Traktor/myenv/Lib/site-packages/pandas/tests/strings/test_api.py
2024-05-26 05:12:46 +02:00

199 lines
6.2 KiB
Python

import numpy as np
import pytest
from pandas import (
CategoricalDtype,
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
option_context,
)
from pandas.core.strings.accessor import StringMethods
# 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
def test_api(any_string_dtype):
# GH 6106, GH 9322
assert Series.str is StringMethods
assert isinstance(Series([""], dtype=any_string_dtype).str, StringMethods)
def test_api_mi_raises():
# GH 23679
mi = MultiIndex.from_arrays([["a", "b", "c"]])
msg = "Can only use .str accessor with Index, not MultiIndex"
with pytest.raises(AttributeError, match=msg):
mi.str
assert not hasattr(mi, "str")
@pytest.mark.parametrize("dtype", [object, "category"])
def test_api_per_dtype(index_or_series, dtype, any_skipna_inferred_dtype):
# one instance of parametrized fixture
box = index_or_series
inferred_dtype, values = any_skipna_inferred_dtype
t = box(values, dtype=dtype) # explicit dtype to avoid casting
types_passing_constructor = [
"string",
"unicode",
"empty",
"bytes",
"mixed",
"mixed-integer",
]
if inferred_dtype in types_passing_constructor:
# GH 6106
assert isinstance(t.str, StringMethods)
else:
# GH 9184, GH 23011, GH 23163
msg = "Can only use .str accessor with string values.*"
with pytest.raises(AttributeError, match=msg):
t.str
assert not hasattr(t, "str")
@pytest.mark.parametrize("dtype", [object, "category"])
def test_api_per_method(
index_or_series,
dtype,
any_allowed_skipna_inferred_dtype,
any_string_method,
request,
):
# this test does not check correctness of the different methods,
# just that the methods work on the specified (inferred) dtypes,
# and raise on all others
box = index_or_series
# one instance of each parametrized fixture
inferred_dtype, values = any_allowed_skipna_inferred_dtype
method_name, args, kwargs = any_string_method
reason = None
if box is Index and values.size == 0:
if method_name in ["partition", "rpartition"] and kwargs.get("expand", True):
raises = TypeError
reason = "Method cannot deal with empty Index"
elif method_name == "split" and kwargs.get("expand", None):
raises = TypeError
reason = "Split fails on empty Series when expand=True"
elif method_name == "get_dummies":
raises = ValueError
reason = "Need to fortify get_dummies corner cases"
elif (
box is Index
and inferred_dtype == "empty"
and dtype == object
and method_name == "get_dummies"
):
raises = ValueError
reason = "Need to fortify get_dummies corner cases"
if reason is not None:
mark = pytest.mark.xfail(raises=raises, reason=reason)
request.applymarker(mark)
t = box(values, dtype=dtype) # explicit dtype to avoid casting
method = getattr(t.str, method_name)
bytes_allowed = method_name in ["decode", "get", "len", "slice"]
# as of v0.23.4, all methods except 'cat' are very lenient with the
# allowed data types, just returning NaN for entries that error.
# This could be changed with an 'errors'-kwarg to the `str`-accessor,
# see discussion in GH 13877
mixed_allowed = method_name not in ["cat"]
allowed_types = (
["string", "unicode", "empty"]
+ ["bytes"] * bytes_allowed
+ ["mixed", "mixed-integer"] * mixed_allowed
)
if inferred_dtype in allowed_types:
# xref GH 23555, GH 23556
with option_context("future.no_silent_downcasting", True):
method(*args, **kwargs) # works!
else:
# GH 23011, GH 23163
msg = (
f"Cannot use .str.{method_name} with values of "
f"inferred dtype {repr(inferred_dtype)}."
)
with pytest.raises(TypeError, match=msg):
method(*args, **kwargs)
def test_api_for_categorical(any_string_method, any_string_dtype):
# https://github.com/pandas-dev/pandas/issues/10661
s = Series(list("aabb"), dtype=any_string_dtype)
s = s + " " + s
c = s.astype("category")
c = c.astype(CategoricalDtype(c.dtype.categories.astype("object")))
assert isinstance(c.str, StringMethods)
method_name, args, kwargs = any_string_method
result = getattr(c.str, method_name)(*args, **kwargs)
expected = getattr(s.astype("object").str, method_name)(*args, **kwargs)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
elif isinstance(result, Series):
tm.assert_series_equal(result, expected)
else:
# str.cat(others=None) returns string, for example
assert result == expected