Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/pandas/tests/strings/conftest.py
2023-09-20 19:46:58 +02:00

176 lines
5.1 KiB
Python

import numpy as np
import pytest
from pandas import Series
from pandas.core.strings.accessor import StringMethods
_any_string_method = [
("cat", (), {"sep": ","}),
("cat", (Series(list("zyx")),), {"sep": ",", "join": "left"}),
("center", (10,), {}),
("contains", ("a",), {}),
("count", ("a",), {}),
("decode", ("UTF-8",), {}),
("encode", ("UTF-8",), {}),
("endswith", ("a",), {}),
("endswith", ("a",), {"na": True}),
("endswith", ("a",), {"na": False}),
("extract", ("([a-z]*)",), {"expand": False}),
("extract", ("([a-z]*)",), {"expand": True}),
("extractall", ("([a-z]*)",), {}),
("find", ("a",), {}),
("findall", ("a",), {}),
("get", (0,), {}),
# because "index" (and "rindex") fail intentionally
# if the string is not found, search only for empty string
("index", ("",), {}),
("join", (",",), {}),
("ljust", (10,), {}),
("match", ("a",), {}),
("fullmatch", ("a",), {}),
("normalize", ("NFC",), {}),
("pad", (10,), {}),
("partition", (" ",), {"expand": False}),
("partition", (" ",), {"expand": True}),
("repeat", (3,), {}),
("replace", ("a", "z"), {}),
("rfind", ("a",), {}),
("rindex", ("",), {}),
("rjust", (10,), {}),
("rpartition", (" ",), {"expand": False}),
("rpartition", (" ",), {"expand": True}),
("slice", (0, 1), {}),
("slice_replace", (0, 1, "z"), {}),
("split", (" ",), {"expand": False}),
("split", (" ",), {"expand": True}),
("startswith", ("a",), {}),
("startswith", ("a",), {"na": True}),
("startswith", ("a",), {"na": False}),
("removeprefix", ("a",), {}),
("removesuffix", ("a",), {}),
# translating unicode points of "a" to "d"
("translate", ({97: 100},), {}),
("wrap", (2,), {}),
("zfill", (10,), {}),
] + list(
zip(
[
# methods without positional arguments: zip with empty tuple and empty dict
"capitalize",
"cat",
"get_dummies",
"isalnum",
"isalpha",
"isdecimal",
"isdigit",
"islower",
"isnumeric",
"isspace",
"istitle",
"isupper",
"len",
"lower",
"lstrip",
"partition",
"rpartition",
"rsplit",
"rstrip",
"slice",
"slice_replace",
"split",
"strip",
"swapcase",
"title",
"upper",
"casefold",
],
[()] * 100,
[{}] * 100,
)
)
ids, _, _ = zip(*_any_string_method) # use method name as fixture-id
missing_methods = {f for f in dir(StringMethods) if not f.startswith("_")} - set(ids)
# test that the above list captures all methods of StringMethods
assert not missing_methods
@pytest.fixture(params=_any_string_method, ids=ids)
def any_string_method(request):
"""
Fixture for all public methods of `StringMethods`
This fixture returns a tuple of the method name and sample arguments
necessary to call the method.
Returns
-------
method_name : str
The name of the method in `StringMethods`
args : tuple
Sample values for the positional arguments
kwargs : dict
Sample values for the keyword arguments
Examples
--------
>>> def test_something(any_string_method):
... s = Series(['a', 'b', np.nan, 'd'])
...
... method_name, args, kwargs = any_string_method
... method = getattr(s.str, method_name)
... # will not raise
... method(*args, **kwargs)
"""
return request.param
# subset of the full set from pandas/conftest.py
_any_allowed_skipna_inferred_dtype = [
("string", ["a", np.nan, "c"]),
("bytes", [b"a", np.nan, b"c"]),
("empty", [np.nan, np.nan, np.nan]),
("empty", []),
("mixed-integer", ["a", np.nan, 2]),
]
ids, _ = zip(*_any_allowed_skipna_inferred_dtype) # use inferred type as id
@pytest.fixture(params=_any_allowed_skipna_inferred_dtype, ids=ids)
def any_allowed_skipna_inferred_dtype(request):
"""
Fixture for all (inferred) dtypes allowed in StringMethods.__init__
The covered (inferred) types are:
* 'string'
* 'empty'
* 'bytes'
* 'mixed'
* 'mixed-integer'
Returns
-------
inferred_dtype : str
The string for the inferred dtype from _libs.lib.infer_dtype
values : np.ndarray
An array of object dtype that will be inferred to have
`inferred_dtype`
Examples
--------
>>> from pandas._libs import lib
>>>
>>> def test_something(any_allowed_skipna_inferred_dtype):
... inferred_dtype, values = any_allowed_skipna_inferred_dtype
... # will pass
... assert lib.infer_dtype(values, skipna=True) == inferred_dtype
...
... # constructor for .str-accessor will also pass
... Series(values).str
"""
inferred_dtype, values = request.param
values = np.array(values, dtype=object) # object dtype to avoid casting
# correctness of inference tested in tests/dtypes/test_inference.py
return inferred_dtype, values