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test_categorical.py
805 lines (627 loc) · 28.6 KB
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test_categorical.py
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#
# 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.
#
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
import pyspark.pandas as ps
from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
class CategoricalTest(PandasOnSparkTestCase, TestUtils):
@property
def pdf(self):
return pd.DataFrame(
{
"a": pd.Categorical([1, 2, 3, 1, 2, 3]),
"b": pd.Categorical(
["b", "a", "c", "c", "b", "a"], categories=["c", "b", "d", "a"]
),
},
)
@property
def psdf(self):
return ps.from_pandas(self.pdf)
@property
def df_pair(self):
return self.pdf, self.psdf
def test_categorical_frame(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf, pdf)
self.assert_eq(psdf.a, pdf.a)
self.assert_eq(psdf.b, pdf.b)
self.assert_eq(psdf.index, pdf.index)
self.assert_eq(psdf.sort_index(), pdf.sort_index())
self.assert_eq(psdf.sort_values("b"), pdf.sort_values("b"))
def test_categorical_series(self):
pser = pd.Series([1, 2, 3], dtype="category")
psser = ps.Series([1, 2, 3], dtype="category")
self.assert_eq(psser, pser)
self.assert_eq(psser.cat.categories, pser.cat.categories)
self.assert_eq(psser.cat.codes, pser.cat.codes)
self.assert_eq(psser.cat.ordered, pser.cat.ordered)
def test_categories_setter(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
pser.cat.categories = ["z", "y", "x"]
psser.cat.categories = ["z", "y", "x"]
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=["x", "y", "z"]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
with self.assertRaises(ValueError):
psser.cat.categories = [1, 2, 3, 4]
def test_add_categories(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
self.assert_eq(pser.cat.add_categories(4), psser.cat.add_categories(4))
self.assert_eq(pser.cat.add_categories([4, 5]), psser.cat.add_categories([4, 5]))
self.assert_eq(pser.cat.add_categories([]), psser.cat.add_categories([]))
pser.cat.add_categories(4, inplace=True)
psser.cat.add_categories(4, inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[1, 2, 3, 4]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
self.assertRaises(ValueError, lambda: psser.cat.add_categories(4))
self.assertRaises(ValueError, lambda: psser.cat.add_categories([5, 5]))
def test_remove_categories(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
self.assert_eq(pser.cat.remove_categories(2), psser.cat.remove_categories(2))
self.assert_eq(pser.cat.remove_categories([1, 3]), psser.cat.remove_categories([1, 3]))
self.assert_eq(pser.cat.remove_categories([]), psser.cat.remove_categories([]))
self.assert_eq(pser.cat.remove_categories([2, 2]), psser.cat.remove_categories([2, 2]))
self.assert_eq(
pser.cat.remove_categories([1, 2, 3]), psser.cat.remove_categories([1, 2, 3])
)
self.assert_eq(pser.cat.remove_categories(None), psser.cat.remove_categories(None))
self.assert_eq(pser.cat.remove_categories([None]), psser.cat.remove_categories([None]))
pser.cat.remove_categories(2, inplace=True)
psser.cat.remove_categories(2, inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[1, 3]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
self.assertRaises(ValueError, lambda: psser.cat.remove_categories(4))
self.assertRaises(ValueError, lambda: psser.cat.remove_categories([4, None]))
def test_remove_unused_categories(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
self.assert_eq(pser.cat.remove_unused_categories(), psser.cat.remove_unused_categories())
pser.cat.add_categories(4, inplace=True)
pser.cat.remove_categories(2, inplace=True)
psser.cat.add_categories(4, inplace=True)
psser.cat.remove_categories(2, inplace=True)
self.assert_eq(pser.cat.remove_unused_categories(), psser.cat.remove_unused_categories())
pser.cat.remove_unused_categories(inplace=True)
psser.cat.remove_unused_categories(inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[1, 3]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
def test_reorder_categories(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
self.assert_eq(
pser.cat.reorder_categories([1, 2, 3]), psser.cat.reorder_categories([1, 2, 3])
)
self.assert_eq(
pser.cat.reorder_categories([1, 2, 3], ordered=True),
psser.cat.reorder_categories([1, 2, 3], ordered=True),
)
self.assert_eq(
pser.cat.reorder_categories([3, 2, 1]), psser.cat.reorder_categories([3, 2, 1])
)
self.assert_eq(
pser.cat.reorder_categories([3, 2, 1], ordered=True),
psser.cat.reorder_categories([3, 2, 1], ordered=True),
)
pser.cat.reorder_categories([1, 2, 3], inplace=True)
psser.cat.reorder_categories([1, 2, 3], inplace=True)
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
pser.cat.reorder_categories([3, 2, 1], ordered=True, inplace=True)
psser.cat.reorder_categories([3, 2, 1], ordered=True, inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[3, 2, 1], ordered=True))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2]))
self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2, 4]))
self.assertRaises(ValueError, lambda: psser.cat.reorder_categories([1, 2, 2]))
self.assertRaises(TypeError, lambda: psser.cat.reorder_categories(1))
self.assertRaises(TypeError, lambda: psdf.b.cat.reorder_categories("abcd"))
def test_as_ordered_unordered(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
# as_ordered
self.assert_eq(pser.cat.as_ordered(), psser.cat.as_ordered())
pser.cat.as_ordered(inplace=True)
psser.cat.as_ordered(inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[1, 2, 3], ordered=True))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
# as_unordered
self.assert_eq(pser.cat.as_unordered(), psser.cat.as_unordered())
pser.cat.as_unordered(inplace=True)
psser.cat.as_unordered(inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[1, 2, 3], ordered=False))
pdf.a = pser
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
def test_astype(self):
pser = pd.Series(["a", "b", "c"])
psser = ps.from_pandas(pser)
self.assert_eq(psser.astype("category"), pser.astype("category"))
self.assert_eq(
psser.astype(CategoricalDtype(["c", "a", "b"])),
pser.astype(CategoricalDtype(["c", "a", "b"])),
)
pcser = pser.astype(CategoricalDtype(["c", "a", "b"]))
pscser = psser.astype(CategoricalDtype(["c", "a", "b"]))
self.assert_eq(pscser.astype("category"), pcser.astype("category"))
# CategoricalDtype is not updated if the dtype is same from pandas 1.3.
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
self.assert_eq(
pscser.astype(CategoricalDtype(["b", "c", "a"])),
pcser.astype(CategoricalDtype(["b", "c", "a"])),
)
else:
self.assert_eq(
pscser.astype(CategoricalDtype(["b", "c", "a"])),
pcser,
)
self.assert_eq(pscser.astype(str), pcser.astype(str))
def test_factorize(self):
pser = pd.Series(["a", "b", "c", None], dtype=CategoricalDtype(["c", "a", "d", "b"]))
psser = ps.from_pandas(pser)
pcodes, puniques = pser.factorize()
kcodes, kuniques = psser.factorize()
self.assert_eq(kcodes.tolist(), pcodes.tolist())
self.assert_eq(kuniques, puniques)
pcodes, puniques = pser.factorize(na_sentinel=-2)
kcodes, kuniques = psser.factorize(na_sentinel=-2)
self.assert_eq(kcodes.tolist(), pcodes.tolist())
self.assert_eq(kuniques, puniques)
def test_frame_apply(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.apply(lambda x: x).sort_index(), pdf.apply(lambda x: x).sort_index())
self.assert_eq(
psdf.apply(lambda x: x, axis=1).sort_index(),
pdf.apply(lambda x: x, axis=1).sort_index(),
)
def test_frame_apply_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_frame_apply()
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c"])
def categorize(ser) -> ps.Series[dtype]:
return ser.astype(dtype)
self.assert_eq(
psdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
pdf.apply(categorize).sort_values(["a", "b"]).reset_index(drop=True),
)
def test_frame_transform(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.transform(lambda x: x), pdf.transform(lambda x: x))
self.assert_eq(psdf.transform(lambda x: x.cat.codes), pdf.transform(lambda x: x.cat.codes))
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
self.assert_eq(
psdf.transform(lambda x: x.astype(dtype)).sort_index(),
pdf.transform(lambda x: x.astype(dtype)).sort_index(),
)
def test_frame_transform_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_frame_transform()
pdf, psdf = self.df_pair
def codes(pser) -> ps.Series[np.int8]:
return pser.cat.codes
self.assert_eq(psdf.transform(codes), pdf.transform(codes))
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
def to_category(pser) -> ps.Series[dtype]:
return pser.astype(dtype)
self.assert_eq(
psdf.transform(to_category).sort_index(), pdf.transform(to_category).sort_index()
)
def test_series_apply(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.a.apply(lambda x: x).sort_index(), pdf.a.apply(lambda x: x).sort_index()
)
def test_series_apply_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_series_apply()
pdf, psdf = self.df_pair
ret = psdf.a.dtype
def identity(pser) -> ret:
return pser
self.assert_eq(psdf.a.apply(identity).sort_index(), pdf.a.apply(identity).sort_index())
# TODO: The return type is still category.
# def to_str(x) -> str:
# return str(x)
#
# self.assert_eq(
# psdf.a.apply(to_str).sort_index(), pdf.a.apply(to_str).sort_index()
# )
def test_groupby_apply(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.groupby("a").apply(lambda df: df).sort_index(),
pdf.groupby("a").apply(lambda df: df).sort_index(),
)
self.assert_eq(
psdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
pdf.groupby("b").apply(lambda df: df[["a"]]).sort_index(),
)
self.assert_eq(
psdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
pdf.groupby(["a", "b"]).apply(lambda df: df).sort_index(),
)
self.assert_eq(
psdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
pdf.groupby("a").apply(lambda df: df.b.cat.codes).sort_index(),
)
self.assert_eq(
psdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
pdf.groupby("a")["b"].apply(lambda b: b.cat.codes).sort_index(),
)
# TODO: grouping by a categorical type sometimes preserves unused categories.
# self.assert_eq(
# psdf.groupby("a").apply(len).sort_index(), pdf.groupby("a").apply(len).sort_index(),
# )
def test_groupby_apply_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_groupby_apply()
pdf, psdf = self.df_pair
def identity(df) -> ps.DataFrame[zip(psdf.columns, psdf.dtypes)]:
return df
self.assert_eq(
psdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
pdf.groupby("a").apply(identity).sort_values(["a", "b"]).reset_index(drop=True),
)
def test_groupby_transform(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.groupby("a").transform(lambda x: x).sort_index(),
pdf.groupby("a").transform(lambda x: x).sort_index(),
)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
self.assert_eq(
psdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
pdf.groupby("a").transform(lambda x: x.astype(dtype)).sort_index(),
)
def test_groupby_transform_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_groupby_transform()
pdf, psdf = self.df_pair
def identity(x) -> ps.Series[psdf.b.dtype]: # type: ignore
return x
self.assert_eq(
psdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
pdf.groupby("a").transform(identity).sort_values("b").reset_index(drop=True),
)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
# The behavior for CategoricalDtype is changed from pandas 1.3
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
ret_dtype = pdf.b.dtype
else:
ret_dtype = dtype
def astype(x) -> ps.Series[ret_dtype]:
return x.astype(dtype)
if LooseVersion(pd.__version__) >= LooseVersion("1.2"):
self.assert_eq(
psdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
pdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
)
else:
expected = pdf.groupby("a").transform(astype)
expected["b"] = dtype.categories.take(expected["b"].cat.codes).astype(dtype)
self.assert_eq(
psdf.groupby("a").transform(astype).sort_values("b").reset_index(drop=True),
expected.sort_values("b").reset_index(drop=True),
)
def test_frame_apply_batch(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda pdf: pdf.astype(str)).sort_index(),
pdf.astype(str).sort_index(),
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
pdf.astype(dtype).sort_index(),
)
def test_frame_apply_batch_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_frame_apply_batch()
pdf, psdf = self.df_pair
def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
return pdf.astype(str)
self.assert_eq(
psdf.pandas_on_spark.apply_batch(to_str).sort_values(["a", "b"]).reset_index(drop=True),
to_str(pdf).sort_values(["a", "b"]).reset_index(drop=True),
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
ret = ps.DataFrame["a":dtype, "b":dtype]
def to_category(pdf) -> ret:
return pdf.astype(dtype)
self.assert_eq(
psdf.pandas_on_spark.apply_batch(to_category)
.sort_values(["a", "b"])
.reset_index(drop=True),
to_category(pdf).sort_values(["a", "b"]).reset_index(drop=True),
)
def test_frame_transform_batch(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.astype(str)).sort_index(),
pdf.astype(str).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.b.cat.codes).sort_index(),
pdf.b.cat.codes.sort_index(),
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.astype(dtype)).sort_index(),
pdf.astype(dtype).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.b.astype(dtype)).sort_index(),
pdf.b.astype(dtype).sort_index(),
)
def test_frame_transform_batch_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_frame_transform_batch()
pdf, psdf = self.df_pair
def to_str(pdf) -> 'ps.DataFrame["a":str, "b":str]': # noqa: F405
return pdf.astype(str)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(to_str).sort_index(),
to_str(pdf).sort_index(),
)
def to_codes(pdf) -> ps.Series[np.int8]:
return pdf.b.cat.codes
self.assert_eq(
psdf.pandas_on_spark.transform_batch(to_codes).sort_index(),
to_codes(pdf).sort_index(),
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
ret = ps.DataFrame["a":dtype, "b":dtype]
def to_category(pdf) -> ret:
return pdf.astype(dtype)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(to_category).sort_index(),
to_category(pdf).sort_index(),
)
def to_category(pdf) -> ps.Series[dtype]:
return pdf.b.astype(dtype)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(to_category).sort_index(),
to_category(pdf).rename().sort_index(),
)
def test_series_transform_batch(self):
pdf, psdf = self.df_pair
self.assert_eq(
psdf.a.pandas_on_spark.transform_batch(lambda pser: pser.astype(str)).sort_index(),
pdf.a.astype(str).sort_index(),
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
self.assert_eq(
psdf.a.pandas_on_spark.transform_batch(lambda pser: pser.astype(dtype)).sort_index(),
pdf.a.astype(dtype).sort_index(),
)
def test_series_transform_batch_without_shortcut(self):
with ps.option_context("compute.shortcut_limit", 0):
self.test_series_transform_batch()
pdf, psdf = self.df_pair
def to_str(pser) -> ps.Series[str]:
return pser.astype(str)
self.assert_eq(
psdf.a.pandas_on_spark.transform_batch(to_str).sort_index(), to_str(pdf.a).sort_index()
)
pdf = pd.DataFrame(
{"a": ["a", "b", "c", "a", "b", "c"], "b": ["b", "a", "c", "c", "b", "a"]}
)
psdf = ps.from_pandas(pdf)
dtype = CategoricalDtype(categories=["a", "b", "c", "d"])
def to_category(pser) -> ps.Series[dtype]:
return pser.astype(dtype)
self.assert_eq(
psdf.a.pandas_on_spark.transform_batch(to_category).sort_index(),
to_category(pdf.a).sort_index(),
)
def test_unstack(self):
pdf = self.pdf
index = pd.MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("x", "c"), ("y", "a"), ("y", "b"), ("y", "d")]
)
pdf.index = index
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.a.unstack().sort_index(), pdf.a.unstack().sort_index())
self.assert_eq(psdf.b.unstack().sort_index(), pdf.b.unstack().sort_index())
def test_rename_categories(self):
pdf, psdf = self.df_pair
pser = pdf.b
psser = psdf.b
self.assert_eq(
pser.cat.rename_categories([0, 1, 3, 2]), psser.cat.rename_categories([0, 1, 3, 2])
)
self.assert_eq(
pser.cat.rename_categories({"a": "A", "c": "C"}),
psser.cat.rename_categories({"a": "A", "c": "C"}),
)
self.assert_eq(
pser.cat.rename_categories(lambda x: x.upper()),
psser.cat.rename_categories(lambda x: x.upper()),
)
pser.cat.rename_categories({"a": "A", "c": "C"}, inplace=True)
psser.cat.rename_categories({"a": "A", "c": "C"}, inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=["C", "b", "d", "A"]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
pser.cat.rename_categories(lambda x: x.upper(), inplace=True)
psser.cat.rename_categories(lambda x: x.upper(), inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=["C", "B", "D", "A"]))
pdf.b = pser
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
pser.cat.rename_categories([0, 1, 3, 2], inplace=True)
psser.cat.rename_categories([0, 1, 3, 2], inplace=True)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[0, 1, 3, 2]))
pdf.b = pser
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
self.assertRaisesRegex(
ValueError,
"new categories need to have the same number of items as the old categories",
lambda: psser.cat.rename_categories([0, 1, 2]),
)
self.assertRaises(
TypeError,
lambda: psser.cat.rename_categories(None),
)
self.assertRaises(
TypeError,
lambda: psser.cat.rename_categories(1),
)
self.assertRaises(
TypeError,
lambda: psser.cat.rename_categories("x"),
)
def test_set_categories(self):
pdf, psdf = self.df_pair
pser = pdf.b
psser = psdf.b
self.assert_eq(
pser.cat.set_categories(["a", "c", "b", "o"]),
psser.cat.set_categories(["a", "c", "b", "o"]),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b"]),
psser.cat.set_categories(["a", "c", "b"]),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b", "d", "e"]),
psser.cat.set_categories(["a", "c", "b", "d", "e"]),
)
self.assert_eq(
pser.cat.set_categories([0, 1, 3, 2], rename=True),
psser.cat.set_categories([0, 1, 3, 2], rename=True),
)
self.assert_eq(
pser.cat.set_categories([0, 1, 3], rename=True),
psser.cat.set_categories([0, 1, 3], rename=True),
)
self.assert_eq(
pser.cat.set_categories([0, 1, 3, 2, 4], rename=True),
psser.cat.set_categories([0, 1, 3, 2, 4], rename=True),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b", "o"], ordered=True),
psser.cat.set_categories(["a", "c", "b", "o"], ordered=True),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b"], ordered=True),
psser.cat.set_categories(["a", "c", "b"], ordered=True),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b", "d", "e"], ordered=True),
psser.cat.set_categories(["a", "c", "b", "d", "e"], ordered=True),
)
self.assert_eq(
pser.cat.set_categories(["a", "c", "b", "o"], inplace=True, rename=True),
psser.cat.set_categories(["a", "c", "b", "o"], inplace=True, rename=True),
)
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=["a", "c", "b", "o"]))
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
pser.cat.set_categories([2, 3, 1, 0], inplace=True, rename=False),
psser.cat.set_categories([2, 3, 1, 0], inplace=True, rename=False),
if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
# Bug in pandas 1.3. dtype is not updated properly with `inplace` argument.
pser = pser.astype(CategoricalDtype(categories=[2, 3, 1, 0]))
pdf.b = pser
self.assert_eq(pser, psser)
self.assert_eq(pdf, psdf)
self.assertRaisesRegex(
TypeError,
"Parameter 'new_categories' must be list-like, was",
lambda: psser.cat.set_categories(None),
)
if __name__ == "__main__":
import unittest
from pyspark.pandas.tests.test_categorical 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)