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Fix value_counts() to work properly when dropna is True #1116

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merged 11 commits into from
Dec 18, 2019
Merged

Fix value_counts() to work properly when dropna is True #1116

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37206db
fix
itholic Dec 11, 2019
21c8431
fix comment & rollback useless change
itholic Dec 11, 2019
f2f3df5
fix
itholic Dec 11, 2019
a895436
fix for index
itholic Dec 11, 2019
64d1fb6
fix & add tests
itholic Dec 12, 2019
0362cc2
support for pyspark2.3 with set option
itholic Dec 12, 2019
d2271db
fix
itholic Dec 12, 2019
1299d0e
fix
itholic Dec 12, 2019
0210ebe
fix test logic
itholic Dec 13, 2019
204976e
raise error only for multiindex
itholic Dec 13, 2019
c04be15
fix test more properly
itholic Dec 13, 2019
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16 changes: 12 additions & 4 deletions databricks/koalas/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,9 @@

from functools import wraps
from typing import Union, Callable, Any
from distutils.version import LooseVersion

import pyspark
import numpy as np
import pandas as pd
from pandas.api.types import is_list_like
Expand All @@ -33,7 +35,7 @@
from databricks.koalas import numpy_compat
from databricks.koalas.internal import _InternalFrame, SPARK_INDEX_NAME_FORMAT
from databricks.koalas.typedef import pandas_wraps, spark_type_to_pandas_dtype
from databricks.koalas.utils import align_diff_series, scol_for, validate_axis
from databricks.koalas.utils import align_diff_series, scol_for, validate_axis, default_session
from databricks.koalas.frame import DataFrame


Expand Down Expand Up @@ -941,15 +943,21 @@ def value_counts(self, normalize=False, sort=True, ascending=False, bins=None, d
Name: koalas, dtype: int64
"""
from databricks.koalas.series import Series, _col
if LooseVersion(pyspark.__version__) < LooseVersion("2.4") and \
default_session().conf.get("spark.sql.execution.arrow.enabled") == "true":
raise RuntimeError("if you're using pyspark < 2.4, set conf "
"'spark.sql.execution.arrow.enabled' to 'false' "
"for using this function")
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if bins is not None:
raise NotImplementedError("value_counts currently does not support bins")

if dropna:
sdf_dropna = self._internal._sdf.dropna()
sdf_dropna = self._internal._sdf.select(self._scol).dropna()
else:
sdf_dropna = self._internal._sdf
sdf_dropna = self._internal._sdf.select(self._scol)
index_name = SPARK_INDEX_NAME_FORMAT(0)
sdf = sdf_dropna.groupby(self._scol.alias(index_name)).count()
column_name = self._internal.data_columns[0]
sdf = sdf_dropna.groupby(scol_for(sdf_dropna, column_name).alias(index_name)).count()
if sort:
if ascending:
sdf = sdf.orderBy(F.col('count'))
Expand Down
134 changes: 134 additions & 0 deletions databricks/koalas/tests/test_series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
import pyspark
import numpy as np
import pandas as pd

Expand All @@ -33,6 +34,7 @@
from databricks.koalas.exceptions import PandasNotImplementedError
from databricks.koalas.missing.series import _MissingPandasLikeSeries
from databricks.koalas.config import set_option, reset_option
from databricks.koalas.utils import default_session


class SeriesTest(ReusedSQLTestCase, SQLTestUtils):
Expand Down Expand Up @@ -244,6 +246,13 @@ def test_nunique(self):
self.assertEqual(ks.Series(range(100)).nunique(approx=True, rsd=0.01), 100)

def test_value_counts(self):
set_arrow_conf = False
if LooseVersion(pyspark.__version__) < LooseVersion("2.4") and \
default_session().conf.get("spark.sql.execution.arrow.enabled") == "true":
default_session().conf.set("spark.sql.execution.arrow.enabled", "false")
set_arrow_conf = True
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Why do we need this?
If we need this, could you make sure to set back to the original conf?

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we need this to test at pyspark2.3 same reason with #1116 (comment),

i fixed the test logic to make sure setting back to original conf value.

Thanks for the review :)


# this is also containing test for Index & MultiIndex
pser = pd.Series([1, 2, 1, 3, 3, np.nan, 1, 4], name="x")
kser = ks.from_pandas(pser)

Expand All @@ -261,6 +270,15 @@ def test_value_counts(self):
self.assert_eq(kser.value_counts(ascending=True, dropna=False),
pser.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

with self.assertRaisesRegex(NotImplementedError,
"value_counts currently does not support bins"):
kser.value_counts(bins=3)
Expand All @@ -269,6 +287,122 @@ def test_value_counts(self):
kser.name = 'index'
self.assert_eq(kser.value_counts(), pser.value_counts(), almost=True)

# Series from DataFrame
pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [None, 1, None]})
kdf = ks.from_pandas(pdf)

self.assert_eq(kdf.a.value_counts(normalize=True),
pdf.a.value_counts(normalize=True), almost=True)
self.assert_eq(kdf.a.value_counts(ascending=True),
pdf.a.value_counts(ascending=True), almost=True)
self.assert_eq(kdf.a.value_counts(normalize=True, dropna=False),
pdf.a.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kdf.a.value_counts(ascending=True, dropna=False),
pdf.a.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

# Series with NaN index
pser = pd.Series([1, 2, 3], index=[2, None, 5])
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kser = ks.from_pandas(pser)

self.assert_eq(kser.value_counts(normalize=True),
pser.value_counts(normalize=True), almost=True)
self.assert_eq(kser.value_counts(ascending=True),
pser.value_counts(ascending=True), almost=True)
self.assert_eq(kser.value_counts(normalize=True, dropna=False),
pser.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.value_counts(ascending=True, dropna=False),
pser.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

# Series with MultiIndex
pser.index = pd.MultiIndex.from_tuples([('x', 'a'), ('x', 'b'), ('y', 'c')])
kser = ks.from_pandas(pser)

self.assert_eq(kser.value_counts(normalize=True),
pser.value_counts(normalize=True), almost=True)
self.assert_eq(kser.value_counts(ascending=True),
pser.value_counts(ascending=True), almost=True)
self.assert_eq(kser.value_counts(normalize=True, dropna=False),
pser.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.value_counts(ascending=True, dropna=False),
pser.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

# Series with MultiIndex some of index has NaN
pser.index = pd.MultiIndex.from_tuples([('x', 'a'), ('x', None), ('y', 'c')])
kser = ks.from_pandas(pser)

self.assert_eq(kser.value_counts(normalize=True),
pser.value_counts(normalize=True), almost=True)
self.assert_eq(kser.value_counts(ascending=True),
pser.value_counts(ascending=True), almost=True)
self.assert_eq(kser.value_counts(normalize=True, dropna=False),
pser.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.value_counts(ascending=True, dropna=False),
pser.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

# Series with MultiIndex some of index is NaN.
# This test only available for pandas >= 0.24.
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Why are these only for pandas >= 0.24?

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@itholic itholic Dec 13, 2019 •
edited
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because pandas < 0.24 doesn't support None for MultiIndex like below way.

>>> pd.__version__
'0.23.0'
>>> pidx = pd.MultiIndex.from_tuples([('x', 'a'), None, ('y', 'c')])
Traceback (most recent call last):
...
TypeError: object of type 'NoneType' has no len()

so i think this test should be ran on pandas >= 0.24

if LooseVersion(pd.__version__) >= LooseVersion("0.24"):
pser.index = pd.MultiIndex.from_tuples([('x', 'a'), None, ('y', 'c')])
kser = ks.from_pandas(pser)

self.assert_eq(kser.value_counts(normalize=True),
pser.value_counts(normalize=True), almost=True)
self.assert_eq(kser.value_counts(ascending=True),
pser.value_counts(ascending=True), almost=True)
self.assert_eq(kser.value_counts(normalize=True, dropna=False),
pser.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.value_counts(ascending=True, dropna=False),
pser.value_counts(ascending=True, dropna=False), almost=True)

self.assert_eq(kser.index.value_counts(normalize=True),
pser.index.value_counts(normalize=True), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True),
pser.index.value_counts(ascending=True), almost=True)
self.assert_eq(kser.index.value_counts(normalize=True, dropna=False),
pser.index.value_counts(normalize=True, dropna=False), almost=True)
self.assert_eq(kser.index.value_counts(ascending=True, dropna=False),
pser.index.value_counts(ascending=True, dropna=False), almost=True)

if set_arrow_conf:
default_session().conf.set("spark.sql.execution.arrow.enabled", "true")
self.assertRaises(RuntimeError, lambda: kser.value_counts())

def test_nsmallest(self):
sample_lst = [1, 2, 3, 4, np.nan, 6]
pser = pd.Series(sample_lst, name='x')
Expand Down