craftbeerpi4-pione/venv/lib/python3.8/site-packages/pandas/tests/extension/base/interface.py

import numpy as np

from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.dtypes.dtypes import ExtensionDtype

import pandas as pd
import pandas._testing as tm

from .base import BaseExtensionTests


class BaseInterfaceTests(BaseExtensionTests):
    """Tests that the basic interface is satisfied."""

    # ------------------------------------------------------------------------
    # Interface
    # ------------------------------------------------------------------------

    def test_len(self, data):
        assert len(data) == 100

    def test_size(self, data):
        assert data.size == 100

    def test_ndim(self, data):
        assert data.ndim == 1

    def test_can_hold_na_valid(self, data):
        # GH-20761
        assert data._can_hold_na is True

    def test_contains(self, data, data_missing):
        # GH-37867
        # Tests for membership checks. Membership checks for nan-likes is tricky and
        # the settled on rule is: `nan_like in arr` is True if nan_like is
        # arr.dtype.na_value and arr.isna().any() is True. Else the check returns False.

        na_value = data.dtype.na_value
        # ensure data without missing values
        data = data[~data.isna()]

        # first elements are non-missing
        assert data[0] in data
        assert data_missing[0] in data_missing

        # check the presence of na_value
        assert na_value in data_missing
        assert na_value not in data

        # the data can never contain other nan-likes than na_value
        for na_value_obj in tm.NULL_OBJECTS:
            if na_value_obj is na_value:
                continue
            assert na_value_obj not in data
            assert na_value_obj not in data_missing

    def test_memory_usage(self, data):
        s = pd.Series(data)
        result = s.memory_usage(index=False)
        assert result == s.nbytes

    def test_array_interface(self, data):
        result = np.array(data)
        assert result[0] == data[0]

        result = np.array(data, dtype=object)
        expected = np.array(list(data), dtype=object)
        tm.assert_numpy_array_equal(result, expected)

    def test_is_extension_array_dtype(self, data):
        assert is_extension_array_dtype(data)
        assert is_extension_array_dtype(data.dtype)
        assert is_extension_array_dtype(pd.Series(data))
        assert isinstance(data.dtype, ExtensionDtype)

    def test_no_values_attribute(self, data):
        # GH-20735: EA's with .values attribute give problems with internal
        # code, disallowing this for now until solved
        assert not hasattr(data, "values")
        assert not hasattr(data, "_values")

    def test_is_numeric_honored(self, data):
        result = pd.Series(data)
        assert result._mgr.blocks[0].is_numeric is data.dtype._is_numeric

    def test_isna_extension_array(self, data_missing):
        # If your `isna` returns an ExtensionArray, you must also implement
        # _reduce. At the *very* least, you must implement any and all
        na = data_missing.isna()
        if is_extension_array_dtype(na):
            assert na._reduce("any")
            assert na.any()

            assert not na._reduce("all")
            assert not na.all()

            assert na.dtype._is_boolean

    def test_copy(self, data):
        # GH#27083 removing deep keyword from EA.copy
        assert data[0] != data[1]
        result = data.copy()

        data[1] = data[0]
        assert result[1] != result[0]

    def test_view(self, data):
        # view with no dtype should return a shallow copy, *not* the same
        #  object
        assert data[1] != data[0]

        result = data.view()
        assert result is not data
        assert type(result) == type(data)

        result[1] = result[0]
        assert data[1] == data[0]

        # check specifically that the `dtype` kwarg is accepted
        data.view(dtype=None)