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| import numpy as np
| import pytest
|
| import pandas as pd
| from pandas.core.arrays.floating import (
| Float32Dtype,
| Float64Dtype,
| )
|
|
| def test_dtypes(dtype):
| # smoke tests on auto dtype construction
|
| np.dtype(dtype.type).kind == "f"
| assert dtype.name is not None
|
|
| @pytest.mark.parametrize(
| "dtype, expected",
| [(Float32Dtype(), "Float32Dtype()"), (Float64Dtype(), "Float64Dtype()")],
| )
| def test_repr_dtype(dtype, expected):
| assert repr(dtype) == expected
|
|
| def test_repr_array():
| result = repr(pd.array([1.0, None, 3.0]))
| expected = "<FloatingArray>\n[1.0, <NA>, 3.0]\nLength: 3, dtype: Float64"
| assert result == expected
|
|
| def test_repr_array_long():
| data = pd.array([1.0, 2.0, None] * 1000)
| expected = """<FloatingArray>
| [ 1.0, 2.0, <NA>, 1.0, 2.0, <NA>, 1.0, 2.0, <NA>, 1.0,
| ...
| <NA>, 1.0, 2.0, <NA>, 1.0, 2.0, <NA>, 1.0, 2.0, <NA>]
| Length: 3000, dtype: Float64"""
| result = repr(data)
| assert result == expected
|
|
| def test_frame_repr(data_missing):
| df = pd.DataFrame({"A": data_missing})
| result = repr(df)
| expected = " A\n0 <NA>\n1 0.1"
| assert result == expected
|
|
