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aoc-2022/venv/Lib/site-packages/pandas/conftest.py

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"""
This file is very long and growing, but it was decided to not split it yet, as
it's still manageable (2020-03-17, ~1.1k LoC). See gh-31989
Instead of splitting it was decided to define sections here:
- Configuration / Settings
- Autouse fixtures
- Common arguments
- Missing values & co.
- Classes
- Indices
- Series'
- DataFrames
- Operators & Operations
- Data sets/files
- Time zones
- Dtypes
- Misc
"""
from collections import abc
from datetime import (
date,
datetime,
time,
timedelta,
timezone,
)
from decimal import Decimal
import operator
import os
from typing import Callable
from dateutil.tz import (
tzlocal,
tzutc,
)
import hypothesis
from hypothesis import strategies as st
import numpy as np
import pytest
from pytz import (
FixedOffset,
utc,
)
import pandas.util._test_decorators as td
from pandas.core.dtypes.dtypes import (
DatetimeTZDtype,
IntervalDtype,
)
import pandas as pd
from pandas import (
DataFrame,
Interval,
Period,
Series,
Timedelta,
Timestamp,
)
import pandas._testing as tm
from pandas.core import ops
from pandas.core.indexes.api import (
Index,
MultiIndex,
)
try:
import pyarrow as pa
except ImportError:
has_pyarrow = False
else:
del pa
has_pyarrow = True
zoneinfo = None
if pd.compat.PY39:
# Import "zoneinfo" could not be resolved (reportMissingImports)
import zoneinfo # type: ignore[no-redef]
# Although zoneinfo can be imported in Py39, it is effectively
# "not available" without tzdata/IANA tz data.
# We will set zoneinfo to not found in this case
try:
zoneinfo.ZoneInfo("UTC") # type: ignore[attr-defined]
except zoneinfo.ZoneInfoNotFoundError: # type: ignore[attr-defined]
zoneinfo = None
# Until https://github.com/numpy/numpy/issues/19078 is sorted out, just suppress
suppress_npdev_promotion_warning = pytest.mark.filterwarnings(
"ignore:Promotion of numbers and bools:FutureWarning"
)
# ----------------------------------------------------------------
# Configuration / Settings
# ----------------------------------------------------------------
# pytest
def pytest_addoption(parser) -> None:
parser.addoption("--skip-slow", action="store_true", help="skip slow tests")
parser.addoption("--skip-network", action="store_true", help="skip network tests")
parser.addoption("--skip-db", action="store_true", help="skip db tests")
parser.addoption(
"--run-high-memory", action="store_true", help="run high memory tests"
)
parser.addoption("--only-slow", action="store_true", help="run only slow tests")
parser.addoption(
"--strict-data-files",
action="store_true",
help="Fail if a test is skipped for missing data file.",
)
def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None:
"""Ignore doctest warning.
Parameters
----------
item : pytest.Item
pytest test item.
path : str
Module path to Python object, e.g. "pandas.core.frame.DataFrame.append". A
warning will be filtered when item.name ends with in given path. So it is
sufficient to specify e.g. "DataFrame.append".
message : str
Message to be filtered.
"""
if item.name.endswith(path):
item.add_marker(pytest.mark.filterwarnings(f"ignore:{message}"))
def pytest_collection_modifyitems(items, config):
skip_slow = config.getoption("--skip-slow")
only_slow = config.getoption("--only-slow")
skip_network = config.getoption("--skip-network")
skip_db = config.getoption("--skip-db")
marks = [
(pytest.mark.slow, "slow", skip_slow, "--skip-slow"),
(pytest.mark.network, "network", skip_network, "--network"),
(pytest.mark.db, "db", skip_db, "--skip-db"),
]
# Warnings from doctests that can be ignored; place reason in comment above.
# Each entry specifies (path, message) - see the ignore_doctest_warning function
ignored_doctest_warnings = [
# Deprecations where the docstring will emit a warning
("DataFrame.append", "The frame.append method is deprecated"),
("Series.append", "The series.append method is deprecated"),
("dtypes.common.is_categorical", "is_categorical is deprecated"),
("Categorical.replace", "Categorical.replace is deprecated"),
("dtypes.common.is_extension_type", "'is_extension_type' is deprecated"),
("Index.is_mixed", "Index.is_mixed is deprecated"),
("MultiIndex._is_lexsorted", "MultiIndex.is_lexsorted is deprecated"),
# Docstring divides by zero to show behavior difference
("missing.mask_zero_div_zero", "divide by zero encountered"),
# Docstring demonstrates the call raises a warning
("_validators.validate_axis_style_args", "Use named arguments"),
]
for item in items:
if config.getoption("--doctest-modules") or config.getoption(
"--doctest-cython", default=False
):
# autouse=True for the add_doctest_imports can lead to expensive teardowns
# since doctest_namespace is a session fixture
item.add_marker(pytest.mark.usefixtures("add_doctest_imports"))
for path, message in ignored_doctest_warnings:
ignore_doctest_warning(item, path, message)
# mark all tests in the pandas/tests/frame directory with "arraymanager"
if "/frame/" in item.nodeid:
item.add_marker(pytest.mark.arraymanager)
item.add_marker(suppress_npdev_promotion_warning)
for (mark, kwd, skip_if_found, arg_name) in marks:
if kwd in item.keywords:
# If we're skipping, no need to actually add the marker or look for
# other markers
if skip_if_found:
item.add_marker(pytest.mark.skip(f"skipping due to {arg_name}"))
break
item.add_marker(mark)
if only_slow and "slow" not in item.keywords:
item.add_marker(pytest.mark.skip("skipping due to --only-slow"))
# Hypothesis
hypothesis.settings.register_profile(
"ci",
# Hypothesis timing checks are tuned for scalars by default, so we bump
# them from 200ms to 500ms per test case as the global default. If this
# is too short for a specific test, (a) try to make it faster, and (b)
# if it really is slow add `@settings(deadline=...)` with a working value,
# or `deadline=None` to entirely disable timeouts for that test.
# 2022-02-09: Changed deadline from 500 -> None. Deadline leads to
# non-actionable, flaky CI failures (# GH 24641, 44969, 45118, 44969)
deadline=None,
suppress_health_check=(hypothesis.HealthCheck.too_slow,),
)
hypothesis.settings.load_profile("ci")
# Registering these strategies makes them globally available via st.from_type,
# which is use for offsets in tests/tseries/offsets/test_offsets_properties.py
for name in "MonthBegin MonthEnd BMonthBegin BMonthEnd".split():
cls = getattr(pd.tseries.offsets, name)
st.register_type_strategy(
cls, st.builds(cls, n=st.integers(-99, 99), normalize=st.booleans())
)
for name in "YearBegin YearEnd BYearBegin BYearEnd".split():
cls = getattr(pd.tseries.offsets, name)
st.register_type_strategy(
cls,
st.builds(
cls,
n=st.integers(-5, 5),
normalize=st.booleans(),
month=st.integers(min_value=1, max_value=12),
),
)
for name in "QuarterBegin QuarterEnd BQuarterBegin BQuarterEnd".split():
cls = getattr(pd.tseries.offsets, name)
st.register_type_strategy(
cls,
st.builds(
cls,
n=st.integers(-24, 24),
normalize=st.booleans(),
startingMonth=st.integers(min_value=1, max_value=12),
),
)
@pytest.fixture
def add_doctest_imports(doctest_namespace) -> None:
"""
Make `np` and `pd` names available for doctests.
"""
doctest_namespace["np"] = np
doctest_namespace["pd"] = pd
# ----------------------------------------------------------------
# Autouse fixtures
# ----------------------------------------------------------------
@pytest.fixture(autouse=True)
def configure_tests() -> None:
"""
Configure settings for all tests and test modules.
"""
pd.set_option("chained_assignment", "raise")
# ----------------------------------------------------------------
# Common arguments
# ----------------------------------------------------------------
@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis={repr(x)}")
def axis(request):
"""
Fixture for returning the axis numbers of a DataFrame.
"""
return request.param
axis_frame = axis
@pytest.fixture(params=[1, "columns"], ids=lambda x: f"axis={repr(x)}")
def axis_1(request):
"""
Fixture for returning aliases of axis 1 of a DataFrame.
"""
return request.param
@pytest.fixture(params=[True, False, None])
def observed(request):
"""
Pass in the observed keyword to groupby for [True, False]
This indicates whether categoricals should return values for
values which are not in the grouper [False / None], or only values which
appear in the grouper [True]. [None] is supported for future compatibility
if we decide to change the default (and would need to warn if this
parameter is not passed).
"""
return request.param
@pytest.fixture(params=[True, False, None])
def ordered(request):
"""
Boolean 'ordered' parameter for Categorical.
"""
return request.param
@pytest.fixture(params=["first", "last", False])
def keep(request):
"""
Valid values for the 'keep' parameter used in
.duplicated or .drop_duplicates
"""
return request.param
@pytest.fixture(params=["both", "neither", "left", "right"])
def inclusive_endpoints_fixture(request):
"""
Fixture for trying all interval 'inclusive' parameters.
"""
return request.param
@pytest.fixture(params=["left", "right", "both", "neither"])
def closed(request):
"""
Fixture for trying all interval closed parameters.
"""
return request.param
@pytest.fixture(params=["left", "right", "both", "neither"])
def other_closed(request):
"""
Secondary closed fixture to allow parametrizing over all pairs of closed.
"""
return request.param
@pytest.fixture(
params=[
None,
"gzip",
"bz2",
"zip",
"xz",
"tar",
pytest.param("zstd", marks=td.skip_if_no("zstandard")),
]
)
def compression(request):
"""
Fixture for trying common compression types in compression tests.
"""
return request.param
@pytest.fixture(
params=[
"gzip",
"bz2",
"zip",
"xz",
"tar",
pytest.param("zstd", marks=td.skip_if_no("zstandard")),
]
)
def compression_only(request):
"""
Fixture for trying common compression types in compression tests excluding
uncompressed case.
"""
return request.param
@pytest.fixture(params=[True, False])
def writable(request):
"""
Fixture that an array is writable.
"""
return request.param
@pytest.fixture(params=["inner", "outer", "left", "right"])
def join_type(request):
"""
Fixture for trying all types of join operations.
"""
return request.param
@pytest.fixture(params=["nlargest", "nsmallest"])
def nselect_method(request):
"""
Fixture for trying all nselect methods.
"""
return request.param
# ----------------------------------------------------------------
# Missing values & co.
# ----------------------------------------------------------------
@pytest.fixture(params=tm.NULL_OBJECTS, ids=lambda x: type(x).__name__)
def nulls_fixture(request):
"""
Fixture for each null type in pandas.
"""
return request.param
nulls_fixture2 = nulls_fixture # Generate cartesian product of nulls_fixture
@pytest.fixture(params=[None, np.nan, pd.NaT])
def unique_nulls_fixture(request):
"""
Fixture for each null type in pandas, each null type exactly once.
"""
return request.param
# Generate cartesian product of unique_nulls_fixture:
unique_nulls_fixture2 = unique_nulls_fixture
@pytest.fixture(params=tm.NP_NAT_OBJECTS, ids=lambda x: type(x).__name__)
def np_nat_fixture(request):
"""
Fixture for each NaT type in numpy.
"""
return request.param
# Generate cartesian product of np_nat_fixture:
np_nat_fixture2 = np_nat_fixture
# ----------------------------------------------------------------
# Classes
# ----------------------------------------------------------------
@pytest.fixture(params=[DataFrame, Series])
def frame_or_series(request):
"""
Fixture to parametrize over DataFrame and Series.
"""
return request.param
# error: List item 0 has incompatible type "Type[Index]"; expected "Type[IndexOpsMixin]"
@pytest.fixture(
params=[Index, Series], ids=["index", "series"] # type: ignore[list-item]
)
def index_or_series(request):
"""
Fixture to parametrize over Index and Series, made necessary by a mypy
bug, giving an error:
List item 0 has incompatible type "Type[Series]"; expected "Type[PandasObject]"
See GH#29725
"""
return request.param
# Generate cartesian product of index_or_series fixture:
index_or_series2 = index_or_series
@pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"])
def index_or_series_or_array(request):
"""
Fixture to parametrize over Index, Series, and ExtensionArray
"""
return request.param
@pytest.fixture(params=[Index, Series, DataFrame, pd.array], ids=lambda x: x.__name__)
def box_with_array(request):
"""
Fixture to test behavior for Index, Series, DataFrame, and pandas Array
classes
"""
return request.param
box_with_array2 = box_with_array
@pytest.fixture
def dict_subclass():
"""
Fixture for a dictionary subclass.
"""
class TestSubDict(dict):
def __init__(self, *args, **kwargs) -> None:
dict.__init__(self, *args, **kwargs)
return TestSubDict
@pytest.fixture
def non_dict_mapping_subclass():
"""
Fixture for a non-mapping dictionary subclass.
"""
class TestNonDictMapping(abc.Mapping):
def __init__(self, underlying_dict) -> None:
self._data = underlying_dict
def __getitem__(self, key):
return self._data.__getitem__(key)
def __iter__(self):
return self._data.__iter__()
def __len__(self):
return self._data.__len__()
return TestNonDictMapping
# ----------------------------------------------------------------
# Indices
# ----------------------------------------------------------------
@pytest.fixture
def multiindex_year_month_day_dataframe_random_data():
"""
DataFrame with 3 level MultiIndex (year, month, day) covering
first 100 business days from 2000-01-01 with random data
"""
tdf = tm.makeTimeDataFrame(100)
ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()
# use Int64Index, to make sure things work
ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels])
ymd.index.set_names(["year", "month", "day"], inplace=True)
return ymd
@pytest.fixture
def lexsorted_two_level_string_multiindex() -> MultiIndex:
"""
2-level MultiIndex, lexsorted, with string names.
"""
return MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=["first", "second"],
)
@pytest.fixture
def multiindex_dataframe_random_data(
lexsorted_two_level_string_multiindex,
) -> DataFrame:
"""DataFrame with 2 level MultiIndex with random data"""
index = lexsorted_two_level_string_multiindex
return DataFrame(
np.random.randn(10, 3), index=index, columns=Index(["A", "B", "C"], name="exp")
)
def _create_multiindex():
"""
MultiIndex used to test the general functionality of this object
"""
# See Also: tests.multi.conftest.idx
major_axis = Index(["foo", "bar", "baz", "qux"])
minor_axis = Index(["one", "two"])
major_codes = np.array([0, 0, 1, 2, 3, 3])
minor_codes = np.array([0, 1, 0, 1, 0, 1])
index_names = ["first", "second"]
return MultiIndex(
levels=[major_axis, minor_axis],
codes=[major_codes, minor_codes],
names=index_names,
verify_integrity=False,
)
def _create_mi_with_dt64tz_level():
"""
MultiIndex with a level that is a tzaware DatetimeIndex.
"""
# GH#8367 round trip with pickle
return MultiIndex.from_product(
[[1, 2], ["a", "b"], pd.date_range("20130101", periods=3, tz="US/Eastern")],
names=["one", "two", "three"],
)
indices_dict = {
"string": tm.makeStringIndex(100),
"datetime": tm.makeDateIndex(100),
"datetime-tz": tm.makeDateIndex(100, tz="US/Pacific"),
"period": tm.makePeriodIndex(100),
"timedelta": tm.makeTimedeltaIndex(100),
"int": tm.makeIntIndex(100),
"uint": tm.makeUIntIndex(100),
"range": tm.makeRangeIndex(100),
"float": tm.makeFloatIndex(100),
"complex64": tm.makeFloatIndex(100).astype("complex64"),
"complex128": tm.makeFloatIndex(100).astype("complex128"),
"num_int64": tm.makeNumericIndex(100, dtype="int64"),
"num_int32": tm.makeNumericIndex(100, dtype="int32"),
"num_int16": tm.makeNumericIndex(100, dtype="int16"),
"num_int8": tm.makeNumericIndex(100, dtype="int8"),
"num_uint64": tm.makeNumericIndex(100, dtype="uint64"),
"num_uint32": tm.makeNumericIndex(100, dtype="uint32"),
"num_uint16": tm.makeNumericIndex(100, dtype="uint16"),
"num_uint8": tm.makeNumericIndex(100, dtype="uint8"),
"num_float64": tm.makeNumericIndex(100, dtype="float64"),
"num_float32": tm.makeNumericIndex(100, dtype="float32"),
"bool-object": tm.makeBoolIndex(10).astype(object),
"bool-dtype": Index(np.random.randn(10) < 0),
"categorical": tm.makeCategoricalIndex(100),
"interval": tm.makeIntervalIndex(100),
"empty": Index([]),
"tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),
"mi-with-dt64tz-level": _create_mi_with_dt64tz_level(),
"multi": _create_multiindex(),
"repeats": Index([0, 0, 1, 1, 2, 2]),
"nullable_int": Index(np.arange(100), dtype="Int64"),
"nullable_uint": Index(np.arange(100), dtype="UInt16"),
"nullable_float": Index(np.arange(100), dtype="Float32"),
"nullable_bool": Index(np.arange(100).astype(bool), dtype="boolean"),
"string-python": Index(pd.array(tm.makeStringIndex(100), dtype="string[python]")),
}
if has_pyarrow:
idx = Index(pd.array(tm.makeStringIndex(100), dtype="string[pyarrow]"))
indices_dict["string-pyarrow"] = idx
@pytest.fixture(params=indices_dict.keys())
def index(request):
"""
Fixture for many "simple" kinds of indices.
These indices are unlikely to cover corner cases, e.g.
- no names
- no NaTs/NaNs
- no values near implementation bounds
- ...
"""
# copy to avoid mutation, e.g. setting .name
return indices_dict[request.param].copy()
# Needed to generate cartesian product of indices
index_fixture2 = index
@pytest.fixture(
params=[
key for key in indices_dict if not isinstance(indices_dict[key], MultiIndex)
]
)
def index_flat(request):
"""
index fixture, but excluding MultiIndex cases.
"""
key = request.param
return indices_dict[key].copy()
# Alias so we can test with cartesian product of index_flat
index_flat2 = index_flat
@pytest.fixture(
params=[
key
for key in indices_dict
if not (
key in ["int", "uint", "range", "empty", "repeats", "bool-dtype"]
or key.startswith("num_")
)
and not isinstance(indices_dict[key], MultiIndex)
]
)
def index_with_missing(request):
"""
Fixture for indices with missing values.
Integer-dtype and empty cases are excluded because they cannot hold missing
values.
MultiIndex is excluded because isna() is not defined for MultiIndex.
"""
# GH 35538. Use deep copy to avoid illusive bug on np-dev
# GHA pipeline that writes into indices_dict despite copy
ind = indices_dict[request.param].copy(deep=True)
vals = ind.values
if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
# For setting missing values in the top level of MultiIndex
vals = ind.tolist()
vals[0] = (None,) + vals[0][1:]
vals[-1] = (None,) + vals[-1][1:]
return MultiIndex.from_tuples(vals)
else:
vals[0] = None
vals[-1] = None
return type(ind)(vals)
# ----------------------------------------------------------------
# Series'
# ----------------------------------------------------------------
@pytest.fixture
def string_series() -> Series:
"""
Fixture for Series of floats with Index of unique strings
"""
s = tm.makeStringSeries()
s.name = "series"
return s
@pytest.fixture
def object_series() -> Series:
"""
Fixture for Series of dtype object with Index of unique strings
"""
s = tm.makeObjectSeries()
s.name = "objects"
return s
@pytest.fixture
def datetime_series() -> Series:
"""
Fixture for Series of floats with DatetimeIndex
"""
s = tm.makeTimeSeries()
s.name = "ts"
return s
def _create_series(index):
"""Helper for the _series dict"""
size = len(index)
data = np.random.randn(size)
return Series(data, index=index, name="a")
_series = {
f"series-with-{index_id}-index": _create_series(index)
for index_id, index in indices_dict.items()
}
@pytest.fixture
def series_with_simple_index(index) -> Series:
"""
Fixture for tests on series with changing types of indices.
"""
return _create_series(index)
@pytest.fixture
def series_with_multilevel_index() -> Series:
"""
Fixture with a Series with a 2-level MultiIndex.
"""
arrays = [
["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = zip(*arrays)
index = MultiIndex.from_tuples(tuples)
data = np.random.randn(8)
ser = Series(data, index=index)
ser[3] = np.NaN
return ser
_narrow_series = {
f"{dtype.__name__}-series": tm.make_rand_series(name="a", dtype=dtype)
for dtype in tm.NARROW_NP_DTYPES
}
_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}
@pytest.fixture(params=_index_or_series_objs.keys())
def index_or_series_obj(request):
"""
Fixture for tests on indexes, series and series with a narrow dtype
copy to avoid mutation, e.g. setting .name
"""
return _index_or_series_objs[request.param].copy(deep=True)
# ----------------------------------------------------------------
# DataFrames
# ----------------------------------------------------------------
@pytest.fixture
def int_frame() -> DataFrame:
"""
Fixture for DataFrame of ints with index of unique strings
Columns are ['A', 'B', 'C', 'D']
A B C D
vpBeWjM651 1 0 1 0
5JyxmrP1En -1 0 0 0
qEDaoD49U2 -1 1 0 0
m66TkTfsFe 0 0 0 0
EHPaNzEUFm -1 0 -1 0
fpRJCevQhi 2 0 0 0
OlQvnmfi3Q 0 0 -2 0
... .. .. .. ..
uB1FPlz4uP 0 0 0 1
EcSe6yNzCU 0 0 -1 0
L50VudaiI8 -1 1 -2 0
y3bpw4nwIp 0 -1 0 0
H0RdLLwrCT 1 1 0 0
rY82K0vMwm 0 0 0 0
1OPIUjnkjk 2 0 0 0
[30 rows x 4 columns]
"""
return DataFrame(tm.getSeriesData()).astype("int64")
@pytest.fixture
def datetime_frame() -> DataFrame:
"""
Fixture for DataFrame of floats with DatetimeIndex
Columns are ['A', 'B', 'C', 'D']
A B C D
2000-01-03 -1.122153 0.468535 0.122226 1.693711
2000-01-04 0.189378 0.486100 0.007864 -1.216052
2000-01-05 0.041401 -0.835752 -0.035279 -0.414357
2000-01-06 0.430050 0.894352 0.090719 0.036939
2000-01-07 -0.620982 -0.668211 -0.706153 1.466335
2000-01-10 -0.752633 0.328434 -0.815325 0.699674
2000-01-11 -2.236969 0.615737 -0.829076 -1.196106
... ... ... ... ...
2000-02-03 1.642618 -0.579288 0.046005 1.385249
2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
2000-02-07 -2.656149 -0.601387 1.410148 0.444150
2000-02-08 -1.201881 -1.289040 0.772992 -1.445300
2000-02-09 1.377373 0.398619 1.008453 -0.928207
2000-02-10 0.473194 -0.636677 0.984058 0.511519
2000-02-11 -0.965556 0.408313 -1.312844 -0.381948
[30 rows x 4 columns]
"""
return DataFrame(tm.getTimeSeriesData())
@pytest.fixture
def float_frame() -> DataFrame:
"""
Fixture for DataFrame of floats with index of unique strings
Columns are ['A', 'B', 'C', 'D'].
A B C D
P7GACiRnxd -0.465578 -0.361863 0.886172 -0.053465
qZKh6afn8n -0.466693 -0.373773 0.266873 1.673901
tkp0r6Qble 0.148691 -0.059051 0.174817 1.598433
wP70WOCtv8 0.133045 -0.581994 -0.992240 0.261651
M2AeYQMnCz -1.207959 -0.185775 0.588206 0.563938
QEPzyGDYDo -0.381843 -0.758281 0.502575 -0.565053
r78Jwns6dn -0.653707 0.883127 0.682199 0.206159
... ... ... ... ...
IHEGx9NO0T -0.277360 0.113021 -1.018314 0.196316
lPMj8K27FA -1.313667 -0.604776 -1.305618 -0.863999
qa66YMWQa5 1.110525 0.475310 -0.747865 0.032121
yOa0ATsmcE -0.431457 0.067094 0.096567 -0.264962
65znX3uRNG 1.528446 0.160416 -0.109635 -0.032987
eCOBvKqf3e 0.235281 1.622222 0.781255 0.392871
xSucinXxuV -1.263557 0.252799 -0.552247 0.400426
[30 rows x 4 columns]
"""
return DataFrame(tm.getSeriesData())
@pytest.fixture
def mixed_type_frame() -> DataFrame:
"""
Fixture for DataFrame of float/int/string columns with RangeIndex
Columns are ['a', 'b', 'c', 'float32', 'int32'].
"""
return DataFrame(
{
"a": 1.0,
"b": 2,
"c": "foo",
"float32": np.array([1.0] * 10, dtype="float32"),
"int32": np.array([1] * 10, dtype="int32"),
},
index=np.arange(10),
)
@pytest.fixture
def rand_series_with_duplicate_datetimeindex() -> Series:
"""
Fixture for Series with a DatetimeIndex that has duplicates.
"""
dates = [
datetime(2000, 1, 2),
datetime(2000, 1, 2),
datetime(2000, 1, 2),
datetime(2000, 1, 3),
datetime(2000, 1, 3),
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 4),
datetime(2000, 1, 4),
datetime(2000, 1, 5),
]
return Series(np.random.randn(len(dates)), index=dates)
# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(
params=[
(Interval(left=0, right=5), IntervalDtype("int64", "right")),
(Interval(left=0.1, right=0.5), IntervalDtype("float64", "right")),
(Period("2012-01", freq="M"), "period[M]"),
(Period("2012-02-01", freq="D"), "period[D]"),
(
Timestamp("2011-01-01", tz="US/Eastern"),
DatetimeTZDtype(tz="US/Eastern"),
),
(Timedelta(seconds=500), "timedelta64[ns]"),
]
)
def ea_scalar_and_dtype(request):
return request.param
# ----------------------------------------------------------------
# Operators & Operations
# ----------------------------------------------------------------
_all_arithmetic_operators = [
"__add__",
"__radd__",
"__sub__",
"__rsub__",
"__mul__",
"__rmul__",
"__floordiv__",
"__rfloordiv__",
"__truediv__",
"__rtruediv__",
"__pow__",
"__rpow__",
"__mod__",
"__rmod__",
]
@pytest.fixture(params=_all_arithmetic_operators)
def all_arithmetic_operators(request):
"""
Fixture for dunder names for common arithmetic operations.
"""
return request.param
@pytest.fixture(
params=[
operator.add,
ops.radd,
operator.sub,
ops.rsub,
operator.mul,
ops.rmul,
operator.truediv,
ops.rtruediv,
operator.floordiv,
ops.rfloordiv,
operator.mod,
ops.rmod,
operator.pow,
ops.rpow,
operator.eq,
operator.ne,
operator.lt,
operator.le,
operator.gt,
operator.ge,
operator.and_,
ops.rand_,
operator.xor,
ops.rxor,
operator.or_,
ops.ror_,
]
)
def all_binary_operators(request):
"""
Fixture for operator and roperator arithmetic, comparison, and logical ops.
"""
return request.param
@pytest.fixture(
params=[
operator.add,
ops.radd,
operator.sub,
ops.rsub,
operator.mul,
ops.rmul,
operator.truediv,
ops.rtruediv,
operator.floordiv,
ops.rfloordiv,
operator.mod,
ops.rmod,
operator.pow,
ops.rpow,
]
)
def all_arithmetic_functions(request):
"""
Fixture for operator and roperator arithmetic functions.
Notes
-----
This includes divmod and rdivmod, whereas all_arithmetic_operators
does not.
"""
return request.param
_all_numeric_reductions = [
"sum",
"max",
"min",
"mean",
"prod",
"std",
"var",
"median",
"kurt",
"skew",
]
@pytest.fixture(params=_all_numeric_reductions)
def all_numeric_reductions(request):
"""
Fixture for numeric reduction names.
"""
return request.param
_all_boolean_reductions = ["all", "any"]
@pytest.fixture(params=_all_boolean_reductions)
def all_boolean_reductions(request):
"""
Fixture for boolean reduction names.
"""
return request.param
_all_reductions = _all_numeric_reductions + _all_boolean_reductions
@pytest.fixture(params=_all_reductions)
def all_reductions(request):
"""
Fixture for all (boolean + numeric) reduction names.
"""
return request.param
@pytest.fixture(
params=[
operator.eq,
operator.ne,
operator.gt,
operator.ge,
operator.lt,
operator.le,
]
)
def comparison_op(request):
"""
Fixture for operator module comparison functions.
"""
return request.param
@pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"])
def compare_operators_no_eq_ne(request):
"""
Fixture for dunder names for compare operations except == and !=
* >=
* >
* <
* <=
"""
return request.param
@pytest.fixture(
params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"]
)
def all_logical_operators(request):
"""
Fixture for dunder names for common logical operations
* |
* &
* ^
"""
return request.param
# ----------------------------------------------------------------
# Data sets/files
# ----------------------------------------------------------------
@pytest.fixture
def strict_data_files(pytestconfig):
"""
Returns the configuration for the test setting `--strict-data-files`.
"""
return pytestconfig.getoption("--strict-data-files")
@pytest.fixture
def datapath(strict_data_files: str) -> Callable[..., str]:
"""
Get the path to a data file.
Parameters
----------
path : str
Path to the file, relative to ``pandas/tests/``
Returns
-------
path including ``pandas/tests``.
Raises
------
ValueError
If the path doesn't exist and the --strict-data-files option is set.
"""
BASE_PATH = os.path.join(os.path.dirname(__file__), "tests")
def deco(*args):
path = os.path.join(BASE_PATH, *args)
if not os.path.exists(path):
if strict_data_files:
raise ValueError(
f"Could not find file {path} and --strict-data-files is set."
)
else:
pytest.skip(f"Could not find {path}.")
return path
return deco
@pytest.fixture
def iris(datapath) -> DataFrame:
"""
The iris dataset as a DataFrame.
"""
return pd.read_csv(datapath("io", "data", "csv", "iris.csv"))
# ----------------------------------------------------------------
# Time zones
# ----------------------------------------------------------------
TIMEZONES = [
None,
"UTC",
"US/Eastern",
"Asia/Tokyo",
"dateutil/US/Pacific",
"dateutil/Asia/Singapore",
"+01:15",
"-02:15",
"UTC+01:15",
"UTC-02:15",
tzutc(),
tzlocal(),
FixedOffset(300),
FixedOffset(0),
FixedOffset(-300),
timezone.utc,
timezone(timedelta(hours=1)),
timezone(timedelta(hours=-1), name="foo"),
]
if zoneinfo is not None:
TIMEZONES.extend([zoneinfo.ZoneInfo("US/Pacific"), zoneinfo.ZoneInfo("UTC")])
TIMEZONE_IDS = [repr(i) for i in TIMEZONES]
@td.parametrize_fixture_doc(str(TIMEZONE_IDS))
@pytest.fixture(params=TIMEZONES, ids=TIMEZONE_IDS)
def tz_naive_fixture(request):
"""
Fixture for trying timezones including default (None): {0}
"""
return request.param
@td.parametrize_fixture_doc(str(TIMEZONE_IDS[1:]))
@pytest.fixture(params=TIMEZONES[1:], ids=TIMEZONE_IDS[1:])
def tz_aware_fixture(request):
"""
Fixture for trying explicit timezones: {0}
"""
return request.param
# Generate cartesian product of tz_aware_fixture:
tz_aware_fixture2 = tz_aware_fixture
_UTCS = ["utc", "dateutil/UTC", utc, tzutc(), timezone.utc]
if zoneinfo is not None:
_UTCS.append(zoneinfo.ZoneInfo("UTC"))
@pytest.fixture(params=_UTCS)
def utc_fixture(request):
"""
Fixture to provide variants of UTC timezone strings and tzinfo objects.
"""
return request.param
utc_fixture2 = utc_fixture
# ----------------------------------------------------------------
# Dtypes
# ----------------------------------------------------------------
@pytest.fixture(params=tm.STRING_DTYPES)
def string_dtype(request):
"""
Parametrized fixture for string dtypes.
* str
* 'str'
* 'U'
"""
return request.param
@pytest.fixture(
params=[
"string[python]",
pytest.param(
"string[pyarrow]", marks=td.skip_if_no("pyarrow", min_version="1.0.0")
),
]
)
def nullable_string_dtype(request):
"""
Parametrized fixture for string dtypes.
* 'string[python]'
* 'string[pyarrow]'
"""
return request.param
@pytest.fixture(
params=[
"python",
pytest.param("pyarrow", marks=td.skip_if_no("pyarrow", min_version="1.0.0")),
]
)
def string_storage(request):
"""
Parametrized fixture for pd.options.mode.string_storage.
* 'python'
* 'pyarrow'
"""
return request.param
# Alias so we can test with cartesian product of string_storage
string_storage2 = string_storage
@pytest.fixture(params=tm.BYTES_DTYPES)
def bytes_dtype(request):
"""
Parametrized fixture for bytes dtypes.
* bytes
* 'bytes'
"""
return request.param
@pytest.fixture(params=tm.OBJECT_DTYPES)
def object_dtype(request):
"""
Parametrized fixture for object dtypes.
* object
* 'object'
"""
return request.param
@pytest.fixture(
params=[
"object",
"string[python]",
pytest.param(
"string[pyarrow]", marks=td.skip_if_no("pyarrow", min_version="1.0.0")
),
]
)
def any_string_dtype(request):
"""
Parametrized fixture for string dtypes.
* 'object'
* 'string[python]'
* 'string[pyarrow]'
"""
return request.param
@pytest.fixture(params=tm.DATETIME64_DTYPES)
def datetime64_dtype(request):
"""
Parametrized fixture for datetime64 dtypes.
* 'datetime64[ns]'
* 'M8[ns]'
"""
return request.param
@pytest.fixture(params=tm.TIMEDELTA64_DTYPES)
def timedelta64_dtype(request):
"""
Parametrized fixture for timedelta64 dtypes.
* 'timedelta64[ns]'
* 'm8[ns]'
"""
return request.param
@pytest.fixture
def fixed_now_ts() -> Timestamp:
"""
Fixture emits fixed Timestamp.now()
"""
return Timestamp(
year=2021, month=1, day=1, hour=12, minute=4, second=13, microsecond=22
)
@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES)
def float_numpy_dtype(request):
"""
Parameterized fixture for float dtypes.
* float
* 'float32'
* 'float64'
"""
return request.param
@pytest.fixture(params=tm.FLOAT_EA_DTYPES)
def float_ea_dtype(request):
"""
Parameterized fixture for float dtypes.
* 'Float32'
* 'Float64'
"""
return request.param
@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES + tm.FLOAT_EA_DTYPES)
def any_float_dtype(request):
"""
Parameterized fixture for float dtypes.
* float
* 'float32'
* 'float64'
* 'Float32'
* 'Float64'
"""
return request.param
@pytest.fixture(params=tm.COMPLEX_DTYPES)
def complex_dtype(request):
"""
Parameterized fixture for complex dtypes.
* complex
* 'complex64'
* 'complex128'
"""
return request.param
@pytest.fixture(params=tm.SIGNED_INT_NUMPY_DTYPES)
def any_signed_int_numpy_dtype(request):
"""
Parameterized fixture for signed integer dtypes.
* int
* 'int8'
* 'int16'
* 'int32'
* 'int64'
"""
return request.param
@pytest.fixture(params=tm.UNSIGNED_INT_NUMPY_DTYPES)
def any_unsigned_int_numpy_dtype(request):
"""
Parameterized fixture for unsigned integer dtypes.
* 'uint8'
* 'uint16'
* 'uint32'
* 'uint64'
"""
return request.param
@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES)
def any_int_numpy_dtype(request):
"""
Parameterized fixture for any integer dtype.
* int
* 'int8'
* 'uint8'
* 'int16'
* 'uint16'
* 'int32'
* 'uint32'
* 'int64'
* 'uint64'
"""
return request.param
@pytest.fixture(params=tm.ALL_INT_EA_DTYPES)
def any_int_ea_dtype(request):
"""
Parameterized fixture for any nullable integer dtype.
* 'UInt8'
* 'Int8'
* 'UInt16'
* 'Int16'
* 'UInt32'
* 'Int32'
* 'UInt64'
* 'Int64'
"""
return request.param
@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES + tm.ALL_INT_EA_DTYPES)
def any_int_dtype(request):
"""
Parameterized fixture for any nullable integer dtype.
* int
* 'int8'
* 'uint8'
* 'int16'
* 'uint16'
* 'int32'
* 'uint32'
* 'int64'
* 'uint64'
* 'UInt8'
* 'Int8'
* 'UInt16'
* 'Int16'
* 'UInt32'
* 'Int32'
* 'UInt64'
* 'Int64'
"""
return request.param
@pytest.fixture(params=tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES)
def any_numeric_ea_dtype(request):
"""
Parameterized fixture for any nullable integer dtype and
any float ea dtypes.
* 'UInt8'
* 'Int8'
* 'UInt16'
* 'Int16'
* 'UInt32'
* 'Int32'
* 'UInt64'
* 'Int64'
* 'Float32'
* 'Float64'
"""
return request.param
@pytest.fixture(params=tm.SIGNED_INT_EA_DTYPES)
def any_signed_int_ea_dtype(request):
"""
Parameterized fixture for any signed nullable integer dtype.
* 'Int8'
* 'Int16'
* 'Int32'
* 'Int64'
"""
return request.param
@pytest.fixture(params=tm.ALL_REAL_NUMPY_DTYPES)
def any_real_numpy_dtype(request):
"""
Parameterized fixture for any (purely) real numeric dtype.
* int
* 'int8'
* 'uint8'
* 'int16'
* 'uint16'
* 'int32'
* 'uint32'
* 'int64'
* 'uint64'
* float
* 'float32'
* 'float64'
"""
return request.param
@pytest.fixture(params=tm.ALL_NUMPY_DTYPES)
def any_numpy_dtype(request):
"""
Parameterized fixture for all numpy dtypes.
* bool
* 'bool'
* int
* 'int8'
* 'uint8'
* 'int16'
* 'uint16'
* 'int32'
* 'uint32'
* 'int64'
* 'uint64'
* float
* 'float32'
* 'float64'
* complex
* 'complex64'
* 'complex128'
* str
* 'str'
* 'U'
* bytes
* 'bytes'
* 'datetime64[ns]'
* 'M8[ns]'
* 'timedelta64[ns]'
* 'm8[ns]'
* object
* 'object'
"""
return request.param
@pytest.fixture(
params=tm.ALL_REAL_NUMPY_DTYPES
+ tm.COMPLEX_DTYPES
+ tm.ALL_INT_EA_DTYPES
+ tm.FLOAT_EA_DTYPES
)
def any_numeric_dtype(request):
"""
Parameterized fixture for all numeric dtypes.
* int
* 'int8'
* 'uint8'
* 'int16'
* 'uint16'
* 'int32'
* 'uint32'
* 'int64'
* 'uint64'
* float
* 'float32'
* 'float64'
* complex
* 'complex64'
* 'complex128'
* 'UInt8'
* 'Int8'
* 'UInt16'
* 'Int16'
* 'UInt32'
* 'Int32'
* 'UInt64'
* 'Int64'
* 'Float32'
* 'Float64'
"""
return request.param
# categoricals are handled separately
_any_skipna_inferred_dtype = [
("string", ["a", np.nan, "c"]),
("string", ["a", pd.NA, "c"]),
("mixed", ["a", pd.NaT, "c"]), # pd.NaT not considered valid by is_string_array
("bytes", [b"a", np.nan, b"c"]),
("empty", [np.nan, np.nan, np.nan]),
("empty", []),
("mixed-integer", ["a", np.nan, 2]),
("mixed", ["a", np.nan, 2.0]),
("floating", [1.0, np.nan, 2.0]),
("integer", [1, np.nan, 2]),
("mixed-integer-float", [1, np.nan, 2.0]),
("decimal", [Decimal(1), np.nan, Decimal(2)]),
("boolean", [True, np.nan, False]),
("boolean", [True, pd.NA, False]),
("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
("datetime", [Timestamp("20130101"), np.nan, Timestamp("20180101")]),
("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
# The following two dtypes are commented out due to GH 23554
# ('complex', [1 + 1j, np.nan, 2 + 2j]),
# ('timedelta64', [np.timedelta64(1, 'D'),
# np.nan, np.timedelta64(2, 'D')]),
("timedelta", [timedelta(1), np.nan, timedelta(2)]),
("time", [time(1), np.nan, time(2)]),
("period", [Period(2013), pd.NaT, Period(2018)]),
("interval", [Interval(0, 1), np.nan, Interval(0, 2)]),
]
ids, _ = zip(*_any_skipna_inferred_dtype) # use inferred type as fixture-id
@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
def any_skipna_inferred_dtype(request):
"""
Fixture for all inferred dtypes from _libs.lib.infer_dtype
The covered (inferred) types are:
* 'string'
* 'empty'
* 'bytes'
* 'mixed'
* 'mixed-integer'
* 'mixed-integer-float'
* 'floating'
* 'integer'
* 'decimal'
* 'boolean'
* 'datetime64'
* 'datetime'
* 'date'
* 'timedelta'
* 'time'
* 'period'
* 'interval'
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
--------
>>> import pandas._libs.lib as lib
>>>
>>> def test_something(any_skipna_inferred_dtype):
... inferred_dtype, values = any_skipna_inferred_dtype
... # will pass
... assert lib.infer_dtype(values, skipna=True) == inferred_dtype
"""
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
# ----------------------------------------------------------------
# Misc
# ----------------------------------------------------------------
@pytest.fixture
def ip():
"""
Get an instance of IPython.InteractiveShell.
Will raise a skip if IPython is not installed.
"""
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.interactiveshell import InteractiveShell
# GH#35711 make sure sqlite history file handle is not leaked
from traitlets.config import Config # isort:skip
c = Config()
c.HistoryManager.hist_file = ":memory:"
return InteractiveShell(config=c)
@pytest.fixture(params=["bsr", "coo", "csc", "csr", "dia", "dok", "lil"])
def spmatrix(request):
"""
Yields scipy sparse matrix classes.
"""
from scipy import sparse
return getattr(sparse, request.param + "_matrix")
@pytest.fixture(
params=[
getattr(pd.offsets, o)
for o in pd.offsets.__all__
if issubclass(getattr(pd.offsets, o), pd.offsets.Tick) and o != "Tick"
]
)
def tick_classes(request):
"""
Fixture for Tick based datetime offsets available for a time series.
"""
return request.param
@pytest.fixture(params=[None, lambda x: x])
def sort_by_key(request):
"""
Simple fixture for testing keys in sorting methods.
Tests None (no key) and the identity key.
"""
return request.param
@pytest.fixture()
def fsspectest():
pytest.importorskip("fsspec")
from fsspec import register_implementation
from fsspec.implementations.memory import MemoryFileSystem
from fsspec.registry import _registry as registry
class TestMemoryFS(MemoryFileSystem):
protocol = "testmem"
test = [None]
def __init__(self, **kwargs) -> None:
self.test[0] = kwargs.pop("test", None)
super().__init__(**kwargs)
register_implementation("testmem", TestMemoryFS, clobber=True)
yield TestMemoryFS()
registry.pop("testmem", None)
TestMemoryFS.test[0] = None
TestMemoryFS.store.clear()
@pytest.fixture(
params=[
("foo", None, None),
("Egon", "Venkman", None),
("NCC1701D", "NCC1701D", "NCC1701D"),
# possibly-matching NAs
(np.nan, np.nan, np.nan),
(np.nan, pd.NaT, None),
(np.nan, pd.NA, None),
(pd.NA, pd.NA, pd.NA),
]
)
def names(request):
"""
A 3-tuple of names, the first two for operands, the last for a result.
"""
return request.param
@pytest.fixture(params=[tm.setitem, tm.loc, tm.iloc])
def indexer_sli(request):
"""
Parametrize over __setitem__, loc.__setitem__, iloc.__setitem__
"""
return request.param
@pytest.fixture(params=[tm.loc, tm.iloc])
def indexer_li(request):
"""
Parametrize over loc.__getitem__, iloc.__getitem__
"""
return request.param
@pytest.fixture(params=[tm.setitem, tm.iloc])
def indexer_si(request):
"""
Parametrize over __setitem__, iloc.__setitem__
"""
return request.param
@pytest.fixture(params=[tm.setitem, tm.loc])
def indexer_sl(request):
"""
Parametrize over __setitem__, loc.__setitem__
"""
return request.param
@pytest.fixture(params=[tm.at, tm.loc])
def indexer_al(request):
"""
Parametrize over at.__setitem__, loc.__setitem__
"""
return request.param
@pytest.fixture(params=[tm.iat, tm.iloc])
def indexer_ial(request):
"""
Parametrize over iat.__setitem__, iloc.__setitem__
"""
return request.param
@pytest.fixture
def using_array_manager():
"""
Fixture to check if the array manager is being used.
"""
return pd.options.mode.data_manager == "array"
@pytest.fixture
def using_copy_on_write() -> bool:
"""
Fixture to check if Copy-on-Write is enabled.
"""
return pd.options.mode.copy_on_write and pd.options.mode.data_manager == "block"
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