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