119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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)
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import pandas._testing as tm
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@pytest.fixture
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def m():
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return 5
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@pytest.fixture
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def n():
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return 100
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@pytest.fixture
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def cols():
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return ["jim", "joe", "jolie", "joline", "jolia"]
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@pytest.fixture
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def vals(n):
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vals = [
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np.random.default_rng(2).integers(0, 10, n),
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np.random.default_rng(2).choice(list("abcdefghij"), n),
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np.random.default_rng(2).choice(
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pd.date_range("20141009", periods=10).tolist(), n
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),
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np.random.default_rng(2).choice(list("ZYXWVUTSRQ"), n),
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np.random.default_rng(2).standard_normal(n),
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]
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vals = list(map(tuple, zip(*vals)))
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return vals
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@pytest.fixture
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def keys(n, m, vals):
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# bunch of keys for testing
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keys = [
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np.random.default_rng(2).integers(0, 11, m),
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np.random.default_rng(2).choice(list("abcdefghijk"), m),
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np.random.default_rng(2).choice(
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pd.date_range("20141009", periods=11).tolist(), m
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),
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np.random.default_rng(2).choice(list("ZYXWVUTSRQP"), m),
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]
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keys = list(map(tuple, zip(*keys)))
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keys += [t[:-1] for t in vals[:: n // m]]
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return keys
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# covers both unique index and non-unique index
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@pytest.fixture
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def df(vals, cols):
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return DataFrame(vals, columns=cols)
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@pytest.fixture
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def a(df):
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return pd.concat([df, df])
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@pytest.fixture
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def b(df, cols):
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return df.drop_duplicates(subset=cols[:-1])
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@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
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@pytest.mark.parametrize("lexsort_depth", list(range(5)))
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@pytest.mark.parametrize("frame_fixture", ["a", "b"])
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def test_multiindex_get_loc(request, lexsort_depth, keys, frame_fixture, cols):
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# GH7724, GH2646
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frame = request.getfixturevalue(frame_fixture)
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if lexsort_depth == 0:
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df = frame.copy(deep=False)
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else:
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df = frame.sort_values(by=cols[:lexsort_depth])
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mi = df.set_index(cols[:-1])
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assert not mi.index._lexsort_depth < lexsort_depth
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for key in keys:
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mask = np.ones(len(df), dtype=bool)
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# test for all partials of this key
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for i, k in enumerate(key):
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mask &= df.iloc[:, i] == k
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if not mask.any():
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assert key[: i + 1] not in mi.index
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continue
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assert key[: i + 1] in mi.index
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right = df[mask].copy(deep=False)
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if i + 1 != len(key): # partial key
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return_value = right.drop(cols[: i + 1], axis=1, inplace=True)
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assert return_value is None
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return_value = right.set_index(cols[i + 1 : -1], inplace=True)
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assert return_value is None
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tm.assert_frame_equal(mi.loc[key[: i + 1]], right)
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else: # full key
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return_value = right.set_index(cols[:-1], inplace=True)
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assert return_value is None
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if len(right) == 1: # single hit
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right = Series(
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right["jolia"].values, name=right.index[0], index=["jolia"]
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)
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tm.assert_series_equal(mi.loc[key[: i + 1]], right)
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else: # multi hit
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tm.assert_frame_equal(mi.loc[key[: i + 1]], right)
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