import numpy as np
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import pytest
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import pandas as pd
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from pandas.api.types import (
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infer_dtype,
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is_object_dtype,
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is_string_dtype,
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)
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from pandas.tests.extension.base.base import BaseExtensionTests
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class BaseDtypeTests(BaseExtensionTests):
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"""Base class for ExtensionDtype classes"""
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def test_name(self, dtype):
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assert isinstance(dtype.name, str)
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def test_kind(self, dtype):
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valid = set("biufcmMOSUV")
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assert dtype.kind in valid
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def test_is_dtype_from_name(self, dtype):
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result = type(dtype).is_dtype(dtype.name)
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assert result is True
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def test_is_dtype_unboxes_dtype(self, data, dtype):
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assert dtype.is_dtype(data) is True
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def test_is_dtype_from_self(self, dtype):
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result = type(dtype).is_dtype(dtype)
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assert result is True
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def test_is_dtype_other_input(self, dtype):
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assert dtype.is_dtype([1, 2, 3]) is False
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def test_is_not_string_type(self, dtype):
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assert not is_string_dtype(dtype)
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def test_is_not_object_type(self, dtype):
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assert not is_object_dtype(dtype)
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def test_eq_with_str(self, dtype):
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assert dtype == dtype.name
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assert dtype != dtype.name + "-suffix"
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def test_eq_with_numpy_object(self, dtype):
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assert dtype != np.dtype("object")
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def test_eq_with_self(self, dtype):
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assert dtype == dtype
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assert dtype != object()
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def test_array_type(self, data, dtype):
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assert dtype.construct_array_type() is type(data)
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def test_check_dtype(self, data):
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dtype = data.dtype
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# check equivalency for using .dtypes
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df = pd.DataFrame(
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{"A": pd.Series(data, dtype=dtype), "B": data, "C": "foo", "D": 1}
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)
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result = df.dtypes == str(dtype)
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try:
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new_numpy_behavior = np.dtype("int64") != "Int64"
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except TypeError:
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# numpy<=1.20.3 this comparison could raise or in some cases
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# come back True
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new_numpy_behavior = True
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assert new_numpy_behavior
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expected = pd.Series([True, True, False, False], index=list("ABCD"))
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self.assert_series_equal(result, expected)
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expected = pd.Series([True, True, False, False], index=list("ABCD"))
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result = df.dtypes.apply(str) == str(dtype)
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self.assert_series_equal(result, expected)
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def test_hashable(self, dtype):
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hash(dtype) # no error
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def test_str(self, dtype):
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assert str(dtype) == dtype.name
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def test_eq(self, dtype):
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assert dtype == dtype.name
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assert dtype != "anonther_type"
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def test_construct_from_string_own_name(self, dtype):
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result = dtype.construct_from_string(dtype.name)
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assert type(result) is type(dtype)
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# check OK as classmethod
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result = type(dtype).construct_from_string(dtype.name)
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assert type(result) is type(dtype)
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def test_construct_from_string_another_type_raises(self, dtype):
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msg = f"Cannot construct a '{type(dtype).__name__}' from 'another_type'"
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with pytest.raises(TypeError, match=msg):
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type(dtype).construct_from_string("another_type")
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def test_construct_from_string_wrong_type_raises(self, dtype):
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with pytest.raises(
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TypeError,
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match="'construct_from_string' expects a string, got <class 'int'>",
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):
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type(dtype).construct_from_string(0)
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def test_get_common_dtype(self, dtype):
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# in practice we will not typically call this with a 1-length list
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# (we shortcut to just use that dtype as the common dtype), but
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# still testing as good practice to have this working (and it is the
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# only case we can test in general)
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assert dtype._get_common_dtype([dtype]) == dtype
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@pytest.mark.parametrize("skipna", [True, False])
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def test_infer_dtype(self, data, data_missing, skipna):
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# only testing that this works without raising an error
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res = infer_dtype(data, skipna=skipna)
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assert isinstance(res, str)
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res = infer_dtype(data_missing, skipna=skipna)
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assert isinstance(res, str)
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