mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-14 11:08:15 +01:00
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from .base import BaseExtensionTests
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class BaseGroupbyTests(BaseExtensionTests):
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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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{"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
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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.grouper, df.A.values)
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tm.assert_extension_array_equal(gr2.grouper, 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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result = df.groupby("B", as_index=as_index).A.mean()
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_, index = pd.factorize(data_for_grouping, sort=True)
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index = pd.Index(index, name="B")
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expected = pd.Series([3, 1, 4], index=index, name="A")
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if as_index:
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self.assert_series_equal(result, expected)
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else:
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expected = expected.reset_index()
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self.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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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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expected = pd.Series([1, 3, 4], index=index, name="A")
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self.assert_series_equal(result, expected)
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def test_groupby_extension_transform(self, data_for_grouping):
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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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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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self.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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df.groupby("B").apply(groupby_apply_op)
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df.groupby("B").A.apply(groupby_apply_op)
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df.groupby("A").apply(groupby_apply_op)
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df.groupby("A").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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self.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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result = df.groupby("A").sum().columns
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if data_for_grouping.dtype._is_numeric:
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expected = pd.Index(["B", "C"])
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else:
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expected = pd.Index(["C"])
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tm.assert_index_equal(result, expected)
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