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- import numpy as np
- import pandas.util._test_decorators as td
- from pandas import (
- DataFrame,
- Timestamp,
- )
- import pandas._testing as tm
- class TestToNumpy:
- def test_to_numpy(self):
- df = DataFrame({"A": [1, 2], "B": [3, 4.5]})
- expected = np.array([[1, 3], [2, 4.5]])
- result = df.to_numpy()
- tm.assert_numpy_array_equal(result, expected)
- def test_to_numpy_dtype(self):
- df = DataFrame({"A": [1, 2], "B": [3, 4.5]})
- expected = np.array([[1, 3], [2, 4]], dtype="int64")
- result = df.to_numpy(dtype="int64")
- tm.assert_numpy_array_equal(result, expected)
- @td.skip_array_manager_invalid_test
- def test_to_numpy_copy(self, using_copy_on_write):
- arr = np.random.randn(4, 3)
- df = DataFrame(arr)
- if using_copy_on_write:
- assert df.values.base is not arr
- assert df.to_numpy(copy=False).base is df.values.base
- else:
- assert df.values.base is arr
- assert df.to_numpy(copy=False).base is arr
- assert df.to_numpy(copy=True).base is not arr
- def test_to_numpy_mixed_dtype_to_str(self):
- # https://github.com/pandas-dev/pandas/issues/35455
- df = DataFrame([[Timestamp("2020-01-01 00:00:00"), 100.0]])
- result = df.to_numpy(dtype=str)
- expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str)
- tm.assert_numpy_array_equal(result, expected)
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