50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import numpy as np
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import pandas.util._test_decorators as td
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from pandas import (
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DataFrame,
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Timestamp,
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)
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import pandas._testing as tm
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class TestToNumpy:
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def test_to_numpy(self):
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df = DataFrame({"A": [1, 2], "B": [3, 4.5]})
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expected = np.array([[1, 3], [2, 4.5]])
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result = df.to_numpy()
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tm.assert_numpy_array_equal(result, expected)
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def test_to_numpy_dtype(self):
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df = DataFrame({"A": [1, 2], "B": [3, 4.5]})
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expected = np.array([[1, 3], [2, 4]], dtype="int64")
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result = df.to_numpy(dtype="int64")
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tm.assert_numpy_array_equal(result, expected)
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@td.skip_array_manager_invalid_test
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def test_to_numpy_copy(self, using_copy_on_write):
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arr = np.random.default_rng(2).standard_normal((4, 3))
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df = DataFrame(arr)
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if using_copy_on_write:
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assert df.values.base is not arr
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assert df.to_numpy(copy=False).base is df.values.base
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else:
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assert df.values.base is arr
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assert df.to_numpy(copy=False).base is arr
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assert df.to_numpy(copy=True).base is not arr
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# we still don't want a copy when na_value=np.nan is passed,
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# and that can be respected because we are already numpy-float
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if using_copy_on_write:
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assert df.to_numpy(copy=False).base is df.values.base
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else:
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assert df.to_numpy(copy=False, na_value=np.nan).base is arr
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def test_to_numpy_mixed_dtype_to_str(self):
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# https://github.com/pandas-dev/pandas/issues/35455
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df = DataFrame([[Timestamp("2020-01-01 00:00:00"), 100.0]])
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result = df.to_numpy(dtype=str)
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expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str)
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tm.assert_numpy_array_equal(result, expected)
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