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- from datetime import (
- date,
- datetime,
- )
- from io import StringIO
- import numpy as np
- import pytest
- import pandas as pd
- from pandas import (
- DataFrame,
- Index,
- MultiIndex,
- Series,
- bdate_range,
- )
- import pandas._testing as tm
- from pandas.tests.groupby import get_groupby_method_args
- def test_apply_issues():
- # GH 5788
- s = """2011.05.16,00:00,1.40893
- 2011.05.16,01:00,1.40760
- 2011.05.16,02:00,1.40750
- 2011.05.16,03:00,1.40649
- 2011.05.17,02:00,1.40893
- 2011.05.17,03:00,1.40760
- 2011.05.17,04:00,1.40750
- 2011.05.17,05:00,1.40649
- 2011.05.18,02:00,1.40893
- 2011.05.18,03:00,1.40760
- 2011.05.18,04:00,1.40750
- 2011.05.18,05:00,1.40649"""
- df = pd.read_csv(
- StringIO(s),
- header=None,
- names=["date", "time", "value"],
- parse_dates=[["date", "time"]],
- )
- df = df.set_index("date_time")
- expected = df.groupby(df.index.date).idxmax()
- result = df.groupby(df.index.date).apply(lambda x: x.idxmax())
- tm.assert_frame_equal(result, expected)
- # GH 5789
- # don't auto coerce dates
- df = pd.read_csv(StringIO(s), header=None, names=["date", "time", "value"])
- exp_idx = Index(
- ["2011.05.16", "2011.05.17", "2011.05.18"], dtype=object, name="date"
- )
- expected = Series(["00:00", "02:00", "02:00"], index=exp_idx)
- result = df.groupby("date", group_keys=False).apply(
- lambda x: x["time"][x["value"].idxmax()]
- )
- tm.assert_series_equal(result, expected)
- def test_apply_trivial():
- # GH 20066
- # trivial apply: ignore input and return a constant dataframe.
- df = DataFrame(
- {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
- columns=["key", "data"],
- )
- expected = pd.concat([df.iloc[1:], df.iloc[1:]], axis=1, keys=["float64", "object"])
- result = df.groupby([str(x) for x in df.dtypes], axis=1).apply(
- lambda x: df.iloc[1:]
- )
- tm.assert_frame_equal(result, expected)
- def test_apply_trivial_fail():
- # GH 20066
- df = DataFrame(
- {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
- columns=["key", "data"],
- )
- expected = pd.concat([df, df], axis=1, keys=["float64", "object"])
- result = df.groupby([str(x) for x in df.dtypes], axis=1, group_keys=True).apply(
- lambda x: df
- )
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize(
- "df, group_names",
- [
- (DataFrame({"a": [1, 1, 1, 2, 3], "b": ["a", "a", "a", "b", "c"]}), [1, 2, 3]),
- (DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]}), [0, 1]),
- (DataFrame({"a": [1]}), [1]),
- (DataFrame({"a": [1, 1, 1, 2, 2, 1, 1, 2], "b": range(8)}), [1, 2]),
- (DataFrame({"a": [1, 2, 3, 1, 2, 3], "two": [4, 5, 6, 7, 8, 9]}), [1, 2, 3]),
- (
- DataFrame(
- {
- "a": list("aaabbbcccc"),
- "B": [3, 4, 3, 6, 5, 2, 1, 9, 5, 4],
- "C": [4, 0, 2, 2, 2, 7, 8, 6, 2, 8],
- }
- ),
- ["a", "b", "c"],
- ),
- (DataFrame([[1, 2, 3], [2, 2, 3]], columns=["a", "b", "c"]), [1, 2]),
- ],
- ids=[
- "GH2936",
- "GH7739 & GH10519",
- "GH10519",
- "GH2656",
- "GH12155",
- "GH20084",
- "GH21417",
- ],
- )
- def test_group_apply_once_per_group(df, group_names):
- # GH2936, GH7739, GH10519, GH2656, GH12155, GH20084, GH21417
- # This test should ensure that a function is only evaluated
- # once per group. Previously the function has been evaluated twice
- # on the first group to check if the Cython index slider is safe to use
- # This test ensures that the side effect (append to list) is only triggered
- # once per group
- names = []
- # cannot parameterize over the functions since they need external
- # `names` to detect side effects
- def f_copy(group):
- # this takes the fast apply path
- names.append(group.name)
- return group.copy()
- def f_nocopy(group):
- # this takes the slow apply path
- names.append(group.name)
- return group
- def f_scalar(group):
- # GH7739, GH2656
- names.append(group.name)
- return 0
- def f_none(group):
- # GH10519, GH12155, GH21417
- names.append(group.name)
- def f_constant_df(group):
- # GH2936, GH20084
- names.append(group.name)
- return DataFrame({"a": [1], "b": [1]})
- for func in [f_copy, f_nocopy, f_scalar, f_none, f_constant_df]:
- del names[:]
- df.groupby("a", group_keys=False).apply(func)
- assert names == group_names
- def test_group_apply_once_per_group2(capsys):
- # GH: 31111
- # groupby-apply need to execute len(set(group_by_columns)) times
- expected = 2 # Number of times `apply` should call a function for the current test
- df = DataFrame(
- {
- "group_by_column": [0, 0, 0, 0, 1, 1, 1, 1],
- "test_column": ["0", "2", "4", "6", "8", "10", "12", "14"],
- },
- index=["0", "2", "4", "6", "8", "10", "12", "14"],
- )
- df.groupby("group_by_column", group_keys=False).apply(
- lambda df: print("function_called")
- )
- result = capsys.readouterr().out.count("function_called")
- # If `groupby` behaves unexpectedly, this test will break
- assert result == expected
- def test_apply_fast_slow_identical():
- # GH 31613
- df = DataFrame({"A": [0, 0, 1], "b": range(3)})
- # For simple index structures we check for fast/slow apply using
- # an identity check on in/output
- def slow(group):
- return group
- def fast(group):
- return group.copy()
- fast_df = df.groupby("A", group_keys=False).apply(fast)
- slow_df = df.groupby("A", group_keys=False).apply(slow)
- tm.assert_frame_equal(fast_df, slow_df)
- @pytest.mark.parametrize(
- "func",
- [
- lambda x: x,
- lambda x: x[:],
- lambda x: x.copy(deep=False),
- lambda x: x.copy(deep=True),
- ],
- )
- def test_groupby_apply_identity_maybecopy_index_identical(func):
- # GH 14927
- # Whether the function returns a copy of the input data or not should not
- # have an impact on the index structure of the result since this is not
- # transparent to the user
- df = DataFrame({"g": [1, 2, 2, 2], "a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
- result = df.groupby("g", group_keys=False).apply(func)
- tm.assert_frame_equal(result, df)
- def test_apply_with_mixed_dtype():
- # GH3480, apply with mixed dtype on axis=1 breaks in 0.11
- df = DataFrame(
- {
- "foo1": np.random.randn(6),
- "foo2": ["one", "two", "two", "three", "one", "two"],
- }
- )
- result = df.apply(lambda x: x, axis=1).dtypes
- expected = df.dtypes
- tm.assert_series_equal(result, expected)
- # GH 3610 incorrect dtype conversion with as_index=False
- df = DataFrame({"c1": [1, 2, 6, 6, 8]})
- df["c2"] = df.c1 / 2.0
- result1 = df.groupby("c2").mean().reset_index().c2
- result2 = df.groupby("c2", as_index=False).mean().c2
- tm.assert_series_equal(result1, result2)
- def test_groupby_as_index_apply():
- # GH #4648 and #3417
- df = DataFrame(
- {
- "item_id": ["b", "b", "a", "c", "a", "b"],
- "user_id": [1, 2, 1, 1, 3, 1],
- "time": range(6),
- }
- )
- g_as = df.groupby("user_id", as_index=True)
- g_not_as = df.groupby("user_id", as_index=False)
- res_as = g_as.head(2).index
- res_not_as = g_not_as.head(2).index
- exp = Index([0, 1, 2, 4])
- tm.assert_index_equal(res_as, exp)
- tm.assert_index_equal(res_not_as, exp)
- res_as_apply = g_as.apply(lambda x: x.head(2)).index
- res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index
- # apply doesn't maintain the original ordering
- # changed in GH5610 as the as_index=False returns a MI here
- exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (2, 4)])
- tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
- exp_as_apply = MultiIndex.from_tuples(tp, names=["user_id", None])
- tm.assert_index_equal(res_as_apply, exp_as_apply)
- tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)
- ind = Index(list("abcde"))
- df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
- res = df.groupby(0, as_index=False, group_keys=False).apply(lambda x: x).index
- tm.assert_index_equal(res, ind)
- def test_apply_concat_preserve_names(three_group):
- grouped = three_group.groupby(["A", "B"])
- def desc(group):
- result = group.describe()
- result.index.name = "stat"
- return result
- def desc2(group):
- result = group.describe()
- result.index.name = "stat"
- result = result[: len(group)]
- # weirdo
- return result
- def desc3(group):
- result = group.describe()
- # names are different
- result.index.name = f"stat_{len(group):d}"
- result = result[: len(group)]
- # weirdo
- return result
- result = grouped.apply(desc)
- assert result.index.names == ("A", "B", "stat")
- result2 = grouped.apply(desc2)
- assert result2.index.names == ("A", "B", "stat")
- result3 = grouped.apply(desc3)
- assert result3.index.names == ("A", "B", None)
- def test_apply_series_to_frame():
- def f(piece):
- with np.errstate(invalid="ignore"):
- logged = np.log(piece)
- return DataFrame(
- {"value": piece, "demeaned": piece - piece.mean(), "logged": logged}
- )
- dr = bdate_range("1/1/2000", periods=100)
- ts = Series(np.random.randn(100), index=dr)
- grouped = ts.groupby(lambda x: x.month, group_keys=False)
- result = grouped.apply(f)
- assert isinstance(result, DataFrame)
- assert not hasattr(result, "name") # GH49907
- tm.assert_index_equal(result.index, ts.index)
- def test_apply_series_yield_constant(df):
- result = df.groupby(["A", "B"])["C"].apply(len)
- assert result.index.names[:2] == ("A", "B")
- def test_apply_frame_yield_constant(df):
- # GH13568
- result = df.groupby(["A", "B"]).apply(len)
- assert isinstance(result, Series)
- assert result.name is None
- result = df.groupby(["A", "B"])[["C", "D"]].apply(len)
- assert isinstance(result, Series)
- assert result.name is None
- def test_apply_frame_to_series(df):
- grouped = df.groupby(["A", "B"])
- result = grouped.apply(len)
- expected = grouped.count()["C"]
- tm.assert_index_equal(result.index, expected.index)
- tm.assert_numpy_array_equal(result.values, expected.values)
- def test_apply_frame_not_as_index_column_name(df):
- # GH 35964 - path within _wrap_applied_output not hit by a test
- grouped = df.groupby(["A", "B"], as_index=False)
- result = grouped.apply(len)
- expected = grouped.count().rename(columns={"C": np.nan}).drop(columns="D")
- # TODO(GH#34306): Use assert_frame_equal when column name is not np.nan
- tm.assert_index_equal(result.index, expected.index)
- tm.assert_numpy_array_equal(result.values, expected.values)
- def test_apply_frame_concat_series():
- def trans(group):
- return group.groupby("B")["C"].sum().sort_values().iloc[:2]
- def trans2(group):
- grouped = group.groupby(df.reindex(group.index)["B"])
- return grouped.sum().sort_values().iloc[:2]
- df = DataFrame(
- {
- "A": np.random.randint(0, 5, 1000),
- "B": np.random.randint(0, 5, 1000),
- "C": np.random.randn(1000),
- }
- )
- result = df.groupby("A").apply(trans)
- exp = df.groupby("A")["C"].apply(trans2)
- tm.assert_series_equal(result, exp, check_names=False)
- assert result.name == "C"
- def test_apply_transform(ts):
- grouped = ts.groupby(lambda x: x.month, group_keys=False)
- result = grouped.apply(lambda x: x * 2)
- expected = grouped.transform(lambda x: x * 2)
- tm.assert_series_equal(result, expected)
- def test_apply_multikey_corner(tsframe):
- grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
- def f(group):
- return group.sort_values("A")[-5:]
- result = grouped.apply(f)
- for key, group in grouped:
- tm.assert_frame_equal(result.loc[key], f(group))
- @pytest.mark.parametrize("group_keys", [True, False])
- def test_apply_chunk_view(group_keys):
- # Low level tinkering could be unsafe, make sure not
- df = DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
- result = df.groupby("key", group_keys=group_keys).apply(lambda x: x.iloc[:2])
- expected = df.take([0, 1, 3, 4, 6, 7])
- if group_keys:
- expected.index = MultiIndex.from_arrays(
- [[1, 1, 2, 2, 3, 3], expected.index], names=["key", None]
- )
- tm.assert_frame_equal(result, expected)
- def test_apply_no_name_column_conflict():
- df = DataFrame(
- {
- "name": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
- "name2": [0, 0, 0, 1, 1, 1, 0, 0, 1, 1],
- "value": range(9, -1, -1),
- }
- )
- # it works! #2605
- grouped = df.groupby(["name", "name2"])
- grouped.apply(lambda x: x.sort_values("value", inplace=True))
- def test_apply_typecast_fail():
- df = DataFrame(
- {
- "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
- "c": np.tile(["a", "b", "c"], 2),
- "v": np.arange(1.0, 7.0),
- }
- )
- def f(group):
- v = group["v"]
- group["v2"] = (v - v.min()) / (v.max() - v.min())
- return group
- result = df.groupby("d", group_keys=False).apply(f)
- expected = df.copy()
- expected["v2"] = np.tile([0.0, 0.5, 1], 2)
- tm.assert_frame_equal(result, expected)
- def test_apply_multiindex_fail():
- index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]])
- df = DataFrame(
- {
- "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
- "c": np.tile(["a", "b", "c"], 2),
- "v": np.arange(1.0, 7.0),
- },
- index=index,
- )
- def f(group):
- v = group["v"]
- group["v2"] = (v - v.min()) / (v.max() - v.min())
- return group
- result = df.groupby("d", group_keys=False).apply(f)
- expected = df.copy()
- expected["v2"] = np.tile([0.0, 0.5, 1], 2)
- tm.assert_frame_equal(result, expected)
- def test_apply_corner(tsframe):
- result = tsframe.groupby(lambda x: x.year, group_keys=False).apply(lambda x: x * 2)
- expected = tsframe * 2
- tm.assert_frame_equal(result, expected)
- def test_apply_without_copy():
- # GH 5545
- # returning a non-copy in an applied function fails
- data = DataFrame(
- {
- "id_field": [100, 100, 200, 300],
- "category": ["a", "b", "c", "c"],
- "value": [1, 2, 3, 4],
- }
- )
- def filt1(x):
- if x.shape[0] == 1:
- return x.copy()
- else:
- return x[x.category == "c"]
- def filt2(x):
- if x.shape[0] == 1:
- return x
- else:
- return x[x.category == "c"]
- expected = data.groupby("id_field").apply(filt1)
- result = data.groupby("id_field").apply(filt2)
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("test_series", [True, False])
- def test_apply_with_duplicated_non_sorted_axis(test_series):
- # GH 30667
- df = DataFrame(
- [["x", "p"], ["x", "p"], ["x", "o"]], columns=["X", "Y"], index=[1, 2, 2]
- )
- if test_series:
- ser = df.set_index("Y")["X"]
- result = ser.groupby(level=0, group_keys=False).apply(lambda x: x)
- # not expecting the order to remain the same for duplicated axis
- result = result.sort_index()
- expected = ser.sort_index()
- tm.assert_series_equal(result, expected)
- else:
- result = df.groupby("Y", group_keys=False).apply(lambda x: x)
- # not expecting the order to remain the same for duplicated axis
- result = result.sort_values("Y")
- expected = df.sort_values("Y")
- tm.assert_frame_equal(result, expected)
- def test_apply_reindex_values():
- # GH: 26209
- # reindexing from a single column of a groupby object with duplicate indices caused
- # a ValueError (cannot reindex from duplicate axis) in 0.24.2, the problem was
- # solved in #30679
- values = [1, 2, 3, 4]
- indices = [1, 1, 2, 2]
- df = DataFrame({"group": ["Group1", "Group2"] * 2, "value": values}, index=indices)
- expected = Series(values, index=indices, name="value")
- def reindex_helper(x):
- return x.reindex(np.arange(x.index.min(), x.index.max() + 1))
- # the following group by raised a ValueError
- result = df.groupby("group", group_keys=False).value.apply(reindex_helper)
- tm.assert_series_equal(expected, result)
- def test_apply_corner_cases():
- # #535, can't use sliding iterator
- N = 1000
- labels = np.random.randint(0, 100, size=N)
- df = DataFrame(
- {
- "key": labels,
- "value1": np.random.randn(N),
- "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
- }
- )
- grouped = df.groupby("key", group_keys=False)
- def f(g):
- g["value3"] = g["value1"] * 2
- return g
- result = grouped.apply(f)
- assert "value3" in result
- def test_apply_numeric_coercion_when_datetime():
- # In the past, group-by/apply operations have been over-eager
- # in converting dtypes to numeric, in the presence of datetime
- # columns. Various GH issues were filed, the reproductions
- # for which are here.
- # GH 15670
- df = DataFrame(
- {"Number": [1, 2], "Date": ["2017-03-02"] * 2, "Str": ["foo", "inf"]}
- )
- expected = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
- df.Date = pd.to_datetime(df.Date)
- result = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
- tm.assert_series_equal(result["Str"], expected["Str"])
- # GH 15421
- df = DataFrame(
- {"A": [10, 20, 30], "B": ["foo", "3", "4"], "T": [pd.Timestamp("12:31:22")] * 3}
- )
- def get_B(g):
- return g.iloc[0][["B"]]
- result = df.groupby("A").apply(get_B)["B"]
- expected = df.B
- expected.index = df.A
- tm.assert_series_equal(result, expected)
- # GH 14423
- def predictions(tool):
- out = Series(index=["p1", "p2", "useTime"], dtype=object)
- if "step1" in list(tool.State):
- out["p1"] = str(tool[tool.State == "step1"].Machine.values[0])
- if "step2" in list(tool.State):
- out["p2"] = str(tool[tool.State == "step2"].Machine.values[0])
- out["useTime"] = str(tool[tool.State == "step2"].oTime.values[0])
- return out
- df1 = DataFrame(
- {
- "Key": ["B", "B", "A", "A"],
- "State": ["step1", "step2", "step1", "step2"],
- "oTime": ["", "2016-09-19 05:24:33", "", "2016-09-19 23:59:04"],
- "Machine": ["23", "36L", "36R", "36R"],
- }
- )
- df2 = df1.copy()
- df2.oTime = pd.to_datetime(df2.oTime)
- expected = df1.groupby("Key").apply(predictions).p1
- result = df2.groupby("Key").apply(predictions).p1
- tm.assert_series_equal(expected, result)
- def test_apply_aggregating_timedelta_and_datetime():
- # Regression test for GH 15562
- # The following groupby caused ValueErrors and IndexErrors pre 0.20.0
- df = DataFrame(
- {
- "clientid": ["A", "B", "C"],
- "datetime": [np.datetime64("2017-02-01 00:00:00")] * 3,
- }
- )
- df["time_delta_zero"] = df.datetime - df.datetime
- result = df.groupby("clientid").apply(
- lambda ddf: Series(
- {"clientid_age": ddf.time_delta_zero.min(), "date": ddf.datetime.min()}
- )
- )
- expected = DataFrame(
- {
- "clientid": ["A", "B", "C"],
- "clientid_age": [np.timedelta64(0, "D")] * 3,
- "date": [np.datetime64("2017-02-01 00:00:00")] * 3,
- }
- ).set_index("clientid")
- tm.assert_frame_equal(result, expected)
- def test_apply_groupby_datetimeindex():
- # GH 26182
- # groupby apply failed on dataframe with DatetimeIndex
- data = [["A", 10], ["B", 20], ["B", 30], ["C", 40], ["C", 50]]
- df = DataFrame(
- data, columns=["Name", "Value"], index=pd.date_range("2020-09-01", "2020-09-05")
- )
- result = df.groupby("Name").sum()
- expected = DataFrame({"Name": ["A", "B", "C"], "Value": [10, 50, 90]})
- expected.set_index("Name", inplace=True)
- tm.assert_frame_equal(result, expected)
- def test_time_field_bug():
- # Test a fix for the following error related to GH issue 11324 When
- # non-key fields in a group-by dataframe contained time-based fields
- # that were not returned by the apply function, an exception would be
- # raised.
- df = DataFrame({"a": 1, "b": [datetime.now() for nn in range(10)]})
- def func_with_no_date(batch):
- return Series({"c": 2})
- def func_with_date(batch):
- return Series({"b": datetime(2015, 1, 1), "c": 2})
- dfg_no_conversion = df.groupby(by=["a"]).apply(func_with_no_date)
- dfg_no_conversion_expected = DataFrame({"c": 2}, index=[1])
- dfg_no_conversion_expected.index.name = "a"
- dfg_conversion = df.groupby(by=["a"]).apply(func_with_date)
- dfg_conversion_expected = DataFrame({"b": datetime(2015, 1, 1), "c": 2}, index=[1])
- dfg_conversion_expected.index.name = "a"
- tm.assert_frame_equal(dfg_no_conversion, dfg_no_conversion_expected)
- tm.assert_frame_equal(dfg_conversion, dfg_conversion_expected)
- def test_gb_apply_list_of_unequal_len_arrays():
- # GH1738
- df = DataFrame(
- {
- "group1": ["a", "a", "a", "b", "b", "b", "a", "a", "a", "b", "b", "b"],
- "group2": ["c", "c", "d", "d", "d", "e", "c", "c", "d", "d", "d", "e"],
- "weight": [1.1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 1, 2],
- "value": [7.1, 8, 9, 10, 11, 12, 8, 7, 6, 5, 4, 3],
- }
- )
- df = df.set_index(["group1", "group2"])
- df_grouped = df.groupby(level=["group1", "group2"], sort=True)
- def noddy(value, weight):
- out = np.array(value * weight).repeat(3)
- return out
- # the kernel function returns arrays of unequal length
- # pandas sniffs the first one, sees it's an array and not
- # a list, and assumed the rest are of equal length
- # and so tries a vstack
- # don't die
- df_grouped.apply(lambda x: noddy(x.value, x.weight))
- def test_groupby_apply_all_none():
- # Tests to make sure no errors if apply function returns all None
- # values. Issue 9684.
- test_df = DataFrame({"groups": [0, 0, 1, 1], "random_vars": [8, 7, 4, 5]})
- def test_func(x):
- pass
- result = test_df.groupby("groups").apply(test_func)
- expected = DataFrame()
- tm.assert_frame_equal(result, expected)
- def test_groupby_apply_none_first():
- # GH 12824. Tests if apply returns None first.
- test_df1 = DataFrame({"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]})
- test_df2 = DataFrame({"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]})
- def test_func(x):
- if x.shape[0] < 2:
- return None
- return x.iloc[[0, -1]]
- result1 = test_df1.groupby("groups").apply(test_func)
- result2 = test_df2.groupby("groups").apply(test_func)
- index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], names=["groups", None])
- index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], names=["groups", None])
- expected1 = DataFrame({"groups": [1, 1], "vars": [0, 2]}, index=index1)
- expected2 = DataFrame({"groups": [2, 2], "vars": [1, 3]}, index=index2)
- tm.assert_frame_equal(result1, expected1)
- tm.assert_frame_equal(result2, expected2)
- def test_groupby_apply_return_empty_chunk():
- # GH 22221: apply filter which returns some empty groups
- df = DataFrame({"value": [0, 1], "group": ["filled", "empty"]})
- groups = df.groupby("group")
- result = groups.apply(lambda group: group[group.value != 1]["value"])
- expected = Series(
- [0],
- name="value",
- index=MultiIndex.from_product(
- [["empty", "filled"], [0]], names=["group", None]
- ).drop("empty"),
- )
- tm.assert_series_equal(result, expected)
- def test_apply_with_mixed_types():
- # gh-20949
- df = DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]})
- g = df.groupby("A", group_keys=False)
- result = g.transform(lambda x: x / x.sum())
- expected = DataFrame({"B": [1 / 3.0, 2 / 3.0, 1], "C": [0.4, 0.6, 1.0]})
- tm.assert_frame_equal(result, expected)
- result = g.apply(lambda x: x / x.sum())
- tm.assert_frame_equal(result, expected)
- def test_func_returns_object():
- # GH 28652
- df = DataFrame({"a": [1, 2]}, index=Index([1, 2]))
- result = df.groupby("a").apply(lambda g: g.index)
- expected = Series([Index([1]), Index([2])], index=Index([1, 2], name="a"))
- tm.assert_series_equal(result, expected)
- @pytest.mark.parametrize(
- "group_column_dtlike",
- [datetime.today(), datetime.today().date(), datetime.today().time()],
- )
- def test_apply_datetime_issue(group_column_dtlike):
- # GH-28247
- # groupby-apply throws an error if one of the columns in the DataFrame
- # is a datetime object and the column labels are different from
- # standard int values in range(len(num_columns))
- df = DataFrame({"a": ["foo"], "b": [group_column_dtlike]})
- result = df.groupby("a").apply(lambda x: Series(["spam"], index=[42]))
- expected = DataFrame(
- ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
- )
- tm.assert_frame_equal(result, expected)
- def test_apply_series_return_dataframe_groups():
- # GH 10078
- tdf = DataFrame(
- {
- "day": {
- 0: pd.Timestamp("2015-02-24 00:00:00"),
- 1: pd.Timestamp("2015-02-24 00:00:00"),
- 2: pd.Timestamp("2015-02-24 00:00:00"),
- 3: pd.Timestamp("2015-02-24 00:00:00"),
- 4: pd.Timestamp("2015-02-24 00:00:00"),
- },
- "userAgent": {
- 0: "some UA string",
- 1: "some UA string",
- 2: "some UA string",
- 3: "another UA string",
- 4: "some UA string",
- },
- "userId": {
- 0: "17661101",
- 1: "17661101",
- 2: "17661101",
- 3: "17661101",
- 4: "17661101",
- },
- }
- )
- def most_common_values(df):
- return Series({c: s.value_counts().index[0] for c, s in df.items()})
- result = tdf.groupby("day").apply(most_common_values)["userId"]
- expected = Series(
- ["17661101"], index=pd.DatetimeIndex(["2015-02-24"], name="day"), name="userId"
- )
- tm.assert_series_equal(result, expected)
- @pytest.mark.parametrize("category", [False, True])
- def test_apply_multi_level_name(category):
- # https://github.com/pandas-dev/pandas/issues/31068
- b = [1, 2] * 5
- if category:
- b = pd.Categorical(b, categories=[1, 2, 3])
- expected_index = pd.CategoricalIndex([1, 2, 3], categories=[1, 2, 3], name="B")
- # GH#40669 - summing an empty frame gives float dtype
- expected_values = [20.0, 25.0, 0.0]
- else:
- expected_index = Index([1, 2], name="B")
- expected_values = [20, 25]
- expected = DataFrame(
- {"C": expected_values, "D": expected_values}, index=expected_index
- )
- df = DataFrame(
- {"A": np.arange(10), "B": b, "C": list(range(10)), "D": list(range(10))}
- ).set_index(["A", "B"])
- result = df.groupby("B").apply(lambda x: x.sum())
- tm.assert_frame_equal(result, expected)
- assert df.index.names == ["A", "B"]
- def test_groupby_apply_datetime_result_dtypes():
- # GH 14849
- data = DataFrame.from_records(
- [
- (pd.Timestamp(2016, 1, 1), "red", "dark", 1, "8"),
- (pd.Timestamp(2015, 1, 1), "green", "stormy", 2, "9"),
- (pd.Timestamp(2014, 1, 1), "blue", "bright", 3, "10"),
- (pd.Timestamp(2013, 1, 1), "blue", "calm", 4, "potato"),
- ],
- columns=["observation", "color", "mood", "intensity", "score"],
- )
- result = data.groupby("color").apply(lambda g: g.iloc[0]).dtypes
- expected = Series(
- [np.dtype("datetime64[ns]"), object, object, np.int64, object],
- index=["observation", "color", "mood", "intensity", "score"],
- )
- tm.assert_series_equal(result, expected)
- @pytest.mark.parametrize(
- "index",
- [
- pd.CategoricalIndex(list("abc")),
- pd.interval_range(0, 3),
- pd.period_range("2020", periods=3, freq="D"),
- MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
- ],
- )
- def test_apply_index_has_complex_internals(index):
- # GH 31248
- df = DataFrame({"group": [1, 1, 2], "value": [0, 1, 0]}, index=index)
- result = df.groupby("group", group_keys=False).apply(lambda x: x)
- tm.assert_frame_equal(result, df)
- @pytest.mark.parametrize(
- "function, expected_values",
- [
- (lambda x: x.index.to_list(), [[0, 1], [2, 3]]),
- (lambda x: set(x.index.to_list()), [{0, 1}, {2, 3}]),
- (lambda x: tuple(x.index.to_list()), [(0, 1), (2, 3)]),
- (
- lambda x: dict(enumerate(x.index.to_list())),
- [{0: 0, 1: 1}, {0: 2, 1: 3}],
- ),
- (
- lambda x: [{n: i} for (n, i) in enumerate(x.index.to_list())],
- [[{0: 0}, {1: 1}], [{0: 2}, {1: 3}]],
- ),
- ],
- )
- def test_apply_function_returns_non_pandas_non_scalar(function, expected_values):
- # GH 31441
- df = DataFrame(["A", "A", "B", "B"], columns=["groups"])
- result = df.groupby("groups").apply(function)
- expected = Series(expected_values, index=Index(["A", "B"], name="groups"))
- tm.assert_series_equal(result, expected)
- def test_apply_function_returns_numpy_array():
- # GH 31605
- def fct(group):
- return group["B"].values.flatten()
- df = DataFrame({"A": ["a", "a", "b", "none"], "B": [1, 2, 3, np.nan]})
- result = df.groupby("A").apply(fct)
- expected = Series(
- [[1.0, 2.0], [3.0], [np.nan]], index=Index(["a", "b", "none"], name="A")
- )
- tm.assert_series_equal(result, expected)
- @pytest.mark.parametrize("function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1])
- def test_apply_function_index_return(function):
- # GH: 22541
- df = DataFrame([1, 2, 2, 2, 1, 2, 3, 1, 3, 1], columns=["id"])
- result = df.groupby("id").apply(function)
- expected = Series(
- [Index([0, 4, 7, 9]), Index([1, 2, 3, 5]), Index([6, 8])],
- index=Index([1, 2, 3], name="id"),
- )
- tm.assert_series_equal(result, expected)
- def test_apply_function_with_indexing_return_column():
- # GH#7002, GH#41480, GH#49256
- df = DataFrame(
- {
- "foo1": ["one", "two", "two", "three", "one", "two"],
- "foo2": [1, 2, 4, 4, 5, 6],
- }
- )
- result = df.groupby("foo1", as_index=False).apply(lambda x: x.mean())
- expected = DataFrame(
- {
- "foo1": ["one", "three", "two"],
- "foo2": [3.0, 4.0, 4.0],
- }
- )
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize(
- "udf",
- [(lambda x: x.copy()), (lambda x: x.copy().rename(lambda y: y + 1))],
- )
- @pytest.mark.parametrize("group_keys", [True, False])
- def test_apply_result_type(group_keys, udf):
- # https://github.com/pandas-dev/pandas/issues/34809
- # We'd like to control whether the group keys end up in the index
- # regardless of whether the UDF happens to be a transform.
- df = DataFrame({"A": ["a", "b"], "B": [1, 2]})
- df_result = df.groupby("A", group_keys=group_keys).apply(udf)
- series_result = df.B.groupby(df.A, group_keys=group_keys).apply(udf)
- if group_keys:
- assert df_result.index.nlevels == 2
- assert series_result.index.nlevels == 2
- else:
- assert df_result.index.nlevels == 1
- assert series_result.index.nlevels == 1
- def test_result_order_group_keys_false():
- # GH 34998
- # apply result order should not depend on whether index is the same or just equal
- df = DataFrame({"A": [2, 1, 2], "B": [1, 2, 3]})
- result = df.groupby("A", group_keys=False).apply(lambda x: x)
- expected = df.groupby("A", group_keys=False).apply(lambda x: x.copy())
- tm.assert_frame_equal(result, expected)
- def test_apply_with_timezones_aware():
- # GH: 27212
- dates = ["2001-01-01"] * 2 + ["2001-01-02"] * 2 + ["2001-01-03"] * 2
- index_no_tz = pd.DatetimeIndex(dates)
- index_tz = pd.DatetimeIndex(dates, tz="UTC")
- df1 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_no_tz})
- df2 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_tz})
- result1 = df1.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
- result2 = df2.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
- tm.assert_frame_equal(result1, result2)
- def test_apply_is_unchanged_when_other_methods_are_called_first(reduction_func):
- # GH #34656
- # GH #34271
- df = DataFrame(
- {
- "a": [99, 99, 99, 88, 88, 88],
- "b": [1, 2, 3, 4, 5, 6],
- "c": [10, 20, 30, 40, 50, 60],
- }
- )
- expected = DataFrame(
- {"a": [264, 297], "b": [15, 6], "c": [150, 60]},
- index=Index([88, 99], name="a"),
- )
- # Check output when no other methods are called before .apply()
- grp = df.groupby(by="a")
- result = grp.apply(sum)
- tm.assert_frame_equal(result, expected)
- # Check output when another method is called before .apply()
- grp = df.groupby(by="a")
- args = get_groupby_method_args(reduction_func, df)
- _ = getattr(grp, reduction_func)(*args)
- result = grp.apply(sum)
- tm.assert_frame_equal(result, expected)
- def test_apply_with_date_in_multiindex_does_not_convert_to_timestamp():
- # GH 29617
- df = DataFrame(
- {
- "A": ["a", "a", "a", "b"],
- "B": [
- date(2020, 1, 10),
- date(2020, 1, 10),
- date(2020, 2, 10),
- date(2020, 2, 10),
- ],
- "C": [1, 2, 3, 4],
- },
- index=Index([100, 101, 102, 103], name="idx"),
- )
- grp = df.groupby(["A", "B"])
- result = grp.apply(lambda x: x.head(1))
- expected = df.iloc[[0, 2, 3]]
- expected = expected.reset_index()
- expected.index = MultiIndex.from_frame(expected[["A", "B", "idx"]])
- expected = expected.drop(columns="idx")
- tm.assert_frame_equal(result, expected)
- for val in result.index.levels[1]:
- assert type(val) is date
- def test_apply_by_cols_equals_apply_by_rows_transposed():
- # GH 16646
- # Operating on the columns, or transposing and operating on the rows
- # should give the same result. There was previously a bug where the
- # by_rows operation would work fine, but by_cols would throw a ValueError
- df = DataFrame(
- np.random.random([6, 4]),
- columns=MultiIndex.from_product([["A", "B"], [1, 2]]),
- )
- by_rows = df.T.groupby(axis=0, level=0).apply(
- lambda x: x.droplevel(axis=0, level=0)
- )
- by_cols = df.groupby(axis=1, level=0).apply(lambda x: x.droplevel(axis=1, level=0))
- tm.assert_frame_equal(by_cols, by_rows.T)
- tm.assert_frame_equal(by_cols, df)
- @pytest.mark.parametrize("dropna", [True, False])
- def test_apply_dropna_with_indexed_same(dropna):
- # GH 38227
- # GH#43205
- df = DataFrame(
- {
- "col": [1, 2, 3, 4, 5],
- "group": ["a", np.nan, np.nan, "b", "b"],
- },
- index=list("xxyxz"),
- )
- result = df.groupby("group", dropna=dropna, group_keys=False).apply(lambda x: x)
- expected = df.dropna() if dropna else df.iloc[[0, 3, 1, 2, 4]]
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize(
- "as_index, expected",
- [
- [
- False,
- DataFrame(
- [[1, 1, 1], [2, 2, 1]], columns=Index(["a", "b", None], dtype=object)
- ),
- ],
- [
- True,
- Series(
- [1, 1], index=MultiIndex.from_tuples([(1, 1), (2, 2)], names=["a", "b"])
- ),
- ],
- ],
- )
- def test_apply_as_index_constant_lambda(as_index, expected):
- # GH 13217
- df = DataFrame({"a": [1, 1, 2, 2], "b": [1, 1, 2, 2], "c": [1, 1, 1, 1]})
- result = df.groupby(["a", "b"], as_index=as_index).apply(lambda x: 1)
- tm.assert_equal(result, expected)
- def test_sort_index_groups():
- # GH 20420
- df = DataFrame(
- {"A": [1, 2, 3, 4, 5], "B": [6, 7, 8, 9, 0], "C": [1, 1, 1, 2, 2]},
- index=range(5),
- )
- result = df.groupby("C").apply(lambda x: x.A.sort_index())
- expected = Series(
- range(1, 6),
- index=MultiIndex.from_tuples(
- [(1, 0), (1, 1), (1, 2), (2, 3), (2, 4)], names=["C", None]
- ),
- name="A",
- )
- tm.assert_series_equal(result, expected)
- def test_positional_slice_groups_datetimelike():
- # GH 21651
- expected = DataFrame(
- {
- "date": pd.date_range("2010-01-01", freq="12H", periods=5),
- "vals": range(5),
- "let": list("abcde"),
- }
- )
- result = expected.groupby(
- [expected.let, expected.date.dt.date], group_keys=False
- ).apply(lambda x: x.iloc[0:])
- tm.assert_frame_equal(result, expected)
- def test_groupby_apply_shape_cache_safety():
- # GH#42702 this fails if we cache_readonly Block.shape
- df = DataFrame({"A": ["a", "a", "b"], "B": [1, 2, 3], "C": [4, 6, 5]})
- gb = df.groupby("A")
- result = gb[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min())
- expected = DataFrame(
- {"B": [1.0, 0.0], "C": [2.0, 0.0]}, index=Index(["a", "b"], name="A")
- )
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("dropna", [True, False])
- def test_apply_na(dropna):
- # GH#28984
- df = DataFrame(
- {"grp": [1, 1, 2, 2], "y": [1, 0, 2, 5], "z": [1, 2, np.nan, np.nan]}
- )
- dfgrp = df.groupby("grp", dropna=dropna)
- result = dfgrp.apply(lambda grp_df: grp_df.nlargest(1, "z"))
- expected = dfgrp.apply(lambda x: x.sort_values("z", ascending=False).head(1))
- tm.assert_frame_equal(result, expected)
- def test_apply_empty_string_nan_coerce_bug():
- # GH#24903
- result = (
- DataFrame(
- {
- "a": [1, 1, 2, 2],
- "b": ["", "", "", ""],
- "c": pd.to_datetime([1, 2, 3, 4], unit="s"),
- }
- )
- .groupby(["a", "b"])
- .apply(lambda df: df.iloc[-1])
- )
- expected = DataFrame(
- [[1, "", pd.to_datetime(2, unit="s")], [2, "", pd.to_datetime(4, unit="s")]],
- columns=["a", "b", "c"],
- index=MultiIndex.from_tuples([(1, ""), (2, "")], names=["a", "b"]),
- )
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("index_values", [[1, 2, 3], [1.0, 2.0, 3.0]])
- def test_apply_index_key_error_bug(index_values):
- # GH 44310
- result = DataFrame(
- {
- "a": ["aa", "a2", "a3"],
- "b": [1, 2, 3],
- },
- index=Index(index_values),
- )
- expected = DataFrame(
- {
- "b_mean": [2.0, 3.0, 1.0],
- },
- index=Index(["a2", "a3", "aa"], name="a"),
- )
- result = result.groupby("a").apply(
- lambda df: Series([df["b"].mean()], index=["b_mean"])
- )
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize(
- "arg,idx",
- [
- [
- [
- 1,
- 2,
- 3,
- ],
- [
- 0.1,
- 0.3,
- 0.2,
- ],
- ],
- [
- [
- 1,
- 2,
- 3,
- ],
- [
- 0.1,
- 0.2,
- 0.3,
- ],
- ],
- [
- [
- 1,
- 4,
- 3,
- ],
- [
- 0.1,
- 0.4,
- 0.2,
- ],
- ],
- ],
- )
- def test_apply_nonmonotonic_float_index(arg, idx):
- # GH 34455
- expected = DataFrame({"col": arg}, index=idx)
- result = expected.groupby("col", group_keys=False).apply(lambda x: x)
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("args, kwargs", [([True], {}), ([], {"numeric_only": True})])
- def test_apply_str_with_args(df, args, kwargs):
- # GH#46479
- gb = df.groupby("A")
- result = gb.apply("sum", *args, **kwargs)
- expected = gb.sum(numeric_only=True)
- tm.assert_frame_equal(result, expected)
- @pytest.mark.parametrize("name", ["some_name", None])
- def test_result_name_when_one_group(name):
- # GH 46369
- ser = Series([1, 2], name=name)
- result = ser.groupby(["a", "a"], group_keys=False).apply(lambda x: x)
- expected = Series([1, 2], name=name)
- tm.assert_series_equal(result, expected)
- @pytest.mark.parametrize(
- "method, op",
- [
- ("apply", lambda gb: gb.values[-1]),
- ("apply", lambda gb: gb["b"].iloc[0]),
- ("agg", "skew"),
- ("agg", "prod"),
- ("agg", "sum"),
- ],
- )
- def test_empty_df(method, op):
- # GH 47985
- empty_df = DataFrame({"a": [], "b": []})
- gb = empty_df.groupby("a", group_keys=True)
- group = getattr(gb, "b")
- result = getattr(group, method)(op)
- expected = Series(
- [], name="b", dtype="float64", index=Index([], dtype="float64", name="a")
- )
- tm.assert_series_equal(result, expected)
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