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86 lines
2.8 KiB
86 lines
2.8 KiB
import operator
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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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Index,
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Series,
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)
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import pandas._testing as tm
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class TestMatMul:
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def test_matmul(self):
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# matmul test is for GH#10259
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a = DataFrame(
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np.random.randn(3, 4), index=["a", "b", "c"], columns=["p", "q", "r", "s"]
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)
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b = DataFrame(
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np.random.randn(4, 2), index=["p", "q", "r", "s"], columns=["one", "two"]
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)
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# DataFrame @ DataFrame
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result = operator.matmul(a, b)
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expected = DataFrame(
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np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
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)
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tm.assert_frame_equal(result, expected)
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# DataFrame @ Series
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result = operator.matmul(a, b.one)
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expected = Series(np.dot(a.values, b.one.values), index=["a", "b", "c"])
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tm.assert_series_equal(result, expected)
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# np.array @ DataFrame
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result = operator.matmul(a.values, b)
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assert isinstance(result, DataFrame)
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assert result.columns.equals(b.columns)
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assert result.index.equals(Index(range(3)))
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expected = np.dot(a.values, b.values)
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tm.assert_almost_equal(result.values, expected)
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# nested list @ DataFrame (__rmatmul__)
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result = operator.matmul(a.values.tolist(), b)
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expected = DataFrame(
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np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
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)
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tm.assert_almost_equal(result.values, expected.values)
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# mixed dtype DataFrame @ DataFrame
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a["q"] = a.q.round().astype(int)
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result = operator.matmul(a, b)
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expected = DataFrame(
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np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
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)
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tm.assert_frame_equal(result, expected)
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# different dtypes DataFrame @ DataFrame
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a = a.astype(int)
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result = operator.matmul(a, b)
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expected = DataFrame(
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np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"]
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)
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tm.assert_frame_equal(result, expected)
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# unaligned
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df = DataFrame(np.random.randn(3, 4), index=[1, 2, 3], columns=range(4))
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df2 = DataFrame(np.random.randn(5, 3), index=range(5), columns=[1, 2, 3])
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with pytest.raises(ValueError, match="aligned"):
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operator.matmul(df, df2)
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def test_matmul_message_shapes(self):
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# GH#21581 exception message should reflect original shapes,
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# not transposed shapes
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a = np.random.rand(10, 4)
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b = np.random.rand(5, 3)
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df = DataFrame(b)
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msg = r"shapes \(10, 4\) and \(5, 3\) not aligned"
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with pytest.raises(ValueError, match=msg):
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a @ df
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with pytest.raises(ValueError, match=msg):
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a.tolist() @ df
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