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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
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