You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
133 lines
4.3 KiB
133 lines
4.3 KiB
import numpy as np
|
|
import pytest
|
|
|
|
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
|
|
|
|
|
|
class BaseInterfaceTests:
|
|
"""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 or type(na_value_obj) == type(na_value):
|
|
# type check for e.g. two instances of Decimal("NAN")
|
|
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)
|
|
if hasattr(result._mgr, "blocks"):
|
|
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()
|
|
|
|
if data.dtype._is_immutable:
|
|
pytest.skip("test_copy assumes mutability")
|
|
|
|
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)
|
|
|
|
if data.dtype._is_immutable:
|
|
pytest.skip("test_view assumes mutability")
|
|
|
|
result[1] = result[0]
|
|
assert data[1] == data[0]
|
|
|
|
# check specifically that the `dtype` kwarg is accepted
|
|
data.view(dtype=None)
|
|
|
|
def test_tolist(self, data):
|
|
result = data.tolist()
|
|
expected = list(data)
|
|
assert isinstance(result, list)
|
|
assert result == expected
|
|
|