331 行
9.6 KiB
Python
331 行
9.6 KiB
Python
"""
|
|
Testing that we work in the downstream packages
|
|
"""
|
|
import importlib
|
|
import subprocess
|
|
import sys
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
DataFrame,
|
|
Series,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
# geopandas, xarray, fsspec, fastparquet all produce these
|
|
pytestmark = pytest.mark.filterwarnings(
|
|
"ignore:distutils Version classes are deprecated.*:DeprecationWarning"
|
|
)
|
|
|
|
|
|
def import_module(name):
|
|
# we *only* want to skip if the module is truly not available
|
|
# and NOT just an actual import error because of pandas changes
|
|
|
|
try:
|
|
return importlib.import_module(name)
|
|
except ModuleNotFoundError:
|
|
pytest.skip(f"skipping as {name} not available")
|
|
|
|
|
|
@pytest.fixture
|
|
def df():
|
|
return DataFrame({"A": [1, 2, 3]})
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:.*64Index is deprecated:FutureWarning")
|
|
def test_dask(df):
|
|
|
|
# dask sets "compute.use_numexpr" to False, so catch the current value
|
|
# and ensure to reset it afterwards to avoid impacting other tests
|
|
olduse = pd.get_option("compute.use_numexpr")
|
|
|
|
try:
|
|
toolz = import_module("toolz") # noqa:F841
|
|
dask = import_module("dask") # noqa:F841
|
|
|
|
import dask.dataframe as dd
|
|
|
|
ddf = dd.from_pandas(df, npartitions=3)
|
|
assert ddf.A is not None
|
|
assert ddf.compute() is not None
|
|
finally:
|
|
pd.set_option("compute.use_numexpr", olduse)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:.*64Index is deprecated:FutureWarning")
|
|
@pytest.mark.filterwarnings("ignore:The __array_wrap__:DeprecationWarning")
|
|
def test_dask_ufunc():
|
|
# At the time of dask 2022.01.0, dask is still directly using __array_wrap__
|
|
# for some ufuncs (https://github.com/dask/dask/issues/8580).
|
|
|
|
# dask sets "compute.use_numexpr" to False, so catch the current value
|
|
# and ensure to reset it afterwards to avoid impacting other tests
|
|
olduse = pd.get_option("compute.use_numexpr")
|
|
|
|
try:
|
|
dask = import_module("dask") # noqa:F841
|
|
import dask.array as da
|
|
import dask.dataframe as dd
|
|
|
|
s = Series([1.5, 2.3, 3.7, 4.0])
|
|
ds = dd.from_pandas(s, npartitions=2)
|
|
|
|
result = da.fix(ds).compute()
|
|
expected = np.fix(s)
|
|
tm.assert_series_equal(result, expected)
|
|
finally:
|
|
pd.set_option("compute.use_numexpr", olduse)
|
|
|
|
|
|
@td.skip_if_no("dask")
|
|
def test_construct_dask_float_array_int_dtype_match_ndarray():
|
|
# GH#40110 make sure we treat a float-dtype dask array with the same
|
|
# rules we would for an ndarray
|
|
import dask.dataframe as dd
|
|
|
|
arr = np.array([1, 2.5, 3])
|
|
darr = dd.from_array(arr)
|
|
|
|
res = Series(darr)
|
|
expected = Series(arr)
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
res = Series(darr, dtype="i8")
|
|
expected = Series(arr, dtype="i8")
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
msg = "In a future version, passing float-dtype values containing NaN"
|
|
arr[2] = np.nan
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = Series(darr, dtype="i8")
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
expected = Series(arr, dtype="i8")
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
|
|
def test_xarray(df):
|
|
|
|
xarray = import_module("xarray") # noqa:F841
|
|
|
|
assert df.to_xarray() is not None
|
|
|
|
|
|
@td.skip_if_no("cftime")
|
|
@td.skip_if_no("xarray", "0.10.4")
|
|
def test_xarray_cftimeindex_nearest():
|
|
# https://github.com/pydata/xarray/issues/3751
|
|
import cftime
|
|
import xarray
|
|
|
|
times = xarray.cftime_range("0001", periods=2)
|
|
key = cftime.DatetimeGregorian(2000, 1, 1)
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="deprecated", check_stacklevel=False
|
|
):
|
|
result = times.get_loc(key, method="nearest")
|
|
expected = 1
|
|
assert result == expected
|
|
|
|
|
|
def test_oo_optimizable():
|
|
# GH 21071
|
|
subprocess.check_call([sys.executable, "-OO", "-c", "import pandas"])
|
|
|
|
|
|
def test_oo_optimized_datetime_index_unpickle():
|
|
# GH 42866
|
|
subprocess.check_call(
|
|
[
|
|
sys.executable,
|
|
"-OO",
|
|
"-c",
|
|
(
|
|
"import pandas as pd, pickle; "
|
|
"pickle.loads(pickle.dumps(pd.date_range('2021-01-01', periods=1)))"
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
@pytest.mark.network
|
|
@tm.network
|
|
# Cython import warning
|
|
@pytest.mark.filterwarnings("ignore:pandas.util.testing is deprecated")
|
|
@pytest.mark.filterwarnings("ignore:can't:ImportWarning")
|
|
@pytest.mark.filterwarnings("ignore:.*64Index is deprecated:FutureWarning")
|
|
@pytest.mark.filterwarnings(
|
|
# patsy needs to update their imports
|
|
"ignore:Using or importing the ABCs from 'collections:DeprecationWarning"
|
|
)
|
|
@pytest.mark.filterwarnings(
|
|
# numpy 1.22
|
|
"ignore:`np.MachAr` is deprecated.*:DeprecationWarning"
|
|
)
|
|
def test_statsmodels():
|
|
|
|
statsmodels = import_module("statsmodels") # noqa:F841
|
|
import statsmodels.api as sm
|
|
import statsmodels.formula.api as smf
|
|
|
|
df = sm.datasets.get_rdataset("Guerry", "HistData").data
|
|
smf.ols("Lottery ~ Literacy + np.log(Pop1831)", data=df).fit()
|
|
|
|
|
|
# Cython import warning
|
|
@pytest.mark.filterwarnings("ignore:can't:ImportWarning")
|
|
def test_scikit_learn():
|
|
|
|
sklearn = import_module("sklearn") # noqa:F841
|
|
from sklearn import (
|
|
datasets,
|
|
svm,
|
|
)
|
|
|
|
digits = datasets.load_digits()
|
|
clf = svm.SVC(gamma=0.001, C=100.0)
|
|
clf.fit(digits.data[:-1], digits.target[:-1])
|
|
clf.predict(digits.data[-1:])
|
|
|
|
|
|
# Cython import warning and traitlets
|
|
@pytest.mark.network
|
|
@tm.network
|
|
@pytest.mark.filterwarnings("ignore")
|
|
def test_seaborn():
|
|
|
|
seaborn = import_module("seaborn")
|
|
tips = seaborn.load_dataset("tips")
|
|
seaborn.stripplot(x="day", y="total_bill", data=tips)
|
|
|
|
|
|
def test_pandas_gbq():
|
|
# Older versions import from non-public, non-existent pandas funcs
|
|
pytest.importorskip("pandas_gbq", minversion="0.10.0")
|
|
pandas_gbq = import_module("pandas_gbq") # noqa:F841
|
|
|
|
|
|
@pytest.mark.network
|
|
@tm.network
|
|
@pytest.mark.xfail(
|
|
raises=ValueError,
|
|
reason="The Quandl API key must be provided either through the api_key "
|
|
"variable or through the environmental variable QUANDL_API_KEY",
|
|
)
|
|
def test_pandas_datareader():
|
|
|
|
pandas_datareader = import_module("pandas_datareader")
|
|
pandas_datareader.DataReader("F", "quandl", "2017-01-01", "2017-02-01")
|
|
|
|
|
|
def test_geopandas():
|
|
|
|
geopandas = import_module("geopandas")
|
|
gdf = geopandas.GeoDataFrame(
|
|
{"col": [1, 2, 3], "geometry": geopandas.points_from_xy([1, 2, 3], [1, 2, 3])}
|
|
)
|
|
assert gdf[["col", "geometry"]].geometry.x.equals(Series([1.0, 2.0, 3.0]))
|
|
|
|
|
|
# Cython import warning
|
|
@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
|
|
@pytest.mark.filterwarnings("ignore:RangeIndex.* is deprecated:DeprecationWarning")
|
|
def test_pyarrow(df):
|
|
|
|
pyarrow = import_module("pyarrow")
|
|
table = pyarrow.Table.from_pandas(df)
|
|
result = table.to_pandas()
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
|
|
def test_torch_frame_construction(using_array_manager):
|
|
# GH#44616
|
|
torch = import_module("torch")
|
|
val_tensor = torch.randn(700, 64)
|
|
|
|
df = DataFrame(val_tensor)
|
|
|
|
if not using_array_manager:
|
|
assert np.shares_memory(df, val_tensor)
|
|
|
|
ser = Series(val_tensor[0])
|
|
assert np.shares_memory(ser, val_tensor)
|
|
|
|
|
|
def test_yaml_dump(df):
|
|
# GH#42748
|
|
yaml = import_module("yaml")
|
|
|
|
dumped = yaml.dump(df)
|
|
|
|
loaded = yaml.load(dumped, Loader=yaml.Loader)
|
|
tm.assert_frame_equal(df, loaded)
|
|
|
|
loaded2 = yaml.load(dumped, Loader=yaml.UnsafeLoader)
|
|
tm.assert_frame_equal(df, loaded2)
|
|
|
|
|
|
def test_missing_required_dependency():
|
|
# GH 23868
|
|
# To ensure proper isolation, we pass these flags
|
|
# -S : disable site-packages
|
|
# -s : disable user site-packages
|
|
# -E : disable PYTHON* env vars, especially PYTHONPATH
|
|
# https://github.com/MacPython/pandas-wheels/pull/50
|
|
|
|
pyexe = sys.executable.replace("\\", "/")
|
|
|
|
# We skip this test if pandas is installed as a site package. We first
|
|
# import the package normally and check the path to the module before
|
|
# executing the test which imports pandas with site packages disabled.
|
|
call = [pyexe, "-c", "import pandas;print(pandas.__file__)"]
|
|
output = subprocess.check_output(call).decode()
|
|
if "site-packages" in output:
|
|
pytest.skip("pandas installed as site package")
|
|
|
|
# This test will fail if pandas is installed as a site package. The flags
|
|
# prevent pandas being imported and the test will report Failed: DID NOT
|
|
# RAISE <class 'subprocess.CalledProcessError'>
|
|
call = [pyexe, "-sSE", "-c", "import pandas"]
|
|
|
|
msg = (
|
|
rf"Command '\['{pyexe}', '-sSE', '-c', 'import pandas'\]' "
|
|
"returned non-zero exit status 1."
|
|
)
|
|
|
|
with pytest.raises(subprocess.CalledProcessError, match=msg) as exc:
|
|
subprocess.check_output(call, stderr=subprocess.STDOUT)
|
|
|
|
output = exc.value.stdout.decode()
|
|
for name in ["numpy", "pytz", "dateutil"]:
|
|
assert name in output
|
|
|
|
|
|
def test_frame_setitem_dask_array_into_new_col():
|
|
# GH#47128
|
|
|
|
# dask sets "compute.use_numexpr" to False, so catch the current value
|
|
# and ensure to reset it afterwards to avoid impacting other tests
|
|
olduse = pd.get_option("compute.use_numexpr")
|
|
|
|
try:
|
|
dask = import_module("dask") # noqa:F841
|
|
|
|
import dask.array as da
|
|
|
|
dda = da.array([1, 2])
|
|
df = DataFrame({"a": ["a", "b"]})
|
|
df["b"] = dda
|
|
df["c"] = dda
|
|
df.loc[[False, True], "b"] = 100
|
|
result = df.loc[[1], :]
|
|
expected = DataFrame({"a": ["b"], "b": [100], "c": [2]}, index=[1])
|
|
tm.assert_frame_equal(result, expected)
|
|
finally:
|
|
pd.set_option("compute.use_numexpr", olduse)
|