175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
import re
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import pytest
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from pandas.core.dtypes.common import (
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is_bool_dtype,
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is_numeric_dtype,
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is_object_dtype,
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is_string_dtype,
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)
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import pandas as pd
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import pandas._testing as tm
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@pytest.mark.filterwarnings(
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"ignore:The default of observed=False is deprecated:FutureWarning"
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)
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class BaseGroupbyTests:
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"""Groupby-specific tests."""
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def test_grouping_grouper(self, data_for_grouping):
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df = pd.DataFrame(
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{
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"A": pd.Series(
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["B", "B", None, None, "A", "A", "B", "C"], dtype=object
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),
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"B": data_for_grouping,
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}
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)
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gr1 = df.groupby("A")._grouper.groupings[0]
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gr2 = df.groupby("B")._grouper.groupings[0]
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tm.assert_numpy_array_equal(gr1.grouping_vector, df.A.values)
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tm.assert_extension_array_equal(gr2.grouping_vector, data_for_grouping)
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@pytest.mark.parametrize("as_index", [True, False])
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def test_groupby_extension_agg(self, as_index, data_for_grouping):
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df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
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is_bool = data_for_grouping.dtype._is_boolean
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if is_bool:
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# only 2 unique values, and the final entry has c==b
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# (see data_for_grouping docstring)
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df = df.iloc[:-1]
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result = df.groupby("B", as_index=as_index).A.mean()
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_, uniques = pd.factorize(data_for_grouping, sort=True)
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exp_vals = [3.0, 1.0, 4.0]
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if is_bool:
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exp_vals = exp_vals[:-1]
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if as_index:
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index = pd.Index(uniques, name="B")
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expected = pd.Series(exp_vals, index=index, name="A")
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tm.assert_series_equal(result, expected)
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else:
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expected = pd.DataFrame({"B": uniques, "A": exp_vals})
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tm.assert_frame_equal(result, expected)
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def test_groupby_agg_extension(self, data_for_grouping):
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# GH#38980 groupby agg on extension type fails for non-numeric types
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df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
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expected = df.iloc[[0, 2, 4, 7]]
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expected = expected.set_index("A")
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result = df.groupby("A").agg({"B": "first"})
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tm.assert_frame_equal(result, expected)
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result = df.groupby("A").agg("first")
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tm.assert_frame_equal(result, expected)
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result = df.groupby("A").first()
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tm.assert_frame_equal(result, expected)
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def test_groupby_extension_no_sort(self, data_for_grouping):
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df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
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is_bool = data_for_grouping.dtype._is_boolean
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if is_bool:
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# only 2 unique values, and the final entry has c==b
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# (see data_for_grouping docstring)
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df = df.iloc[:-1]
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result = df.groupby("B", sort=False).A.mean()
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_, index = pd.factorize(data_for_grouping, sort=False)
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index = pd.Index(index, name="B")
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exp_vals = [1.0, 3.0, 4.0]
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if is_bool:
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exp_vals = exp_vals[:-1]
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expected = pd.Series(exp_vals, index=index, name="A")
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tm.assert_series_equal(result, expected)
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def test_groupby_extension_transform(self, data_for_grouping):
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is_bool = data_for_grouping.dtype._is_boolean
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valid = data_for_grouping[~data_for_grouping.isna()]
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df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
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is_bool = data_for_grouping.dtype._is_boolean
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if is_bool:
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# only 2 unique values, and the final entry has c==b
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# (see data_for_grouping docstring)
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df = df.iloc[:-1]
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result = df.groupby("B").A.transform(len)
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expected = pd.Series([3, 3, 2, 2, 3, 1], name="A")
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if is_bool:
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expected = expected[:-1]
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tm.assert_series_equal(result, expected)
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def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
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df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
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msg = "DataFrameGroupBy.apply operated on the grouping columns"
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with tm.assert_produces_warning(DeprecationWarning, match=msg):
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df.groupby("B", group_keys=False, observed=False).apply(groupby_apply_op)
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df.groupby("B", group_keys=False, observed=False).A.apply(groupby_apply_op)
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msg = "DataFrameGroupBy.apply operated on the grouping columns"
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with tm.assert_produces_warning(DeprecationWarning, match=msg):
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df.groupby("A", group_keys=False, observed=False).apply(groupby_apply_op)
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df.groupby("A", group_keys=False, observed=False).B.apply(groupby_apply_op)
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def test_groupby_apply_identity(self, data_for_grouping):
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df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
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result = df.groupby("A").B.apply(lambda x: x.array)
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expected = pd.Series(
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[
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df.B.iloc[[0, 1, 6]].array,
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df.B.iloc[[2, 3]].array,
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df.B.iloc[[4, 5]].array,
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df.B.iloc[[7]].array,
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],
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index=pd.Index([1, 2, 3, 4], name="A"),
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name="B",
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)
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tm.assert_series_equal(result, expected)
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def test_in_numeric_groupby(self, data_for_grouping):
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df = pd.DataFrame(
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{
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"A": [1, 1, 2, 2, 3, 3, 1, 4],
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"B": data_for_grouping,
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"C": [1, 1, 1, 1, 1, 1, 1, 1],
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}
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)
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dtype = data_for_grouping.dtype
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if (
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is_numeric_dtype(dtype)
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or is_bool_dtype(dtype)
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or dtype.name == "decimal"
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or is_string_dtype(dtype)
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or is_object_dtype(dtype)
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or dtype.kind == "m" # in particular duration[*][pyarrow]
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):
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expected = pd.Index(["B", "C"])
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result = df.groupby("A").sum().columns
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else:
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expected = pd.Index(["C"])
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msg = "|".join(
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[
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# period/datetime
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"does not support sum operations",
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# all others
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re.escape(f"agg function failed [how->sum,dtype->{dtype}"),
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]
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)
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with pytest.raises(TypeError, match=msg):
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df.groupby("A").sum()
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result = df.groupby("A").sum(numeric_only=True).columns
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tm.assert_index_equal(result, expected)
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