86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
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import numpy as np
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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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@pytest.fixture(params=[["inner"], ["inner", "outer"]])
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def frame(request):
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levels = request.param
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df = pd.DataFrame(
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{
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"outer": ["a", "a", "a", "b", "b", "b"],
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"inner": [1, 2, 3, 1, 2, 3],
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"A": np.arange(6),
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"B": ["one", "one", "two", "two", "one", "one"],
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}
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)
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if levels:
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df = df.set_index(levels)
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return df
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@pytest.fixture()
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def series():
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df = pd.DataFrame(
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{
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"outer": ["a", "a", "a", "b", "b", "b"],
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"inner": [1, 2, 3, 1, 2, 3],
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"A": np.arange(6),
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"B": ["one", "one", "two", "two", "one", "one"],
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}
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)
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s = df.set_index(["outer", "inner", "B"])["A"]
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return s
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@pytest.mark.parametrize(
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"key_strs,groupers",
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[
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("inner", pd.Grouper(level="inner")), # Index name
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(["inner"], [pd.Grouper(level="inner")]), # List of index name
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(["B", "inner"], ["B", pd.Grouper(level="inner")]), # Column and index
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(["inner", "B"], [pd.Grouper(level="inner"), "B"]), # Index and column
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],
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)
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def test_grouper_index_level_as_string(frame, key_strs, groupers):
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if "B" not in key_strs or "outer" in frame.columns:
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result = frame.groupby(key_strs).mean(numeric_only=True)
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expected = frame.groupby(groupers).mean(numeric_only=True)
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else:
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result = frame.groupby(key_strs).mean()
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expected = frame.groupby(groupers).mean()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"levels",
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[
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"inner",
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"outer",
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"B",
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["inner"],
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["outer"],
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["B"],
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["inner", "outer"],
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["outer", "inner"],
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["inner", "outer", "B"],
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["B", "outer", "inner"],
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],
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)
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def test_grouper_index_level_as_string_series(series, levels):
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# Compute expected result
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if isinstance(levels, list):
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groupers = [pd.Grouper(level=lv) for lv in levels]
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else:
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groupers = pd.Grouper(level=levels)
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expected = series.groupby(groupers).mean()
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# Compute and check result
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result = series.groupby(levels).mean()
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tm.assert_series_equal(result, expected)
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