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94 lines
3.3 KiB
94 lines
3.3 KiB
import numpy as np
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import pytest
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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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class TestLookup:
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def test_lookup_float(self, float_frame):
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df = float_frame
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rows = list(df.index) * len(df.columns)
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cols = list(df.columns) * len(df.index)
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with tm.assert_produces_warning(FutureWarning):
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result = df.lookup(rows, cols)
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expected = np.array([df.loc[r, c] for r, c in zip(rows, cols)])
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tm.assert_numpy_array_equal(result, expected)
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def test_lookup_mixed(self, float_string_frame):
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df = float_string_frame
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rows = list(df.index) * len(df.columns)
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cols = list(df.columns) * len(df.index)
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with tm.assert_produces_warning(FutureWarning):
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result = df.lookup(rows, cols)
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expected = np.array(
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[df.loc[r, c] for r, c in zip(rows, cols)], dtype=np.object_
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)
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tm.assert_almost_equal(result, expected)
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def test_lookup_bool(self):
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df = DataFrame(
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{
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"label": ["a", "b", "a", "c"],
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"mask_a": [True, True, False, True],
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"mask_b": [True, False, False, False],
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"mask_c": [False, True, False, True],
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}
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)
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with tm.assert_produces_warning(FutureWarning):
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df["mask"] = df.lookup(df.index, "mask_" + df["label"])
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exp_mask = np.array(
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[df.loc[r, c] for r, c in zip(df.index, "mask_" + df["label"])]
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)
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tm.assert_series_equal(df["mask"], Series(exp_mask, name="mask"))
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assert df["mask"].dtype == np.bool_
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def test_lookup_raises(self, float_frame):
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with pytest.raises(KeyError, match="'One or more row labels was not found'"):
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with tm.assert_produces_warning(FutureWarning):
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float_frame.lookup(["xyz"], ["A"])
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with pytest.raises(KeyError, match="'One or more column labels was not found'"):
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with tm.assert_produces_warning(FutureWarning):
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float_frame.lookup([float_frame.index[0]], ["xyz"])
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with pytest.raises(ValueError, match="same size"):
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with tm.assert_produces_warning(FutureWarning):
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float_frame.lookup(["a", "b", "c"], ["a"])
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def test_lookup_requires_unique_axes(self):
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# GH#33041 raise with a helpful error message
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df = DataFrame(np.random.randn(6).reshape(3, 2), columns=["A", "A"])
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rows = [0, 1]
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cols = ["A", "A"]
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# homogeneous-dtype case
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with pytest.raises(ValueError, match="requires unique index and columns"):
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with tm.assert_produces_warning(FutureWarning):
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df.lookup(rows, cols)
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with pytest.raises(ValueError, match="requires unique index and columns"):
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with tm.assert_produces_warning(FutureWarning):
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df.T.lookup(cols, rows)
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# heterogeneous dtype
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df["B"] = 0
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with pytest.raises(ValueError, match="requires unique index and columns"):
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with tm.assert_produces_warning(FutureWarning):
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df.lookup(rows, cols)
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def test_lookup_deprecated():
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# GH#18262
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df = DataFrame(
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{"col": ["A", "A", "B", "B"], "A": [80, 23, np.nan, 22], "B": [80, 55, 76, 67]}
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)
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with tm.assert_produces_warning(FutureWarning):
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df.lookup(df.index, df["col"])
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