# # 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 inspect from distutils.version import LooseVersion from itertools import product import numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.pandas.config import option_context from pyspark.pandas.exceptions import PandasNotImplementedError, DataError from pyspark.pandas.missing.groupby import ( MissingPandasLikeDataFrameGroupBy, MissingPandasLikeSeriesGroupBy, ) from pyspark.pandas.groupby import is_multi_agg_with_relabel from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils class GroupByTest(PandasOnSparkTestCase, TestUtils): def test_groupby_simple(self): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2], "c": [4, 2, 7, 3, None, 1, 1, 1, 2], "d": list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) psdf = ps.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("a").reset_index(drop=True) self.assert_eq( sort(psdf.groupby("a", as_index=as_index).sum()), sort(pdf.groupby("a", as_index=as_index).sum()), ) self.assert_eq( sort(psdf.groupby("a", as_index=as_index).b.sum()), sort(pdf.groupby("a", as_index=as_index).b.sum()), ) self.assert_eq( sort(psdf.groupby("a", as_index=as_index)["b"].sum()), sort(pdf.groupby("a", as_index=as_index)["b"].sum()), ) self.assert_eq( sort(psdf.groupby("a", as_index=as_index)[["b", "c"]].sum()), sort(pdf.groupby("a", as_index=as_index)[["b", "c"]].sum()), ) self.assert_eq( sort(psdf.groupby("a", as_index=as_index)[[]].sum()), sort(pdf.groupby("a", as_index=as_index)[[]].sum()), ) self.assert_eq( sort(psdf.groupby("a", as_index=as_index)["c"].sum()), sort(pdf.groupby("a", as_index=as_index)["c"].sum()), ) self.assert_eq( psdf.groupby("a").a.sum().sort_index(), pdf.groupby("a").a.sum().sort_index() ) self.assert_eq( psdf.groupby("a")["a"].sum().sort_index(), pdf.groupby("a")["a"].sum().sort_index() ) self.assert_eq( psdf.groupby("a")[["a"]].sum().sort_index(), pdf.groupby("a")[["a"]].sum().sort_index() ) self.assert_eq( psdf.groupby("a")[["a", "c"]].sum().sort_index(), pdf.groupby("a")[["a", "c"]].sum().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b).sum().sort_index(), pdf.a.groupby(pdf.b).sum().sort_index() ) for axis in [0, "index"]: self.assert_eq( psdf.groupby("a", axis=axis).a.sum().sort_index(), pdf.groupby("a", axis=axis).a.sum().sort_index(), ) self.assert_eq( psdf.groupby("a", axis=axis)["a"].sum().sort_index(), pdf.groupby("a", axis=axis)["a"].sum().sort_index(), ) self.assert_eq( psdf.groupby("a", axis=axis)[["a"]].sum().sort_index(), pdf.groupby("a", axis=axis)[["a"]].sum().sort_index(), ) self.assert_eq( psdf.groupby("a", axis=axis)[["a", "c"]].sum().sort_index(), pdf.groupby("a", axis=axis)[["a", "c"]].sum().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b, axis=axis).sum().sort_index(), pdf.a.groupby(pdf.b, axis=axis).sum().sort_index(), ) self.assertRaises(ValueError, lambda: psdf.groupby("a", as_index=False).a) self.assertRaises(ValueError, lambda: psdf.groupby("a", as_index=False)["a"]) self.assertRaises(ValueError, lambda: psdf.groupby("a", as_index=False)[["a"]]) self.assertRaises(ValueError, lambda: psdf.groupby("a", as_index=False)[["a", "c"]]) self.assertRaises(KeyError, lambda: psdf.groupby("z", as_index=False)[["a", "c"]]) self.assertRaises(KeyError, lambda: psdf.groupby(["z"], as_index=False)[["a", "c"]]) self.assertRaises(TypeError, lambda: psdf.a.groupby(psdf.b, as_index=False)) self.assertRaises(NotImplementedError, lambda: psdf.groupby("a", axis=1)) self.assertRaises(NotImplementedError, lambda: psdf.groupby("a", axis="columns")) self.assertRaises(ValueError, lambda: psdf.groupby("a", "b")) self.assertRaises(TypeError, lambda: psdf.a.groupby(psdf.a, psdf.b)) # we can't use column name/names as a parameter `by` for `SeriesGroupBy`. self.assertRaises(KeyError, lambda: psdf.a.groupby(by="a")) self.assertRaises(KeyError, lambda: psdf.a.groupby(by=["a", "b"])) self.assertRaises(KeyError, lambda: psdf.a.groupby(by=("a", "b"))) # we can't use DataFrame as a parameter `by` for `DataFrameGroupBy`/`SeriesGroupBy`. self.assertRaises(ValueError, lambda: psdf.groupby(psdf)) self.assertRaises(ValueError, lambda: psdf.a.groupby(psdf)) self.assertRaises(ValueError, lambda: psdf.a.groupby((psdf,))) # non-string names pdf = pd.DataFrame( { 10: [1, 2, 6, 4, 4, 6, 4, 3, 7], 20: [4, 2, 7, 3, 3, 1, 1, 1, 2], 30: [4, 2, 7, 3, None, 1, 1, 1, 2], 40: list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) psdf = ps.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(10).reset_index(drop=True) self.assert_eq( sort(psdf.groupby(10, as_index=as_index).sum()), sort(pdf.groupby(10, as_index=as_index).sum()), ) self.assert_eq( sort(psdf.groupby(10, as_index=as_index)[20].sum()), sort(pdf.groupby(10, as_index=as_index)[20].sum()), ) self.assert_eq( sort(psdf.groupby(10, as_index=as_index)[[20, 30]].sum()), sort(pdf.groupby(10, as_index=as_index)[[20, 30]].sum()), ) def test_groupby_multiindex_columns(self): pdf = pd.DataFrame( { (10, "a"): [1, 2, 6, 4, 4, 6, 4, 3, 7], (10, "b"): [4, 2, 7, 3, 3, 1, 1, 1, 2], (20, "c"): [4, 2, 7, 3, None, 1, 1, 1, 2], (30, "d"): list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby((10, "a")).sum().sort_index(), pdf.groupby((10, "a")).sum().sort_index() ) self.assert_eq( psdf.groupby((10, "a"), as_index=False) .sum() .sort_values((10, "a")) .reset_index(drop=True), pdf.groupby((10, "a"), as_index=False) .sum() .sort_values((10, "a")) .reset_index(drop=True), ) self.assert_eq( psdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), pdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), ) # TODO: a pandas bug? # expected = pdf.groupby((10, "a"))[(20, "c")].sum().sort_index() expected = pd.Series( [4.0, 2.0, 1.0, 4.0, 8.0, 2.0], name=(20, "c"), index=pd.Index([1, 2, 3, 4, 6, 7], name=(10, "a")), ) self.assert_eq(psdf.groupby((10, "a"))[(20, "c")].sum().sort_index(), expected) if LooseVersion(pd.__version__) < LooseVersion("1.1.3"): self.assert_eq( psdf[(20, "c")].groupby(psdf[(10, "a")]).sum().sort_index(), pdf[(20, "c")].groupby(pdf[(10, "a")]).sum().sort_index(), ) else: # seems like a pandas bug introduced in pandas 1.1.3. self.assert_eq(psdf[(20, "c")].groupby(psdf[(10, "a")]).sum().sort_index(), expected) def test_split_apply_combine_on_series(self): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2], "c": [4, 2, 7, 3, None, 1, 1, 1, 2], "d": list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) psdf = ps.from_pandas(pdf) funcs = [ ((True, False), ["sum", "min", "max", "count", "first", "last"]), ((True, True), ["mean"]), ((False, False), ["var", "std"]), ] funcs = [(check_exact, almost, f) for (check_exact, almost), fs in funcs for f in fs] for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(list(df.columns)).reset_index(drop=True) for check_exact, almost, func in funcs: for kkey, pkey in [("b", "b"), (psdf.b, pdf.b)]: with self.subTest(as_index=as_index, func=func, key=pkey): if as_index is True or func != "std": self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index), func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) else: # seems like a pandas' bug for as_index=False and func == "std"? self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index).a, func)()), sort(pdf.groupby(pkey, as_index=True).a.std().reset_index()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index), func)()), sort(pdf.groupby(pkey, as_index=True).std().reset_index()), check_exact=check_exact, almost=almost, ) for kkey, pkey in [(psdf.b + 1, pdf.b + 1), (psdf.copy().b, pdf.copy().b)]: with self.subTest(as_index=as_index, func=func, key=pkey): self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(psdf.groupby(kkey, as_index=as_index), func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) for check_exact, almost, func in funcs: for i in [0, 4, 7]: with self.subTest(as_index=as_index, func=func, i=i): self.assert_eq( sort(getattr(psdf.groupby(psdf.b > i, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pdf.b > i, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(psdf.groupby(psdf.b > i, as_index=as_index), func)()), sort(getattr(pdf.groupby(pdf.b > i, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) for check_exact, almost, func in funcs: for kkey, pkey in [ (psdf.b, pdf.b), (psdf.b + 1, pdf.b + 1), (psdf.copy().b, pdf.copy().b), (psdf.b.rename(), pdf.b.rename()), ]: with self.subTest(func=func, key=pkey): self.assert_eq( getattr(psdf.a.groupby(kkey), func)().sort_index(), getattr(pdf.a.groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr((psdf.a + 1).groupby(kkey), func)().sort_index(), getattr((pdf.a + 1).groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr((psdf.b + 1).groupby(kkey), func)().sort_index(), getattr((pdf.b + 1).groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr(psdf.a.rename().groupby(kkey), func)().sort_index(), getattr(pdf.a.rename().groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) def test_aggregate(self): pdf = pd.DataFrame( {"A": [1, 1, 2, 2], "B": [1, 2, 3, 4], "C": [0.362, 0.227, 1.267, -0.562]} ) psdf = ps.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(list(df.columns)).reset_index(drop=True) for kkey, pkey in [("A", "A"), (psdf.A, pdf.A)]: with self.subTest(as_index=as_index, key=pkey): self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg("sum")), sort(pdf.groupby(pkey, as_index=as_index).agg("sum")), ) self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg({"B": "min", "C": "sum"})), sort(pdf.groupby(pkey, as_index=as_index).agg({"B": "min", "C": "sum"})), ) self.assert_eq( sort( psdf.groupby(kkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), sort( pdf.groupby(pkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), ) if as_index: self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=as_index).agg(["sum"])), ) else: # seems like a pandas' bug for as_index=False and func_or_funcs is list? self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=True).agg(["sum"]).reset_index()), ) for kkey, pkey in [(psdf.A + 1, pdf.A + 1), (psdf.copy().A, pdf.copy().A)]: with self.subTest(as_index=as_index, key=pkey): self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg("sum")), sort(pdf.groupby(pkey, as_index=as_index).agg("sum")), ) self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg({"B": "min", "C": "sum"})), sort(pdf.groupby(pkey, as_index=as_index).agg({"B": "min", "C": "sum"})), ) self.assert_eq( sort( psdf.groupby(kkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), sort( pdf.groupby(pkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), ) self.assert_eq( sort(psdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=as_index).agg(["sum"])), ) expected_error_message = ( r"aggs must be a dict mapping from column name to aggregate functions " r"\(string or list of strings\)." ) with self.assertRaisesRegex(ValueError, expected_error_message): psdf.groupby("A", as_index=as_index).agg(0) # multi-index columns columns = pd.MultiIndex.from_tuples([(10, "A"), (10, "B"), (20, "C")]) pdf.columns = columns psdf.columns = columns for as_index in [True, False]: stats_psdf = psdf.groupby((10, "A"), as_index=as_index).agg( {(10, "B"): "min", (20, "C"): "sum"} ) stats_pdf = pdf.groupby((10, "A"), as_index=as_index).agg( {(10, "B"): "min", (20, "C"): "sum"} ) self.assert_eq( stats_psdf.sort_values(by=[(10, "B"), (20, "C")]).reset_index(drop=True), stats_pdf.sort_values(by=[(10, "B"), (20, "C")]).reset_index(drop=True), ) stats_psdf = psdf.groupby((10, "A")).agg({(10, "B"): ["min", "max"], (20, "C"): "sum"}) stats_pdf = pdf.groupby((10, "A")).agg({(10, "B"): ["min", "max"], (20, "C"): "sum"}) self.assert_eq( stats_psdf.sort_values( by=[(10, "B", "min"), (10, "B", "max"), (20, "C", "sum")] ).reset_index(drop=True), stats_pdf.sort_values( by=[(10, "B", "min"), (10, "B", "max"), (20, "C", "sum")] ).reset_index(drop=True), ) # non-string names pdf.columns = [10, 20, 30] psdf.columns = [10, 20, 30] for as_index in [True, False]: stats_psdf = psdf.groupby(10, as_index=as_index).agg({20: "min", 30: "sum"}) stats_pdf = pdf.groupby(10, as_index=as_index).agg({20: "min", 30: "sum"}) self.assert_eq( stats_psdf.sort_values(by=[20, 30]).reset_index(drop=True), stats_pdf.sort_values(by=[20, 30]).reset_index(drop=True), ) stats_psdf = psdf.groupby(10).agg({20: ["min", "max"], 30: "sum"}) stats_pdf = pdf.groupby(10).agg({20: ["min", "max"], 30: "sum"}) self.assert_eq( stats_psdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index( drop=True ), stats_pdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index( drop=True ), ) def test_aggregate_func_str_list(self): # this is test for cases where only string or list is assigned pdf = pd.DataFrame( { "kind": ["cat", "dog", "cat", "dog"], "height": [9.1, 6.0, 9.5, 34.0], "weight": [7.9, 7.5, 9.9, 198.0], } ) psdf = ps.from_pandas(pdf) agg_funcs = ["max", "min", ["min", "max"]] for aggfunc in agg_funcs: # Since in Koalas groupby, the order of rows might be different # so sort on index to ensure they have same output sorted_agg_psdf = psdf.groupby("kind").agg(aggfunc).sort_index() sorted_agg_pdf = pdf.groupby("kind").agg(aggfunc).sort_index() self.assert_eq(sorted_agg_psdf, sorted_agg_pdf) # test on multi index column case pdf = pd.DataFrame( {"A": [1, 1, 2, 2], "B": [1, 2, 3, 4], "C": [0.362, 0.227, 1.267, -0.562]} ) psdf = ps.from_pandas(pdf) columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")]) pdf.columns = columns psdf.columns = columns for aggfunc in agg_funcs: sorted_agg_psdf = psdf.groupby(("X", "A")).agg(aggfunc).sort_index() sorted_agg_pdf = pdf.groupby(("X", "A")).agg(aggfunc).sort_index() self.assert_eq(sorted_agg_psdf, sorted_agg_pdf) @unittest.skipIf(pd.__version__ < "0.25.0", "not supported before pandas 0.25.0") def test_aggregate_relabel(self): # this is to test named aggregation in groupby pdf = pd.DataFrame({"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}) psdf = ps.from_pandas(pdf) # different agg column, same function agg_pdf = pdf.groupby("group").agg(a_max=("A", "max"), b_max=("B", "max")).sort_index() agg_psdf = psdf.groupby("group").agg(a_max=("A", "max"), b_max=("B", "max")).sort_index() self.assert_eq(agg_pdf, agg_psdf) # same agg column, different functions agg_pdf = pdf.groupby("group").agg(b_max=("B", "max"), b_min=("B", "min")).sort_index() agg_psdf = psdf.groupby("group").agg(b_max=("B", "max"), b_min=("B", "min")).sort_index() self.assert_eq(agg_pdf, agg_psdf) # test on NamedAgg agg_pdf = ( pdf.groupby("group").agg(b_max=pd.NamedAgg(column="B", aggfunc="max")).sort_index() ) agg_psdf = ( psdf.groupby("group").agg(b_max=ps.NamedAgg(column="B", aggfunc="max")).sort_index() ) self.assert_eq(agg_psdf, agg_pdf) # test on NamedAgg multi columns aggregation agg_pdf = ( pdf.groupby("group") .agg( b_max=pd.NamedAgg(column="B", aggfunc="max"), b_min=pd.NamedAgg(column="B", aggfunc="min"), ) .sort_index() ) agg_psdf = ( psdf.groupby("group") .agg( b_max=ps.NamedAgg(column="B", aggfunc="max"), b_min=ps.NamedAgg(column="B", aggfunc="min"), ) .sort_index() ) self.assert_eq(agg_psdf, agg_pdf) def test_dropna(self): pdf = pd.DataFrame( {"A": [None, 1, None, 1, 2], "B": [1, 2, 3, None, None], "C": [4, 5, 6, 7, None]} ) psdf = ps.from_pandas(pdf) # pd.DataFrame.groupby with dropna parameter is implemented since pandas 1.1.0 if LooseVersion(pd.__version__) >= LooseVersion("1.1.0"): for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(psdf.groupby("A", as_index=as_index, dropna=dropna).std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna).std()), ) self.assert_eq( sort(psdf.groupby("A", as_index=as_index, dropna=dropna).B.std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna).B.std()), ) self.assert_eq( sort(psdf.groupby("A", as_index=as_index, dropna=dropna)["B"].std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna)["B"].std()), ) self.assert_eq( sort( psdf.groupby("A", as_index=as_index, dropna=dropna).agg( {"B": "min", "C": "std"} ) ), sort( pdf.groupby("A", as_index=as_index, dropna=dropna).agg( {"B": "min", "C": "std"} ) ), ) for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(["A", "B"]).reset_index(drop=True) self.assert_eq( sort( psdf.groupby(["A", "B"], as_index=as_index, dropna=dropna).agg( {"C": ["min", "std"]} ) ), sort( pdf.groupby(["A", "B"], as_index=as_index, dropna=dropna).agg( {"C": ["min", "std"]} ) ), almost=True, ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")]) pdf.columns = columns psdf.columns = columns for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(("X", "A")).reset_index(drop=True) sorted_stats_psdf = sort( psdf.groupby(("X", "A"), as_index=as_index, dropna=dropna).agg( {("X", "B"): "min", ("Y", "C"): "std"} ) ) sorted_stats_pdf = sort( pdf.groupby(("X", "A"), as_index=as_index, dropna=dropna).agg( {("X", "B"): "min", ("Y", "C"): "std"} ) ) self.assert_eq(sorted_stats_psdf, sorted_stats_pdf) else: # Testing dropna=True (pandas default behavior) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(psdf.groupby("A", as_index=as_index, dropna=True)["B"].min()), sort(pdf.groupby("A", as_index=as_index)["B"].min()), ) if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(["A", "B"]).reset_index(drop=True) self.assert_eq( sort( psdf.groupby(["A", "B"], as_index=as_index, dropna=True).agg( {"C": ["min", "std"]} ) ), sort(pdf.groupby(["A", "B"], as_index=as_index).agg({"C": ["min", "std"]})), almost=True, ) # Testing dropna=False index = pd.Index([1.0, 2.0, np.nan], name="A") expected = pd.Series([2.0, np.nan, 1.0], index=index, name="B") result = psdf.groupby("A", as_index=True, dropna=False)["B"].min().sort_index() self.assert_eq(expected, result) expected = pd.DataFrame({"A": [1.0, 2.0, np.nan], "B": [2.0, np.nan, 1.0]}) result = ( psdf.groupby("A", as_index=False, dropna=False)["B"] .min() .sort_values("A") .reset_index(drop=True) ) self.assert_eq(expected, result) index = pd.MultiIndex.from_tuples( [(1.0, 2.0), (1.0, None), (2.0, None), (None, 1.0), (None, 3.0)], names=["A", "B"] ) expected = pd.DataFrame( { ("C", "min"): [5.0, 7.0, np.nan, 4.0, 6.0], ("C", "std"): [np.nan, np.nan, np.nan, np.nan, np.nan], }, index=index, ) result = ( psdf.groupby(["A", "B"], as_index=True, dropna=False) .agg({"C": ["min", "std"]}) .sort_index() ) self.assert_eq(expected, result) expected = pd.DataFrame( { ("A", ""): [1.0, 1.0, 2.0, np.nan, np.nan], ("B", ""): [2.0, np.nan, np.nan, 1.0, 3.0], ("C", "min"): [5.0, 7.0, np.nan, 4.0, 6.0], ("C", "std"): [np.nan, np.nan, np.nan, np.nan, np.nan], } ) result = ( psdf.groupby(["A", "B"], as_index=False, dropna=False) .agg({"C": ["min", "std"]}) .sort_values(["A", "B"]) .reset_index(drop=True) ) self.assert_eq(expected, result) def test_describe(self): # support for numeric type, not support for string type yet datas = [] datas.append({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]}) datas.append({"a": [-1, -1, -3], "b": [-4, -5, -6], "c": [-7, -8, -9]}) datas.append({"a": [0, 0, 0], "b": [0, 0, 0], "c": [0, 8, 0]}) # it is okay if string type column as a group key datas.append({"a": ["a", "a", "c"], "b": [4, 5, 6], "c": [7, 8, 9]}) percentiles = [0.25, 0.5, 0.75] formatted_percentiles = ["25%", "50%", "75%"] non_percentile_stats = ["count", "mean", "std", "min", "max"] for data in datas: pdf = pd.DataFrame(data) psdf = ps.from_pandas(pdf) describe_pdf = pdf.groupby("a").describe().sort_index() describe_psdf = psdf.groupby("a").describe().sort_index() # since the result of percentile columns are slightly difference from pandas, # we should check them separately: non-percentile columns & percentile columns # 1. Check that non-percentile columns are equal. agg_cols = [col.name for col in psdf.groupby("a")._agg_columns] self.assert_eq( describe_psdf.drop(list(product(agg_cols, formatted_percentiles))), describe_pdf.drop(columns=formatted_percentiles, level=1), check_exact=False, ) # 2. Check that percentile columns are equal. # The interpolation argument is yet to be implemented in Koalas. quantile_pdf = pdf.groupby("a").quantile(percentiles, interpolation="nearest") quantile_pdf = quantile_pdf.unstack(level=1).astype(float) self.assert_eq( describe_psdf.drop(list(product(agg_cols, non_percentile_stats))), quantile_pdf.rename(columns="{:.0%}".format, level=1), ) # not support for string type yet datas = [] datas.append({"a": ["a", "a", "c"], "b": ["d", "e", "f"], "c": ["g", "h", "i"]}) datas.append({"a": ["a", "a", "c"], "b": [4, 0, 1], "c": ["g", "h", "i"]}) for data in datas: pdf = pd.DataFrame(data) psdf = ps.from_pandas(pdf) self.assertRaises( NotImplementedError, lambda: psdf.groupby("a").describe().sort_index() ) # multi-index columns pdf = pd.DataFrame({("x", "a"): [1, 1, 3], ("x", "b"): [4, 5, 6], ("y", "c"): [7, 8, 9]}) psdf = ps.from_pandas(pdf) describe_pdf = pdf.groupby(("x", "a")).describe().sort_index() describe_psdf = psdf.groupby(("x", "a")).describe().sort_index() # 1. Check that non-percentile columns are equal. agg_column_labels = [col._column_label for col in psdf.groupby(("x", "a"))._agg_columns] self.assert_eq( describe_psdf.drop( [ tuple(list(label) + [s]) for label, s in product(agg_column_labels, formatted_percentiles) ] ), describe_pdf.drop(columns=formatted_percentiles, level=2), check_exact=False, ) # 2. Check that percentile columns are equal. # The interpolation argument is yet to be implemented in Koalas. quantile_pdf = pdf.groupby(("x", "a")).quantile(percentiles, interpolation="nearest") quantile_pdf = quantile_pdf.unstack(level=1).astype(float) self.assert_eq( describe_psdf.drop( [ tuple(list(label) + [s]) for label, s in product(agg_column_labels, non_percentile_stats) ] ), quantile_pdf.rename(columns="{:.0%}".format, level=2), ) def test_aggregate_relabel_multiindex(self): pdf = pd.DataFrame({"A": [0, 1, 2, 3], "B": [5, 6, 7, 8], "group": ["a", "a", "b", "b"]}) pdf.columns = pd.MultiIndex.from_tuples([("y", "A"), ("y", "B"), ("x", "group")]) psdf = ps.from_pandas(pdf) if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [1, 3]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = pdf.groupby(("x", "group")).agg(a_max=(("y", "A"), "max")).sort_index() agg_psdf = psdf.groupby(("x", "group")).agg(a_max=(("y", "A"), "max")).sort_index() self.assert_eq(agg_pdf, agg_psdf) # same column, different methods if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [1, 3], "a_min": [0, 2]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = ( pdf.groupby(("x", "group")) .agg(a_max=(("y", "A"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) agg_psdf = ( psdf.groupby(("x", "group")) .agg(a_max=(("y", "A"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) self.assert_eq(agg_pdf, agg_psdf) # different column, different methods if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [6, 8], "a_min": [0, 2]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = ( pdf.groupby(("x", "group")) .agg(a_max=(("y", "B"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) agg_psdf = ( psdf.groupby(("x", "group")) .agg(a_max=(("y", "B"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) self.assert_eq(agg_pdf, agg_psdf) def test_all_any(self): pdf = pd.DataFrame( { "A": [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], "B": [True, True, True, False, False, False, None, True, None, False], } ) psdf = ps.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(psdf.groupby("A", as_index=as_index).all()), sort(pdf.groupby("A", as_index=as_index).all()), ) self.assert_eq( sort(psdf.groupby("A", as_index=as_index).any()), sort(pdf.groupby("A", as_index=as_index).any()), ) self.assert_eq( sort(psdf.groupby("A", as_index=as_index).all()).B, sort(pdf.groupby("A", as_index=as_index).all()).B, ) self.assert_eq( sort(psdf.groupby("A", as_index=as_index).any()).B, sort(pdf.groupby("A", as_index=as_index).any()).B, ) self.assert_eq( psdf.B.groupby(psdf.A).all().sort_index(), pdf.B.groupby(pdf.A).all().sort_index() ) self.assert_eq( psdf.B.groupby(psdf.A).any().sort_index(), pdf.B.groupby(pdf.A).any().sort_index() ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("Y", "B")]) pdf.columns = columns psdf.columns = columns for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(("X", "A")).reset_index(drop=True) self.assert_eq( sort(psdf.groupby(("X", "A"), as_index=as_index).all()), sort(pdf.groupby(("X", "A"), as_index=as_index).all()), ) self.assert_eq( sort(psdf.groupby(("X", "A"), as_index=as_index).any()), sort(pdf.groupby(("X", "A"), as_index=as_index).any()), ) def test_raises(self): psdf = ps.DataFrame( {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) # test raises with incorrect key self.assertRaises(ValueError, lambda: psdf.groupby([])) self.assertRaises(KeyError, lambda: psdf.groupby("x")) self.assertRaises(KeyError, lambda: psdf.groupby(["a", "x"])) self.assertRaises(KeyError, lambda: psdf.groupby("a")["x"]) self.assertRaises(KeyError, lambda: psdf.groupby("a")["b", "x"]) self.assertRaises(KeyError, lambda: psdf.groupby("a")[["b", "x"]]) def test_nunique(self): pdf = pd.DataFrame( {"a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": [2, 2, 2, 3, 3, 4, 4, 5, 5, 5]} ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("a").agg({"b": "nunique"}).sort_index(), pdf.groupby("a").agg({"b": "nunique"}).sort_index(), ) if LooseVersion(pd.__version__) < LooseVersion("1.1.0"): expected = ps.DataFrame({"b": [2, 2]}, index=pd.Index([0, 1], name="a")) self.assert_eq(psdf.groupby("a").nunique().sort_index(), expected) self.assert_eq( psdf.groupby("a").nunique(dropna=False).sort_index(), expected, ) else: self.assert_eq( psdf.groupby("a").nunique().sort_index(), pdf.groupby("a").nunique().sort_index() ) self.assert_eq( psdf.groupby("a").nunique(dropna=False).sort_index(), pdf.groupby("a").nunique(dropna=False).sort_index(), ) self.assert_eq( psdf.groupby("a")["b"].nunique().sort_index(), pdf.groupby("a")["b"].nunique().sort_index(), ) self.assert_eq( psdf.groupby("a")["b"].nunique(dropna=False).sort_index(), pdf.groupby("a")["b"].nunique(dropna=False).sort_index(), ) nunique_psdf = psdf.groupby("a", as_index=False).agg({"b": "nunique"}) nunique_pdf = pdf.groupby("a", as_index=False).agg({"b": "nunique"}) self.assert_eq( nunique_psdf.sort_values(["a", "b"]).reset_index(drop=True), nunique_pdf.sort_values(["a", "b"]).reset_index(drop=True), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")]) pdf.columns = columns psdf.columns = columns if LooseVersion(pd.__version__) < LooseVersion("1.1.0"): expected = ps.DataFrame({("y", "b"): [2, 2]}, index=pd.Index([0, 1], name=("x", "a"))) self.assert_eq( psdf.groupby(("x", "a")).nunique().sort_index(), expected, ) self.assert_eq( psdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), expected, ) else: self.assert_eq( psdf.groupby(("x", "a")).nunique().sort_index(), pdf.groupby(("x", "a")).nunique().sort_index(), ) self.assert_eq( psdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), pdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), ) def test_unique(self): for pdf in [ pd.DataFrame( {"a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": [2, 2, 2, 3, 3, 4, 4, 5, 5, 5]} ), pd.DataFrame( { "a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": ["w", "w", "w", "x", "x", "y", "y", "z", "z", "z"], } ), ]: with self.subTest(pdf=pdf): psdf = ps.from_pandas(pdf) actual = psdf.groupby("a")["b"].unique().sort_index().to_pandas() expect = pdf.groupby("a")["b"].unique().sort_index() self.assert_eq(len(actual), len(expect)) for act, exp in zip(actual, expect): self.assertTrue(sorted(act) == sorted(exp)) def test_value_counts(self): pdf = pd.DataFrame({"A": [1, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, 3]}, columns=["A", "B"]) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("A")["B"].value_counts().sort_index(), pdf.groupby("A")["B"].value_counts().sort_index(), ) self.assert_eq( psdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), pdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), ) self.assert_eq( psdf.groupby("A")["B"].value_counts(sort=True, ascending=True).sort_index(), pdf.groupby("A")["B"].value_counts(sort=True, ascending=True).sort_index(), ) self.assert_eq( psdf.B.rename().groupby(psdf.A).value_counts().sort_index(), pdf.B.rename().groupby(pdf.A).value_counts().sort_index(), ) self.assert_eq( psdf.B.groupby(psdf.A.rename()).value_counts().sort_index(), pdf.B.groupby(pdf.A.rename()).value_counts().sort_index(), ) self.assert_eq( psdf.B.rename().groupby(psdf.A.rename()).value_counts().sort_index(), pdf.B.rename().groupby(pdf.A.rename()).value_counts().sort_index(), ) def test_size(self): pdf = pd.DataFrame({"A": [1, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, 3]}) psdf = ps.from_pandas(pdf) self.assert_eq(psdf.groupby("A").size().sort_index(), pdf.groupby("A").size().sort_index()) self.assert_eq( psdf.groupby("A")["B"].size().sort_index(), pdf.groupby("A")["B"].size().sort_index() ) self.assert_eq( psdf.groupby("A")[["B"]].size().sort_index(), pdf.groupby("A")[["B"]].size().sort_index(), ) self.assert_eq( psdf.groupby(["A", "B"]).size().sort_index(), pdf.groupby(["A", "B"]).size().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("Y", "B")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("X", "A")).size().sort_index(), pdf.groupby(("X", "A")).size().sort_index(), ) self.assert_eq( psdf.groupby([("X", "A"), ("Y", "B")]).size().sort_index(), pdf.groupby([("X", "A"), ("Y", "B")]).size().sort_index(), ) def test_diff(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, } ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf.groupby("b").diff().sort_index(), pdf.groupby("b").diff().sort_index()) self.assert_eq( psdf.groupby(["a", "b"]).diff().sort_index(), pdf.groupby(["a", "b"]).diff().sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].diff().sort_index(), pdf.groupby(["b"])["a"].diff().sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "b"]].diff().sort_index(), pdf.groupby(["b"])[["a", "b"]].diff().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).diff().sort_index(), pdf.groupby(pdf.b // 5).diff().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].diff().sort_index(), pdf.groupby(pdf.b // 5)["a"].diff().sort_index(), ) self.assert_eq(psdf.groupby("b").diff().sum(), pdf.groupby("b").diff().sum().astype(int)) self.assert_eq(psdf.groupby(["b"])["a"].diff().sum(), pdf.groupby(["b"])["a"].diff().sum()) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).diff().sort_index(), pdf.groupby(("x", "b")).diff().sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).diff().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).diff().sort_index(), ) def test_rank(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq(psdf.groupby("b").rank().sort_index(), pdf.groupby("b").rank().sort_index()) self.assert_eq( psdf.groupby(["a", "b"]).rank().sort_index(), pdf.groupby(["a", "b"]).rank().sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].rank().sort_index(), pdf.groupby(["b"])["a"].rank().sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].rank().sort_index(), pdf.groupby(["b"])[["a", "c"]].rank().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).rank().sort_index(), pdf.groupby(pdf.b // 5).rank().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].rank().sort_index(), pdf.groupby(pdf.b // 5)["a"].rank().sort_index(), ) self.assert_eq(psdf.groupby("b").rank().sum(), pdf.groupby("b").rank().sum()) self.assert_eq(psdf.groupby(["b"])["a"].rank().sum(), pdf.groupby(["b"])["a"].rank().sum()) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).rank().sort_index(), pdf.groupby(("x", "b")).rank().sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).rank().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).rank().sort_index(), ) def test_cumcount(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) for ascending in [True, False]: self.assert_eq( psdf.groupby("b").cumcount(ascending=ascending).sort_index(), pdf.groupby("b").cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby(["a", "b"]).cumcount(ascending=ascending).sort_index(), pdf.groupby(["a", "b"]).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].cumcount(ascending=ascending).sort_index(), pdf.groupby(["b"])["a"].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].cumcount(ascending=ascending).sort_index(), pdf.groupby(["b"])[["a", "c"]].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).cumcount(ascending=ascending).sort_index(), pdf.groupby(pdf.b // 5).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].cumcount(ascending=ascending).sort_index(), pdf.groupby(pdf.b // 5)["a"].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby("b").cumcount(ascending=ascending).sum(), pdf.groupby("b").cumcount(ascending=ascending).sum(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).cumcount(ascending=ascending).sort_index(), pdf.a.rename().groupby(pdf.b).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).cumcount(ascending=ascending).sort_index(), pdf.a.groupby(pdf.b.rename()).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).cumcount(ascending=ascending).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumcount(ascending=ascending).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns for ascending in [True, False]: self.assert_eq( psdf.groupby(("x", "b")).cumcount(ascending=ascending).sort_index(), pdf.groupby(("x", "b")).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).cumcount(ascending=ascending).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumcount(ascending=ascending).sort_index(), ) def test_cummin(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").cummin().sort_index(), pdf.groupby("b").cummin().sort_index() ) self.assert_eq( psdf.groupby(["a", "b"]).cummin().sort_index(), pdf.groupby(["a", "b"]).cummin().sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].cummin().sort_index(), pdf.groupby(["b"])["a"].cummin().sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].cummin().sort_index(), pdf.groupby(["b"])[["a", "c"]].cummin().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).cummin().sort_index(), pdf.groupby(pdf.b // 5).cummin().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].cummin().sort_index(), pdf.groupby(pdf.b // 5)["a"].cummin().sort_index(), ) self.assert_eq( psdf.groupby("b").cummin().sum().sort_index(), pdf.groupby("b").cummin().sum().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).cummin().sort_index(), pdf.a.rename().groupby(pdf.b).cummin().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).cummin().sort_index(), pdf.a.groupby(pdf.b.rename()).cummin().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).cummin().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cummin().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).cummin().sort_index(), pdf.groupby(("x", "b")).cummin().sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).cummin().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cummin().sort_index(), ) psdf = ps.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"]).cummin()) psdf = ps.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"])["B"].cummin()) def test_cummax(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").cummax().sort_index(), pdf.groupby("b").cummax().sort_index() ) self.assert_eq( psdf.groupby(["a", "b"]).cummax().sort_index(), pdf.groupby(["a", "b"]).cummax().sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].cummax().sort_index(), pdf.groupby(["b"])["a"].cummax().sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].cummax().sort_index(), pdf.groupby(["b"])[["a", "c"]].cummax().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).cummax().sort_index(), pdf.groupby(pdf.b // 5).cummax().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].cummax().sort_index(), pdf.groupby(pdf.b // 5)["a"].cummax().sort_index(), ) self.assert_eq( psdf.groupby("b").cummax().sum().sort_index(), pdf.groupby("b").cummax().sum().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).cummax().sort_index(), pdf.a.rename().groupby(pdf.b).cummax().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).cummax().sort_index(), pdf.a.groupby(pdf.b.rename()).cummax().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).cummax().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cummax().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).cummax().sort_index(), pdf.groupby(("x", "b")).cummax().sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).cummax().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cummax().sort_index(), ) psdf = ps.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"]).cummax()) psdf = ps.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"])["B"].cummax()) def test_cumsum(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").cumsum().sort_index(), pdf.groupby("b").cumsum().sort_index() ) self.assert_eq( psdf.groupby(["a", "b"]).cumsum().sort_index(), pdf.groupby(["a", "b"]).cumsum().sort_index(), ) self.assert_eq( psdf.groupby(["b"])["a"].cumsum().sort_index(), pdf.groupby(["b"])["a"].cumsum().sort_index(), ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].cumsum().sort_index(), pdf.groupby(["b"])[["a", "c"]].cumsum().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).cumsum().sort_index(), pdf.groupby(pdf.b // 5).cumsum().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].cumsum().sort_index(), pdf.groupby(pdf.b // 5)["a"].cumsum().sort_index(), ) self.assert_eq( psdf.groupby("b").cumsum().sum().sort_index(), pdf.groupby("b").cumsum().sum().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).cumsum().sort_index(), pdf.a.rename().groupby(pdf.b).cumsum().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).cumsum().sort_index(), pdf.a.groupby(pdf.b.rename()).cumsum().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).cumsum().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumsum().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).cumsum().sort_index(), pdf.groupby(("x", "b")).cumsum().sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).cumsum().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumsum().sort_index(), ) psdf = ps.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"]).cumsum()) psdf = ps.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"])["B"].cumsum()) def test_cumprod(self): pdf = pd.DataFrame( { "a": [1, 2, -3, 4, -5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 0, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").cumprod().sort_index(), pdf.groupby("b").cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby(["a", "b"]).cumprod().sort_index(), pdf.groupby(["a", "b"]).cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby(["b"])["a"].cumprod().sort_index(), pdf.groupby(["b"])["a"].cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby(["b"])[["a", "c"]].cumprod().sort_index(), pdf.groupby(["b"])[["a", "c"]].cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby(psdf.b // 3).cumprod().sort_index(), pdf.groupby(pdf.b // 3).cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby(psdf.b // 3)["a"].cumprod().sort_index(), pdf.groupby(pdf.b // 3)["a"].cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby("b").cumprod().sum().sort_index(), pdf.groupby("b").cumprod().sum().sort_index(), check_exact=False, ) self.assert_eq( psdf.a.rename().groupby(psdf.b).cumprod().sort_index(), pdf.a.rename().groupby(pdf.b).cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).cumprod().sort_index(), pdf.a.groupby(pdf.b.rename()).cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).cumprod().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumprod().sort_index(), check_exact=False, ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).cumprod().sort_index(), pdf.groupby(("x", "b")).cumprod().sort_index(), check_exact=False, ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).cumprod().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumprod().sort_index(), check_exact=False, ) psdf = ps.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"]).cumprod()) psdf = ps.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: psdf.groupby(["A"])["B"].cumprod()) def test_nsmallest(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "c": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "d": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, }, index=np.random.rand(9 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby(["a"])["b"].nsmallest(1).sort_values(), pdf.groupby(["a"])["b"].nsmallest(1).sort_values(), ) self.assert_eq( psdf.groupby(["a"])["b"].nsmallest(2).sort_index(), pdf.groupby(["a"])["b"].nsmallest(2).sort_index(), ) self.assert_eq( (psdf.b * 10).groupby(psdf.a).nsmallest(2).sort_index(), (pdf.b * 10).groupby(pdf.a).nsmallest(2).sort_index(), ) self.assert_eq( psdf.b.rename().groupby(psdf.a).nsmallest(2).sort_index(), pdf.b.rename().groupby(pdf.a).nsmallest(2).sort_index(), ) self.assert_eq( psdf.b.groupby(psdf.a.rename()).nsmallest(2).sort_index(), pdf.b.groupby(pdf.a.rename()).nsmallest(2).sort_index(), ) self.assert_eq( psdf.b.rename().groupby(psdf.a.rename()).nsmallest(2).sort_index(), pdf.b.rename().groupby(pdf.a.rename()).nsmallest(2).sort_index(), ) with self.assertRaisesRegex(ValueError, "nsmallest do not support multi-index now"): psdf.set_index(["a", "b"]).groupby(["c"])["d"].nsmallest(1) def test_nlargest(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "c": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "d": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, }, index=np.random.rand(9 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby(["a"])["b"].nlargest(1).sort_values(), pdf.groupby(["a"])["b"].nlargest(1).sort_values(), ) self.assert_eq( psdf.groupby(["a"])["b"].nlargest(2).sort_index(), pdf.groupby(["a"])["b"].nlargest(2).sort_index(), ) self.assert_eq( (psdf.b * 10).groupby(psdf.a).nlargest(2).sort_index(), (pdf.b * 10).groupby(pdf.a).nlargest(2).sort_index(), ) self.assert_eq( psdf.b.rename().groupby(psdf.a).nlargest(2).sort_index(), pdf.b.rename().groupby(pdf.a).nlargest(2).sort_index(), ) self.assert_eq( psdf.b.groupby(psdf.a.rename()).nlargest(2).sort_index(), pdf.b.groupby(pdf.a.rename()).nlargest(2).sort_index(), ) self.assert_eq( psdf.b.rename().groupby(psdf.a.rename()).nlargest(2).sort_index(), pdf.b.rename().groupby(pdf.a.rename()).nlargest(2).sort_index(), ) with self.assertRaisesRegex(ValueError, "nlargest do not support multi-index now"): psdf.set_index(["a", "b"]).groupby(["c"])["d"].nlargest(1) def test_fillna(self): pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, } ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("A").fillna(0).sort_index(), pdf.groupby("A").fillna(0).sort_index() ) self.assert_eq( psdf.groupby("A")["C"].fillna(0).sort_index(), pdf.groupby("A")["C"].fillna(0).sort_index(), ) self.assert_eq( psdf.groupby("A")[["C"]].fillna(0).sort_index(), pdf.groupby("A")[["C"]].fillna(0).sort_index(), ) self.assert_eq( psdf.groupby("A").fillna(method="bfill").sort_index(), pdf.groupby("A").fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby("A")["C"].fillna(method="bfill").sort_index(), pdf.groupby("A")["C"].fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby("A")[["C"]].fillna(method="bfill").sort_index(), pdf.groupby("A")[["C"]].fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby("A").fillna(method="ffill").sort_index(), pdf.groupby("A").fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.groupby("A")["C"].fillna(method="ffill").sort_index(), pdf.groupby("A")["C"].fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.groupby("A")[["C"]].fillna(method="ffill").sort_index(), pdf.groupby("A")[["C"]].fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5).fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5).fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5)["C"].fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5)["C"].fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5)[["C"]].fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5)[["C"]].fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5).fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5).fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5)["C"].fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5)["C"].fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.groupby(psdf.A // 5)[["C"]].fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5)[["C"]].fillna(method="ffill").sort_index(), ) self.assert_eq( psdf.C.rename().groupby(psdf.A).fillna(0).sort_index(), pdf.C.rename().groupby(pdf.A).fillna(0).sort_index(), ) self.assert_eq( psdf.C.groupby(psdf.A.rename()).fillna(0).sort_index(), pdf.C.groupby(pdf.A.rename()).fillna(0).sort_index(), ) self.assert_eq( psdf.C.rename().groupby(psdf.A.rename()).fillna(0).sort_index(), pdf.C.rename().groupby(pdf.A.rename()).fillna(0).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("X", "A")).fillna(0).sort_index(), pdf.groupby(("X", "A")).fillna(0).sort_index(), ) self.assert_eq( psdf.groupby(("X", "A")).fillna(method="bfill").sort_index(), pdf.groupby(("X", "A")).fillna(method="bfill").sort_index(), ) self.assert_eq( psdf.groupby(("X", "A")).fillna(method="ffill").sort_index(), pdf.groupby(("X", "A")).fillna(method="ffill").sort_index(), ) def test_ffill(self): idx = np.random.rand(4 * 3) pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, }, index=idx, ) psdf = ps.from_pandas(pdf) if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( psdf.groupby("A").ffill().sort_index(), pdf.groupby("A").ffill().sort_index().drop("A", 1), ) self.assert_eq( psdf.groupby("A")[["B"]].ffill().sort_index(), pdf.groupby("A")[["B"]].ffill().sort_index().drop("A", 1), ) else: self.assert_eq( psdf.groupby("A").ffill().sort_index(), pdf.groupby("A").ffill().sort_index() ) self.assert_eq( psdf.groupby("A")[["B"]].ffill().sort_index(), pdf.groupby("A")[["B"]].ffill().sort_index(), ) self.assert_eq( psdf.groupby("A")["B"].ffill().sort_index(), pdf.groupby("A")["B"].ffill().sort_index() ) self.assert_eq( psdf.groupby("A")["B"].ffill()[idx[6]], pdf.groupby("A")["B"].ffill()[idx[6]] ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns psdf.columns = columns if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( psdf.groupby(("X", "A")).ffill().sort_index(), pdf.groupby(("X", "A")).ffill().sort_index().drop(("X", "A"), 1), ) else: self.assert_eq( psdf.groupby(("X", "A")).ffill().sort_index(), pdf.groupby(("X", "A")).ffill().sort_index(), ) def test_bfill(self): idx = np.random.rand(4 * 3) pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, }, index=idx, ) psdf = ps.from_pandas(pdf) if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( psdf.groupby("A").bfill().sort_index(), pdf.groupby("A").bfill().sort_index().drop("A", 1), ) self.assert_eq( psdf.groupby("A")[["B"]].bfill().sort_index(), pdf.groupby("A")[["B"]].bfill().sort_index().drop("A", 1), ) else: self.assert_eq( psdf.groupby("A").bfill().sort_index(), pdf.groupby("A").bfill().sort_index() ) self.assert_eq( psdf.groupby("A")[["B"]].bfill().sort_index(), pdf.groupby("A")[["B"]].bfill().sort_index(), ) self.assert_eq( psdf.groupby("A")["B"].bfill().sort_index(), pdf.groupby("A")["B"].bfill().sort_index(), ) self.assert_eq( psdf.groupby("A")["B"].bfill()[idx[6]], pdf.groupby("A")["B"].bfill()[idx[6]] ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns psdf.columns = columns if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( psdf.groupby(("X", "A")).bfill().sort_index(), pdf.groupby(("X", "A")).bfill().sort_index().drop(("X", "A"), 1), ) else: self.assert_eq( psdf.groupby(("X", "A")).bfill().sort_index(), pdf.groupby(("X", "A")).bfill().sort_index(), ) @unittest.skipIf(pd.__version__ < "0.24.0", "not supported before pandas 0.24.0") def test_shift(self): pdf = pd.DataFrame( { "a": [1, 1, 2, 2, 3, 3] * 3, "b": [1, 1, 2, 2, 3, 4] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("a").shift().sort_index(), pdf.groupby("a").shift().sort_index() ) # TODO: seems like a pandas' bug when fill_value is not None? # self.assert_eq(psdf.groupby(['a', 'b']).shift(periods=-1, fill_value=0).sort_index(), # pdf.groupby(['a', 'b']).shift(periods=-1, fill_value=0).sort_index()) self.assert_eq( psdf.groupby(["b"])["a"].shift().sort_index(), pdf.groupby(["b"])["a"].shift().sort_index(), ) self.assert_eq( psdf.groupby(["a", "b"])["c"].shift().sort_index(), pdf.groupby(["a", "b"])["c"].shift().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).shift().sort_index(), pdf.groupby(pdf.b // 5).shift().sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].shift().sort_index(), pdf.groupby(pdf.b // 5)["a"].shift().sort_index(), ) # TODO: known pandas' bug when fill_value is not None pandas>=1.0.0 # https://github.com/pandas-dev/pandas/issues/31971#issue-565171762 if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): self.assert_eq( psdf.groupby(["b"])[["a", "c"]].shift(periods=-1, fill_value=0).sort_index(), pdf.groupby(["b"])[["a", "c"]].shift(periods=-1, fill_value=0).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).shift().sort_index(), pdf.a.rename().groupby(pdf.b).shift().sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).shift().sort_index(), pdf.a.groupby(pdf.b.rename()).shift().sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).shift().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).shift().sort_index(), ) self.assert_eq(psdf.groupby("a").shift().sum(), pdf.groupby("a").shift().sum().astype(int)) self.assert_eq( psdf.a.rename().groupby(psdf.b).shift().sum(), pdf.a.rename().groupby(pdf.b).shift().sum(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "a")).shift().sort_index(), pdf.groupby(("x", "a")).shift().sort_index(), ) # TODO: seems like a pandas' bug when fill_value is not None? # self.assert_eq(psdf.groupby([('x', 'a'), ('x', 'b')]).shift(periods=-1, # fill_value=0).sort_index(), # pdf.groupby([('x', 'a'), ('x', 'b')]).shift(periods=-1, # fill_value=0).sort_index()) def test_apply(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").apply(lambda x: x + x.min()).sort_index(), pdf.groupby("b").apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby("b").apply(len).sort_index(), pdf.groupby("b").apply(len).sort_index(), ) self.assert_eq( psdf.groupby("b")["a"] .apply(lambda x, y, z: x + x.min() + y * z, 10, z=20) .sort_index(), pdf.groupby("b")["a"].apply(lambda x, y, z: x + x.min() + y * z, 10, z=20).sort_index(), ) self.assert_eq( psdf.groupby("b")[["a"]].apply(lambda x: x + x.min()).sort_index(), pdf.groupby("b")[["a"]].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(["a", "b"]) .apply(lambda x, y, z: x + x.min() + y + z, 1, z=2) .sort_index(), pdf.groupby(["a", "b"]).apply(lambda x, y, z: x + x.min() + y + z, 1, z=2).sort_index(), ) self.assert_eq( psdf.groupby(["b"])["c"].apply(lambda x: 1).sort_index(), pdf.groupby(["b"])["c"].apply(lambda x: 1).sort_index(), ) self.assert_eq( psdf.groupby(["b"])["c"].apply(len).sort_index(), pdf.groupby(["b"])["c"].apply(len).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)[["a"]].apply(len).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].apply(len).sort_index(), almost=True, ) self.assert_eq( psdf.a.rename().groupby(psdf.b).apply(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), pdf.a.groupby(pdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), ) with self.assertRaisesRegex(TypeError, "int object is not callable"): psdf.groupby("b").apply(1) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).apply(lambda x: 1).sort_index(), pdf.groupby(("x", "b")).apply(lambda x: 1).sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).apply(lambda x: x + x.min()).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(("x", "b")).apply(len).sort_index(), pdf.groupby(("x", "b")).apply(len).sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).apply(len).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).apply(len).sort_index(), ) def test_apply_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_apply() def test_apply_negative(self): def func(_) -> ps.Series[int]: return pd.Series([1]) with self.assertRaisesRegex(TypeError, "Series as a return type hint at frame groupby"): ps.range(10).groupby("id").apply(func) def test_apply_with_new_dataframe(self): pdf = pd.DataFrame( {"timestamp": [0.0, 0.5, 1.0, 0.0, 0.5], "car_id": ["A", "A", "A", "B", "B"]} ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), pdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), ) self.assert_eq( psdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), pdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), ) # dataframe with 1000+ records pdf = pd.DataFrame( { "timestamp": [0.0, 0.5, 1.0, 0.0, 0.5] * 300, "car_id": ["A", "A", "A", "B", "B"] * 300, } ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), pdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), ) self.assert_eq( psdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), pdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), ) def test_apply_with_new_dataframe_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_apply_with_new_dataframe() def test_apply_key_handling(self): pdf = pd.DataFrame( {"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], "v": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]} ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("d").apply(sum).sort_index(), pdf.groupby("d").apply(sum).sort_index() ) with ps.option_context("compute.shortcut_limit", 1): self.assert_eq( psdf.groupby("d").apply(sum).sort_index(), pdf.groupby("d").apply(sum).sort_index() ) def test_apply_with_side_effect(self): pdf = pd.DataFrame( {"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], "v": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]} ) psdf = ps.from_pandas(pdf) acc = ps.utils.default_session().sparkContext.accumulator(0) def sum_with_acc_frame(x) -> ps.DataFrame[np.float64, np.float64]: nonlocal acc acc += 1 return np.sum(x) actual = psdf.groupby("d").apply(sum_with_acc_frame).sort_index() actual.columns = ["d", "v"] self.assert_eq(actual, pdf.groupby("d").apply(sum).sort_index().reset_index(drop=True)) self.assert_eq(acc.value, 2) def sum_with_acc_series(x) -> np.float64: nonlocal acc acc += 1 return np.sum(x) self.assert_eq( psdf.groupby("d")["v"].apply(sum_with_acc_series).sort_index(), pdf.groupby("d")["v"].apply(sum).sort_index().reset_index(drop=True), ) self.assert_eq(acc.value, 4) def test_transform(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b").transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby("b")["a"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b")["a"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby("b")[["a"]].transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b")[["a"]].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(["a", "b"]).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(["a", "b"]).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(["b"])["c"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(["b"])["c"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)["a"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)["a"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby(psdf.b // 5)[["a"]].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).transform(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), pdf.a.groupby(pdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(("x", "b")).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]).transform(lambda x: x + x.min()).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).transform(lambda x: x + x.min()).sort_index(), ) def test_transform_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_transform() def test_filter(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("b").filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby("b").filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( psdf.groupby("b")["a"].filter(lambda x: any(x == 2)).sort_index(), pdf.groupby("b")["a"].filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( psdf.groupby("b")[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby("b")[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( psdf.groupby(["a", "b"]).filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(["a", "b"]).filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( psdf.groupby(psdf["b"] // 5).filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(pdf["b"] // 5).filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( psdf.groupby(psdf["b"] // 5)["a"].filter(lambda x: any(x == 2)).sort_index(), pdf.groupby(pdf["b"] // 5)["a"].filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( psdf.groupby(psdf["b"] // 5)[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(pdf["b"] // 5)[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b).filter(lambda x: any(x == 2)).sort_index(), pdf.a.rename().groupby(pdf.b).filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( psdf.a.groupby(psdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), pdf.a.groupby(pdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( psdf.a.rename().groupby(psdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), ) with self.assertRaisesRegex(TypeError, "int object is not callable"): psdf.groupby("b").filter(1) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( psdf.groupby(("x", "b")).filter(lambda x: any(x[("x", "a")] == 2)).sort_index(), pdf.groupby(("x", "b")).filter(lambda x: any(x[("x", "a")] == 2)).sort_index(), ) self.assert_eq( psdf.groupby([("x", "a"), ("x", "b")]) .filter(lambda x: any(x[("x", "a")] == 2)) .sort_index(), pdf.groupby([("x", "a"), ("x", "b")]) .filter(lambda x: any(x[("x", "a")] == 2)) .sort_index(), ) def test_idxmax(self): pdf = pd.DataFrame( {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby(["a"]).idxmax().sort_index(), psdf.groupby(["a"]).idxmax().sort_index() ) self.assert_eq( pdf.groupby(["a"]).idxmax(skipna=False).sort_index(), psdf.groupby(["a"]).idxmax(skipna=False).sort_index(), ) self.assert_eq( pdf.groupby(["a"])["b"].idxmax().sort_index(), psdf.groupby(["a"])["b"].idxmax().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).idxmax().sort_index(), psdf.b.rename().groupby(psdf.a).idxmax().sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).idxmax().sort_index(), psdf.b.groupby(psdf.a.rename()).idxmax().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).idxmax().sort_index(), psdf.b.rename().groupby(psdf.a.rename()).idxmax().sort_index(), ) with self.assertRaisesRegex(ValueError, "idxmax only support one-level index now"): psdf.set_index(["a", "b"]).groupby(["c"]).idxmax() # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).idxmax().sort_index(), psdf.groupby(("x", "a")).idxmax().sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), psdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), ) def test_idxmin(self): pdf = pd.DataFrame( {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby(["a"]).idxmin().sort_index(), psdf.groupby(["a"]).idxmin().sort_index() ) self.assert_eq( pdf.groupby(["a"]).idxmin(skipna=False).sort_index(), psdf.groupby(["a"]).idxmin(skipna=False).sort_index(), ) self.assert_eq( pdf.groupby(["a"])["b"].idxmin().sort_index(), psdf.groupby(["a"])["b"].idxmin().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).idxmin().sort_index(), psdf.b.rename().groupby(psdf.a).idxmin().sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).idxmin().sort_index(), psdf.b.groupby(psdf.a.rename()).idxmin().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).idxmin().sort_index(), psdf.b.rename().groupby(psdf.a.rename()).idxmin().sort_index(), ) with self.assertRaisesRegex(ValueError, "idxmin only support one-level index now"): psdf.set_index(["a", "b"]).groupby(["c"]).idxmin() # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).idxmin().sort_index(), psdf.groupby(("x", "a")).idxmin().sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), psdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), ) def test_head(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5] * 3, "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6] * 3, }, index=np.random.rand(10 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby("a").head(2).sort_index(), psdf.groupby("a").head(2).sort_index() ) self.assert_eq( pdf.groupby("a").head(-2).sort_index(), psdf.groupby("a").head(-2).sort_index() ) self.assert_eq( pdf.groupby("a").head(100000).sort_index(), psdf.groupby("a").head(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(2).sort_index(), psdf.groupby("a")["b"].head(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(-2).sort_index(), psdf.groupby("a")["b"].head(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")["b"].head(100000).sort_index(), psdf.groupby("a")["b"].head(100000).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(2).sort_index(), psdf.groupby("a")[["b"]].head(2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(-2).sort_index(), psdf.groupby("a")[["b"]].head(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(100000).sort_index(), psdf.groupby("a")[["b"]].head(100000).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2).head(2).sort_index(), psdf.groupby(psdf.a // 2).head(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)["b"].head(2).sort_index(), psdf.groupby(psdf.a // 2)["b"].head(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)[["b"]].head(2).sort_index(), psdf.groupby(psdf.a // 2)[["b"]].head(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).head(2).sort_index(), psdf.b.rename().groupby(psdf.a).head(2).sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).head(2).sort_index(), psdf.b.groupby(psdf.a.rename()).head(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).head(2).sort_index(), psdf.b.rename().groupby(psdf.a.rename()).head(2).sort_index(), ) # multi-index midx = pd.MultiIndex( [["x", "y"], ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]], [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ) pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3], "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5], "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6], }, columns=["a", "b", "c"], index=midx, ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby("a").head(2).sort_index(), psdf.groupby("a").head(2).sort_index() ) self.assert_eq( pdf.groupby("a").head(-2).sort_index(), psdf.groupby("a").head(-2).sort_index() ) self.assert_eq( pdf.groupby("a").head(100000).sort_index(), psdf.groupby("a").head(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(2).sort_index(), psdf.groupby("a")["b"].head(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(-2).sort_index(), psdf.groupby("a")["b"].head(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")["b"].head(100000).sort_index(), psdf.groupby("a")["b"].head(100000).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).head(2).sort_index(), psdf.groupby(("x", "a")).head(2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).head(-2).sort_index(), psdf.groupby(("x", "a")).head(-2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).head(100000).sort_index(), psdf.groupby(("x", "a")).head(100000).sort_index(), ) def test_missing(self): psdf = ps.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9]}) # DataFrameGroupBy functions missing_functions = inspect.getmembers( MissingPandasLikeDataFrameGroupBy, inspect.isfunction ) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(psdf.groupby("a"), name)() deprecated_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "deprecated_function" ] for name in deprecated_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*is deprecated".format(name) ): getattr(psdf.groupby("a"), name)() # SeriesGroupBy functions missing_functions = inspect.getmembers(MissingPandasLikeSeriesGroupBy, inspect.isfunction) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(psdf.a.groupby(psdf.a), name)() deprecated_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "deprecated_function" ] for name in deprecated_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*is deprecated".format(name) ): getattr(psdf.a.groupby(psdf.a), name)() # DataFrameGroupBy properties missing_properties = inspect.getmembers( MissingPandasLikeDataFrameGroupBy, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(psdf.groupby("a"), name) deprecated_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "deprecated_property" ] for name in deprecated_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*is deprecated".format(name) ): getattr(psdf.groupby("a"), name) # SeriesGroupBy properties missing_properties = inspect.getmembers( MissingPandasLikeSeriesGroupBy, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(psdf.a.groupby(psdf.a), name) deprecated_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "deprecated_property" ] for name in deprecated_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*is deprecated".format(name) ): getattr(psdf.a.groupby(psdf.a), name) @staticmethod def test_is_multi_agg_with_relabel(): assert is_multi_agg_with_relabel(a="max") is False assert is_multi_agg_with_relabel(a_min=("a", "max"), a_max=("a", "min")) is True def test_get_group(self): pdf = pd.DataFrame( [ ("falcon", "bird", 389.0), ("parrot", "bird", 24.0), ("lion", "mammal", 80.5), ("monkey", "mammal", np.nan), ], columns=["name", "class", "max_speed"], index=[0, 2, 3, 1], ) pdf.columns.name = "Koalas" psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby("class").get_group("bird"), pdf.groupby("class").get_group("bird"), ) self.assert_eq( psdf.groupby("class")["name"].get_group("mammal"), pdf.groupby("class")["name"].get_group("mammal"), ) self.assert_eq( psdf.groupby("class")[["name"]].get_group("mammal"), pdf.groupby("class")[["name"]].get_group("mammal"), ) self.assert_eq( psdf.groupby(["class", "name"]).get_group(("mammal", "lion")), pdf.groupby(["class", "name"]).get_group(("mammal", "lion")), ) self.assert_eq( psdf.groupby(["class", "name"])["max_speed"].get_group(("mammal", "lion")), pdf.groupby(["class", "name"])["max_speed"].get_group(("mammal", "lion")), ) self.assert_eq( psdf.groupby(["class", "name"])[["max_speed"]].get_group(("mammal", "lion")), pdf.groupby(["class", "name"])[["max_speed"]].get_group(("mammal", "lion")), ) self.assert_eq( (psdf.max_speed + 1).groupby(psdf["class"]).get_group("mammal"), (pdf.max_speed + 1).groupby(pdf["class"]).get_group("mammal"), ) self.assert_eq( psdf.groupby("max_speed").get_group(80.5), pdf.groupby("max_speed").get_group(80.5), ) self.assertRaises(KeyError, lambda: psdf.groupby("class").get_group("fish")) self.assertRaises(TypeError, lambda: psdf.groupby("class").get_group(["bird", "mammal"])) self.assertRaises(KeyError, lambda: psdf.groupby("class")["name"].get_group("fish")) self.assertRaises( TypeError, lambda: psdf.groupby("class")["name"].get_group(["bird", "mammal"]) ) self.assertRaises( KeyError, lambda: psdf.groupby(["class", "name"]).get_group(("lion", "mammal")) ) self.assertRaises(ValueError, lambda: psdf.groupby(["class", "name"]).get_group(("lion",))) self.assertRaises( ValueError, lambda: psdf.groupby(["class", "name"]).get_group(("mammal",)) ) self.assertRaises(ValueError, lambda: psdf.groupby(["class", "name"]).get_group("mammal")) # MultiIndex columns pdf.columns = pd.MultiIndex.from_tuples([("A", "name"), ("B", "class"), ("C", "max_speed")]) pdf.columns.names = ["Hello", "Koalas"] psdf = ps.from_pandas(pdf) self.assert_eq( psdf.groupby(("B", "class")).get_group("bird"), pdf.groupby(("B", "class")).get_group("bird"), ) self.assert_eq( psdf.groupby(("B", "class"))[[("A", "name")]].get_group("mammal"), pdf.groupby(("B", "class"))[[("A", "name")]].get_group("mammal"), ) self.assert_eq( psdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal", "lion")), pdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal", "lion")), ) self.assert_eq( psdf.groupby([("B", "class"), ("A", "name")])[[("C", "max_speed")]].get_group( ("mammal", "lion") ), pdf.groupby([("B", "class"), ("A", "name")])[[("C", "max_speed")]].get_group( ("mammal", "lion") ), ) self.assert_eq( (psdf[("C", "max_speed")] + 1).groupby(psdf[("B", "class")]).get_group("mammal"), (pdf[("C", "max_speed")] + 1).groupby(pdf[("B", "class")]).get_group("mammal"), ) self.assert_eq( psdf.groupby(("C", "max_speed")).get_group(80.5), pdf.groupby(("C", "max_speed")).get_group(80.5), ) self.assertRaises(KeyError, lambda: psdf.groupby(("B", "class")).get_group("fish")) self.assertRaises( TypeError, lambda: psdf.groupby(("B", "class")).get_group(["bird", "mammal"]) ) self.assertRaises( KeyError, lambda: psdf.groupby(("B", "class"))[("A", "name")].get_group("fish") ) self.assertRaises( KeyError, lambda: psdf.groupby([("B", "class"), ("A", "name")]).get_group(("lion", "mammal")), ) self.assertRaises( ValueError, lambda: psdf.groupby([("B", "class"), ("A", "name")]).get_group(("lion",)), ) self.assertRaises( ValueError, lambda: psdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal",)) ) self.assertRaises( ValueError, lambda: psdf.groupby([("B", "class"), ("A", "name")]).get_group("mammal") ) def test_median(self): psdf = ps.DataFrame( { "a": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0], "b": [2.0, 3.0, 1.0, 4.0, 6.0, 9.0, 8.0, 10.0, 7.0, 5.0], "c": [3.0, 5.0, 2.0, 5.0, 1.0, 2.0, 6.0, 4.0, 3.0, 6.0], }, columns=["a", "b", "c"], index=[7, 2, 4, 1, 3, 4, 9, 10, 5, 6], ) # DataFrame expected_result = ps.DataFrame( {"b": [2.0, 8.0, 7.0], "c": [3.0, 2.0, 4.0]}, index=pd.Index([1.0, 2.0, 3.0], name="a") ) self.assert_eq(expected_result, psdf.groupby("a").median().sort_index()) # Series expected_result = ps.Series( [2.0, 8.0, 7.0], name="b", index=pd.Index([1.0, 2.0, 3.0], name="a") ) self.assert_eq(expected_result, psdf.groupby("a")["b"].median().sort_index()) with self.assertRaisesRegex(TypeError, "accuracy must be an integer; however"): psdf.groupby("a").median(accuracy="a") def test_tail(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5] * 3, "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6] * 3, }, index=np.random.rand(10 * 3), ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby("a").tail(2).sort_index(), psdf.groupby("a").tail(2).sort_index() ) self.assert_eq( pdf.groupby("a").tail(-2).sort_index(), psdf.groupby("a").tail(-2).sort_index() ) self.assert_eq( pdf.groupby("a").tail(100000).sort_index(), psdf.groupby("a").tail(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].tail(2).sort_index(), psdf.groupby("a")["b"].tail(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].tail(-2).sort_index(), psdf.groupby("a")["b"].tail(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")["b"].tail(100000).sort_index(), psdf.groupby("a")["b"].tail(100000).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].tail(2).sort_index(), psdf.groupby("a")[["b"]].tail(2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].tail(-2).sort_index(), psdf.groupby("a")[["b"]].tail(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].tail(100000).sort_index(), psdf.groupby("a")[["b"]].tail(100000).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2).tail(2).sort_index(), psdf.groupby(psdf.a // 2).tail(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)["b"].tail(2).sort_index(), psdf.groupby(psdf.a // 2)["b"].tail(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)[["b"]].tail(2).sort_index(), psdf.groupby(psdf.a // 2)[["b"]].tail(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).tail(2).sort_index(), psdf.b.rename().groupby(psdf.a).tail(2).sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).tail(2).sort_index(), psdf.b.groupby(psdf.a.rename()).tail(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).tail(2).sort_index(), psdf.b.rename().groupby(psdf.a.rename()).tail(2).sort_index(), ) # multi-index midx = pd.MultiIndex( [["x", "y"], ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]], [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ) pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3], "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5], "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6], }, columns=["a", "b", "c"], index=midx, ) psdf = ps.from_pandas(pdf) self.assert_eq( pdf.groupby("a").tail(2).sort_index(), psdf.groupby("a").tail(2).sort_index() ) self.assert_eq( pdf.groupby("a").tail(-2).sort_index(), psdf.groupby("a").tail(-2).sort_index() ) self.assert_eq( pdf.groupby("a").tail(100000).sort_index(), psdf.groupby("a").tail(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].tail(2).sort_index(), psdf.groupby("a")["b"].tail(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].tail(-2).sort_index(), psdf.groupby("a")["b"].tail(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")["b"].tail(100000).sort_index(), psdf.groupby("a")["b"].tail(100000).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns psdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).tail(2).sort_index(), psdf.groupby(("x", "a")).tail(2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).tail(-2).sort_index(), psdf.groupby(("x", "a")).tail(-2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).tail(100000).sort_index(), psdf.groupby(("x", "a")).tail(100000).sort_index(), ) def test_ddof(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5] * 3, "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6] * 3, }, index=np.random.rand(10 * 3), ) psdf = ps.from_pandas(pdf) for ddof in (0, 1): # std self.assert_eq( pdf.groupby("a").std(ddof=ddof).sort_index(), psdf.groupby("a").std(ddof=ddof).sort_index(), check_exact=False, ) self.assert_eq( pdf.groupby("a")["b"].std(ddof=ddof).sort_index(), psdf.groupby("a")["b"].std(ddof=ddof).sort_index(), check_exact=False, ) # var self.assert_eq( pdf.groupby("a").var(ddof=ddof).sort_index(), psdf.groupby("a").var(ddof=ddof).sort_index(), check_exact=False, ) self.assert_eq( pdf.groupby("a")["b"].var(ddof=ddof).sort_index(), psdf.groupby("a")["b"].var(ddof=ddof).sort_index(), check_exact=False, ) if __name__ == "__main__": from pyspark.pandas.tests.test_groupby 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)