| # |
| # 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("1.0.4") |
| and LooseVersion(pd.__version__) != LooseVersion("1.1.3") |
| and LooseVersion(pd.__version__) != LooseVersion("1.1.4") |
| ): |
| self.assert_eq( |
| psdf[(20, "c")].groupby(psdf[(10, "a")]).sum().sort_index(), |
| pdf[(20, "c")].groupby(pdf[(10, "a")]).sum().sort_index(), |
| ) |
| else: |
| # Due to pandas bugs resolved in 1.0.4, re-introduced in 1.1.3 and resolved in 1.1.5 |
| 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) |