def test_numpy_ufuncs_basic(index, func):
# test ufuncs of numpy, see:
# https://numpy.org/doc/stable/reference/ufuncs.html
if isinstance(index, DatetimeIndexOpsMixin):
with tm.external_error_raised((TypeError, AttributeError)):
with np.errstate(all="ignore"):
func(index)
elif isinstance(index,
(Float64Index, Int64Index, UInt64Index, RangeIndex)):
# coerces to float (e.g. np.sin)
with np.errstate(all="ignore"):
result = func(index)
exp = Index(func(index.values), name=index.name)
tm.assert_index_equal(result, exp)
assert isinstance(result, Float64Index)
else:
# raise AttributeError or TypeError
if len(index) == 0:
pass
else:
with tm.external_error_raised((TypeError, AttributeError)):
with np.errstate(all="ignore"):
func(index)
def test_numpy_ufuncs_other(index, func, request):
# test ufuncs of numpy, see:
# https://numpy.org/doc/stable/reference/ufuncs.html
if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
if func in (np.isfinite, np.isinf, np.isnan):
# numpy 1.18 changed isinf and isnan to not raise on dt64/td64
result = func(index)
assert isinstance(result, np.ndarray)
else:
with tm.external_error_raised(TypeError):
func(index)
elif isinstance(index, PeriodIndex):
with tm.external_error_raised(TypeError):
func(index)
elif isinstance(index, NumericIndex):
# Results in bool array
result = func(index)
assert isinstance(result, np.ndarray)
assert not isinstance(result, Index)
else:
if len(index) == 0:
pass
else:
with tm.external_error_raised(TypeError):
func(index)
def test_tz_localize_nonexistent(self, tz, method, exp):
# GH 8917
n = 60
dti = date_range(start="2015-03-29 02:00:00", periods=n, freq="min")
ser = Series(1, index=dti)
df = ser.to_frame()
if method == "raise":
with tm.external_error_raised(pytz.NonExistentTimeError):
dti.tz_localize(tz, nonexistent=method)
with tm.external_error_raised(pytz.NonExistentTimeError):
ser.tz_localize(tz, nonexistent=method)
with tm.external_error_raised(pytz.NonExistentTimeError):
df.tz_localize(tz, nonexistent=method)
elif exp == "invalid":
with pytest.raises(ValueError, match="argument must be one of"):
dti.tz_localize(tz, nonexistent=method)
with pytest.raises(ValueError, match="argument must be one of"):
ser.tz_localize(tz, nonexistent=method)
with pytest.raises(ValueError, match="argument must be one of"):
df.tz_localize(tz, nonexistent=method)
else:
result = ser.tz_localize(tz, nonexistent=method)
expected = Series(1, index=DatetimeIndex([exp] * n, tz=tz))
tm.assert_series_equal(result, expected)
result = df.tz_localize(tz, nonexistent=method)
expected = expected.to_frame()
tm.assert_frame_equal(result, expected)
def test_numpy_ufuncs_basic(index, func):
# test ufuncs of numpy, see:
# https://numpy.org/doc/stable/reference/ufuncs.html
if isinstance(index, DatetimeIndexOpsMixin):
with tm.external_error_raised((TypeError, AttributeError)):
with np.errstate(all="ignore"):
func(index)
elif isinstance(index,
NumericIndex) or (not isinstance(index.dtype, np.dtype)
and index.dtype._is_numeric):
# coerces to float (e.g. np.sin)
with np.errstate(all="ignore"):
result = func(index)
exp = Index(func(index.values), name=index.name)
tm.assert_index_equal(result, exp)
if type(index) is not Index:
# i.e NumericIndex
assert isinstance(result, Float64Index)
else:
# e.g. np.exp with Int64 -> Float64
assert type(result) is Index
else:
# raise AttributeError or TypeError
if len(index) == 0:
pass
else:
with tm.external_error_raised((TypeError, AttributeError)):
with np.errstate(all="ignore"):
func(index)
def test_numpy_ufuncs_other(index, func, request):
# test ufuncs of numpy, see:
# https://numpy.org/doc/stable/reference/ufuncs.html
if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
if (isinstance(index, DatetimeIndex) and index.tz is not None
and func in [np.isfinite, np.isnan, np.isinf]):
mark = pytest.mark.xfail(reason="__array_ufunc__ is not defined")
request.node.add_marker(mark)
if func in (np.isfinite, np.isinf, np.isnan):
# numpy 1.18 changed isinf and isnan to not raise on dt64/tfd64
result = func(index)
assert isinstance(result, np.ndarray)
else:
with tm.external_error_raised(TypeError):
func(index)
elif isinstance(index, PeriodIndex):
with tm.external_error_raised(TypeError):
func(index)
elif isinstance(index, NumericIndex):
# Results in bool array
result = func(index)
assert isinstance(result, np.ndarray)
assert not isinstance(result, Index)
else:
if len(index) == 0:
pass
else:
with tm.external_error_raised(TypeError):
func(index)
def test_dt_round_tz_ambiguous(self, method):
# GH 18946 round near "fall back" DST
df1 = DataFrame(
[
pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True),
pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True),
pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True),
],
columns=["date"],
)
df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid")
# infer
result = getattr(df1.date.dt, method)("H", ambiguous="infer")
expected = df1["date"]
tm.assert_series_equal(result, expected)
# bool-array
result = getattr(df1.date.dt, method)("H", ambiguous=[True, False, False])
tm.assert_series_equal(result, expected)
# NaT
result = getattr(df1.date.dt, method)("H", ambiguous="NaT")
expected = df1["date"].copy()
expected.iloc[0:2] = pd.NaT
tm.assert_series_equal(result, expected)
# raise
with tm.external_error_raised(pytz.AmbiguousTimeError):
getattr(df1.date.dt, method)("H", ambiguous="raise")
def check_external_error_on_write(self, df):
# check that we are raising the exception
# on writing
with tm.external_error_raised(Exception):
with tm.ensure_clean() as path:
to_feather(df, path)
def test_series_tz_localize_ambiguous_bool(self):
# make sure that we are correctly accepting bool values as ambiguous
# GH#14402
ts = Timestamp("2015-11-01 01:00:03")
expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central")
expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central")
ser = Series([ts])
expected0 = Series([expected0])
expected1 = Series([expected1])
with tm.external_error_raised(pytz.AmbiguousTimeError):
ser.dt.tz_localize("US/Central")
result = ser.dt.tz_localize("US/Central", ambiguous=True)
tm.assert_series_equal(result, expected0)
result = ser.dt.tz_localize("US/Central", ambiguous=[True])
tm.assert_series_equal(result, expected0)
result = ser.dt.tz_localize("US/Central", ambiguous=False)
tm.assert_series_equal(result, expected1)
result = ser.dt.tz_localize("US/Central", ambiguous=[False])
tm.assert_series_equal(result, expected1)
def test_delete(self):
idx = timedelta_range(start="1 Days", periods=5, freq="D", name="idx")
# preserve freq
expected_0 = timedelta_range(start="2 Days",
periods=4,
freq="D",
name="idx")
expected_4 = timedelta_range(start="1 Days",
periods=4,
freq="D",
name="idx")
# reset freq to None
expected_1 = TimedeltaIndex(["1 day", "3 day", "4 day", "5 day"],
freq=None,
name="idx")
cases = {
0: expected_0,
-5: expected_0,
-1: expected_4,
4: expected_4,
1: expected_1,
}
for n, expected in cases.items():
result = idx.delete(n)
tm.assert_index_equal(result, expected)
assert result.name == expected.name
assert result.freq == expected.freq
with tm.external_error_raised((IndexError, ValueError)):
# either depending on numpy version
idx.delete(5)
def test_numpy_ufuncs_other(index, func):
# test ufuncs of numpy, see:
# https://numpy.org/doc/stable/reference/ufuncs.html
if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
if func in (np.isfinite, np.isinf, np.isnan):
# numpy 1.18 changed isinf and isnan to not raise on dt64/td64
result = func(index)
assert isinstance(result, np.ndarray)
out = np.empty(index.shape, dtype=bool)
func(index, out=out)
tm.assert_numpy_array_equal(out, result)
else:
with tm.external_error_raised(TypeError):
func(index)
elif isinstance(index, PeriodIndex):
with tm.external_error_raised(TypeError):
func(index)
elif (isinstance(index, NumericIndex) or
(not isinstance(index.dtype, np.dtype) and index.dtype._is_numeric)
or (index.dtype.kind == "c" and func is not np.signbit)
or index.dtype == bool):
# Results in bool array
result = func(index)
if not isinstance(index.dtype, np.dtype):
# e.g. Int64 we expect to get BooleanArray back
assert isinstance(result, BooleanArray)
else:
assert isinstance(result, np.ndarray)
out = np.empty(index.shape, dtype=bool)
func(index, out=out)
if not isinstance(index.dtype, np.dtype):
tm.assert_numpy_array_equal(out, result._data)
else:
tm.assert_numpy_array_equal(out, result)
else:
if len(index) == 0:
pass
else:
with tm.external_error_raised(TypeError):
func(index)
def test_errorbar_plot(self):
s = Series(np.arange(10), name="x")
s_err = np.random.randn(10)
d_err = DataFrame(np.random.randn(10, 2),
index=s.index,
columns=["x", "y"])
# test line and bar plots
kinds = ["line", "bar"]
for kind in kinds:
ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
self._check_has_errorbars(ax, xerr=1, yerr=1)
ax = _check_plot_works(s.plot, xerr=s_err)
self._check_has_errorbars(ax, xerr=1, yerr=0)
# test time series plotting
ix = date_range("1/1/2000", "1/1/2001", freq="M")
ts = Series(np.arange(12), index=ix, name="x")
ts_err = Series(np.random.randn(12), index=ix)
td_err = DataFrame(np.random.randn(12, 2),
index=ix,
columns=["x", "y"])
ax = _check_plot_works(ts.plot, yerr=ts_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(ts.plot, yerr=td_err)
self._check_has_errorbars(ax, xerr=0, yerr=1)
# check incorrect lengths and types
with tm.external_error_raised(ValueError):
s.plot(yerr=np.arange(11))
s_err = ["zzz"] * 10
with tm.external_error_raised(TypeError):
s.plot(yerr=s_err)
def test_read_expands_user_home_dir(
self, reader, module, error_class, fn_ext, monkeypatch
):
pytest.importorskip(module)
path = os.path.join("~", "does_not_exist." + fn_ext)
monkeypatch.setattr(icom, "_expand_user", lambda x: os.path.join("foo", x))
with tm.external_error_raised(error_class):
reader(path)
def test_unsupported_float16_cleanup(self, pa, path_type):
# #44847, #44914
# Not able to write float 16 column using pyarrow.
# Tests cleanup by pyarrow in case of an error
data = np.arange(2, 10, dtype=np.float16)
df = pd.DataFrame(data=data, columns=["fp16"])
with tm.ensure_clean() as path_str:
path = path_type(path_str)
with tm.external_error_raised(pyarrow.ArrowException):
df.to_parquet(path=path, engine=pa)
assert not os.path.isfile(path)
def test_delete(self):
ci = self.create_index()
categories = ci.categories
result = ci.delete(0)
expected = CategoricalIndex(list("abbca"), categories=categories)
tm.assert_index_equal(result, expected, exact=True)
result = ci.delete(-1)
expected = CategoricalIndex(list("aabbc"), categories=categories)
tm.assert_index_equal(result, expected, exact=True)
with tm.external_error_raised((IndexError, ValueError)):
# Either depending on NumPy version
ci.delete(10)
def test_rolling_apply_with_pandas_objects(window):
# 5071
df = DataFrame(
{
"A": np.random.randn(5),
"B": np.random.randint(0, 10, size=5)
},
index=date_range("20130101", periods=5, freq="s"),
)
# we have an equal spaced timeseries index
# so simulate removing the first period
def f(x):
if x.index[0] == df.index[0]:
return np.nan
return x.iloc[-1]
result = df.rolling(window).apply(f, raw=False)
expected = df.iloc[2:].reindex_like(df)
tm.assert_frame_equal(result, expected)
with tm.external_error_raised(AttributeError):
df.rolling(window).apply(f, raw=True)
def check_external_error_on_write(self, df, engine, exc):
# check that an external library is raising the exception on writing
with tm.ensure_clean() as path:
with tm.external_error_raised(exc):
to_parquet(df, path, engine, compression=None)
class TestToGBQIntegrationWithServiceAccountKeyPath:
@pytest.fixture()
def gbq_dataset(self):
# Setup Dataset
_skip_if_no_project_id()
_skip_if_no_private_key_path()
dataset_id = "pydata_pandas_bq_testing_" + generate_rand_str()
self.client = _get_client()
self.dataset = self.client.dataset(dataset_id)
# Create the dataset
self.client.create_dataset(bigquery.Dataset(self.dataset))
table_name = generate_rand_str()
destination_table = f"{dataset_id}.{table_name}"
yield destination_table
# Teardown Dataset
self.client.delete_dataset(self.dataset, delete_contents=True)
def test_roundtrip(self, gbq_dataset):
destination_table = gbq_dataset
test_size = 20001
df = make_mixed_dataframe_v2(test_size)
df.to_gbq(
destination_table,
_get_project_id(),
chunksize=None,
credentials=_get_credentials(),
)
result = pd.read_gbq(
f"SELECT COUNT(*) AS num_rows FROM {destination_table}",
project_id=_get_project_id(),
credentials=_get_credentials(),
dialect="standard",
)
assert result["num_rows"][0] == test_size
@pytest.mark.parametrize(
"if_exists, expected_num_rows, expectation",
[
("append", 300, does_not_raise()),
("fail", 200,
tm.external_error_raised(pandas_gbq.gbq.TableCreationError)),
("replace", 100, does_not_raise()),
],
)
def test_gbq_if_exists(self, if_exists, expected_num_rows, expectation,
gbq_dataset):
# GH 29598
destination_table = gbq_dataset
test_size = 200
df = make_mixed_dataframe_v2(test_size)
df.to_gbq(
destination_table,
_get_project_id(),
chunksize=None,
credentials=_get_credentials(),
)
with expectation:
df.iloc[:100].to_gbq(
destination_table,
_get_project_id(),
if_exists=if_exists,
chunksize=None,
credentials=_get_credentials(),
)
result = pd.read_gbq(
f"SELECT COUNT(*) AS num_rows FROM {destination_table}",
project_id=_get_project_id(),
credentials=_get_credentials(),
dialect="standard",
)
assert result["num_rows"][0] == expected_num_rows
def test_grouped_hist_legacy(self):
from matplotlib.patches import Rectangle
from pandas.plotting._matplotlib.hist import _grouped_hist
df = DataFrame(np.random.randn(500, 1), columns=["A"])
df["B"] = to_datetime(
np.random.randint(
self.start_date_to_int64,
self.end_date_to_int64,
size=500,
dtype=np.int64,
)
)
df["C"] = np.random.randint(0, 4, 500)
df["D"] = ["X"] * 500
axes = _grouped_hist(df.A, by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
axes = df.hist(by=df.C)
self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
tm.close()
# group by a key with single value
axes = df.hist(by="D", rot=30)
self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
self._check_ticks_props(axes, xrot=30)
tm.close()
# make sure kwargs to hist are handled
xf, yf = 20, 18
xrot, yrot = 30, 40
axes = _grouped_hist(
df.A,
by=df.C,
cumulative=True,
bins=4,
xlabelsize=xf,
xrot=xrot,
ylabelsize=yf,
yrot=yrot,
density=True,
)
# height of last bin (index 5) must be 1.0
for ax in axes.ravel():
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
height = rects[-1].get_height()
tm.assert_almost_equal(height, 1.0)
self._check_ticks_props(
axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
)
tm.close()
axes = _grouped_hist(df.A, by=df.C, log=True)
# scale of y must be 'log'
self._check_ax_scales(axes, yaxis="log")
tm.close()
# propagate attr exception from matplotlib.Axes.hist
with tm.external_error_raised(AttributeError):
_grouped_hist(df.A, by=df.C, foo="bar")
msg = "Specify figure size by tuple instead"
with pytest.raises(ValueError, match=msg):
df.hist(by="C", figsize="default")
def test_hist_df_legacy(self):
from matplotlib.patches import Rectangle
with tm.assert_produces_warning(UserWarning):
_check_plot_works(self.hist_df.hist)
# make sure layout is handled
df = DataFrame(np.random.randn(100, 2))
df[2] = to_datetime(
np.random.randint(
self.start_date_to_int64,
self.end_date_to_int64,
size=100,
dtype=np.int64,
)
)
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, grid=False)
self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
assert not axes[1, 1].get_visible()
_check_plot_works(df[[2]].hist)
df = DataFrame(np.random.randn(100, 1))
_check_plot_works(df.hist)
# make sure layout is handled
df = DataFrame(np.random.randn(100, 5))
df[5] = to_datetime(
np.random.randint(
self.start_date_to_int64,
self.end_date_to_int64,
size=100,
dtype=np.int64,
)
)
with tm.assert_produces_warning(UserWarning):
axes = _check_plot_works(df.hist, layout=(4, 2))
self._check_axes_shape(axes, axes_num=6, layout=(4, 2))
# make sure sharex, sharey is handled
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.hist, sharex=True, sharey=True)
# handle figsize arg
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.hist, figsize=(8, 10))
# check bins argument
with tm.assert_produces_warning(UserWarning):
_check_plot_works(df.hist, bins=5)
# make sure xlabelsize and xrot are handled
ser = df[0]
xf, yf = 20, 18
xrot, yrot = 30, 40
axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
self._check_ticks_props(
axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
)
xf, yf = 20, 18
xrot, yrot = 30, 40
axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
self._check_ticks_props(
axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
)
tm.close()
ax = ser.hist(cumulative=True, bins=4, density=True)
# height of last bin (index 5) must be 1.0
rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
tm.assert_almost_equal(rects[-1].get_height(), 1.0)
tm.close()
ax = ser.hist(log=True)
# scale of y must be 'log'
self._check_ax_scales(ax, yaxis="log")
tm.close()
# propagate attr exception from matplotlib.Axes.hist
with tm.external_error_raised(AttributeError):
ser.hist(foo="bar")
def test_sub(left_array, right_array):
with tm.external_error_raised(TypeError):
# numpy points to ^ operator or logical_xor function instead
left_array - right_array
def test_gcs_not_present_exception():
with tm.external_error_raised(ImportError):
read_csv("gs://test/test.csv")
def test_read_non_existant(self, reader, module, error_class, fn_ext):
pytest.importorskip(module)
path = os.path.join(HERE, "data", "does_not_exist." + fn_ext)
with tm.external_error_raised(error_class):
reader(path)
def test_external_error_raised():
with tm.external_error_raised(TypeError):
raise TypeError("Should not check this error message, so it will pass")
