))
self.assert_eq(kdf.sum(axis=1), pdf.sum(axis=1))
self.assert_eq(kdf.product(axis=1), pdf.product(axis=1))
self.assert_eq(kdf.kurtosis(axis=1), pdf.kurtosis(axis=1))
self.assert_eq(kdf.skew(axis=1), pdf.skew(axis=1))
self.assert_eq(kdf.mean(axis=1), pdf.mean(axis=1))
def test_corr(self):
# Disable arrow execution since corr() is using UDT internally which is not supported.
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
# DataFrame
# we do not handle NaNs for now
pdf = pd.util.testing.makeMissingDataframe(0.3, 42).fillna(0)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.corr(), pdf.corr(), check_exact=False)
# Series
pser_a = pdf.A
pser_b = pdf.B
kser_a = kdf.A
kser_b = kdf.B
self.assertAlmostEqual(kser_a.corr(kser_b), pser_a.corr(pser_b))
self.assertRaises(TypeError, lambda: kser_a.corr(kdf))
# multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")])
pdf.columns = columns
kdf.columns = columns
self.assert_eq(kdf.corr(), pdf.corr(), check_exact=False)
# Series
pser_xa = pdf[("X", "A")]
pser_xb = pdf[("X", "B")]
kser_xa = kdf[("X", "A")]
kser_xb = kdf[("X", "B")]
self.assert_eq(kser_xa.corr(kser_xb), pser_xa.corr(pser_xb), almost=True)
def test_cov_corr_meta(self):
# Disable arrow execution since corr() is using UDT internally which is not supported.
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
pdf = pd.DataFrame(
{
"a": np.array([1, 2, 3], dtype="i1"),
"b": np.array([1, 2, 3], dtype="i2"),
"c": np.array([1, 2, 3], dtype="i4"),
"d": np.array([1, 2, 3]),
"e": np.array([1.0, 2.0, 3.0], dtype="f4"),
"f": np.array([1.0, 2.0, 3.0]),
"g": np.array([True, False, True]),
"h": np.array(list("abc")),
},
index=pd.Index([1, 2, 3], name="myindex"),
)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.corr(), pdf.corr())
def test_stats_on_boolean_dataframe(self):
pdf = pd.DataFrame({"A": [True, False, True], "B": [False, False, True]})
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.min(), pdf.min())
self.assert_eq(kdf.max(), pdf.max())
self.assert_eq(kdf.count(), pdf.count())
self.assert_eq(kdf.sum(), pdf.sum())
self.assert_eq(kdf.product(), pdf.product())
self.assert_eq(kdf.mean(), pdf.mean())
self.assert_eq(kdf.var(), pdf.var(), check_exact=False)
self.assert_eq(kdf.var(ddof=0), pdf.var(ddof=0), check_exact=False)
self.assert_eq(kdf.std(), pdf.std(), check_exact=False)
self.assert_eq(kdf.std(ddof=0), pdf.std(ddof=0), check_exact=False)
def test_stats_on_boolean_series(self):
pser = pd.Series([True, False, True])
kser = ks.from_pandas(pser)
self.assert_eq(kser.min(), pser.min())
self.assert_eq(kser.max(), pser.max())
self.assert_eq(kser.count(), pser.count())
self.assert_eq(kser.sum(), pser.sum())
self.assert_eq(kser.product(), pser.product())
self.assert_eq(kser.mean(), pser.mean())
self.assert_eq(kser.var(), pser.var(), almost=True)
self.assert_eq(kser.var(ddof=0), pser.var(ddof=0), almost=True)
self.assert_eq(kser.std(), pser.std(), almost=True)
self.assert_eq(kser.std(ddof=0), pser.std(ddof=0), almost=True)
def test_stats_on_non_numeric_columns_should_be_discarded_if_numeric_only_is_true(self):
pdf = pd.DataFrame({"i": [0, 1, 2], "b": [False, False, True], "s": ["x", "y", "z"]})
kdf = ks.from_pandas(pdf)
self.assert_eq(
kdf[["i", "s"]].max(numeric_only=True), pdf[["i", "s"]].max(numeric_only=True)
)
self.assert_eq(
kdf[["b", "s"]].max(numeric_only=True), pdf[["b", "s"]].max(numeric_only=True)
)
self.assert_eq(
kdf[["i", "s"]].min(numeric_only=True), pdf[["i", "s"]].min(numeric_only=True)
)
self.assert_eq(
kdf[["b", "s"]].min(numeric_only=True), pdf[["b", "s"]].min(numeric_only=True)
)
self.assert_eq(kdf.count(numeric_only=True), pdf.count(numeric_only=True))
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self.assert_eq(kdf.sum(numeric_only=True), pdf.sum(numeric_only=True))
self.assert_eq(kdf.product(numeric_only=True), pdf.product(numeric_only=True))
else:
self.assert_eq(kdf.sum(numeric_only=True), pdf.sum(numeric_only=True).astype(int))
self.assert_eq(
kdf.product(numeric_only=True), pdf.product(numeric_only=True).astype(int)
)
self.assert_eq(kdf.mean(numeric_only=True), pdf.mean(numeric_only=True))
self.assert_eq(kdf.var(numeric_only=True), pdf.var(numeric_only=True), check_exact=False)
self.assert_eq(
kdf.var(ddof=0, numeric_only=True),
pdf.var(ddof=0, numeric_only=True),
check_exact=False,
)
self.assert_eq(kdf.std(numeric_only=True), pdf.std(numeric_only=True), check_exact=False)
self.assert_eq(
kdf.std(ddof=0, numeric_only=True),
pdf.std(ddof=0, numeric_only=True),
check_exact=False,
)
self.assert_eq(len(kdf.median(numeric_only=True)), len(pdf.median(numeric_only=True)))
self.assert_eq(len(kdf.kurtosis(numeric_only=True)), len(pdf.kurtosis(numeric_only=True)))
self.assert_eq(len(kdf.skew(numeric_only=True)), len(pdf.skew(numeric_only=True)))
self.assert_eq(
len(kdf.quantile(q=0.5, numeric_only=True)), len(pdf.quantile(q=0.5, numeric_only=True))
)
self.assert_eq(
len(kdf.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"]})
kdf = ks.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self.assert_eq(kdf.sum(numeric_only=True), pdf.sum(numeric_only=True))
self.assert_eq(
kdf[["i", "b"]].sum(numeric_only=False), pdf[["i", "b"]].sum(numeric_only=False)
)
else:
self.assert_eq(kdf.sum(numeric_only=True), pdf.sum(numeric_only=True).astype(int))
self.assert_eq(
kdf[["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"):
kdf.sum(numeric_only=False)
with self.assertRaisesRegex(TypeError, "Could not convert object \\(string\\) to numeric"):
kdf.s.sum()