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FIX: fix cleanup warnings for errorbar timeseries #36982

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Merged
merged 6 commits into from
Oct 10, 2020
Merged

FIX: fix cleanup warnings for errorbar timeseries #36982

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1c04f3c
FIX: fix cleanup warnings for errorbar timeseries
ivanovmg Oct 8, 2020
4083380
REF: refactor using assert_produces_warning
ivanovmg Oct 9, 2020
73bf910
REF: catch warning in test_errorbar_plot
ivanovmg Oct 9, 2020
d5a9e5e
REF: extract test_errorbar_plot_different_kinds
ivanovmg Oct 9, 2020
198fd1e
REF: check errorbars for list-like axes
ivanovmg Oct 9, 2020
762b9b9
FIX: fix comments
ivanovmg Oct 9, 2020
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77 changes: 52 additions & 25 deletions pandas/tests/plotting/test_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2765,35 +2765,62 @@ def test_errorbar_with_partial_columns(self):
self._check_has_errorbars(ax, xerr=0, yerr=1)

@pytest.mark.slow
def test_errorbar_timeseries(self):
@pytest.mark.parametrize("kind", ["line", "bar", "barh"])
def test_errorbar_timeseries(self, kind):
import matplotlib.pyplot as plt

with warnings.catch_warnings():
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}

# check time-series plots
ix = date_range("1/1/2000", "1/1/2001", freq="M")
tdf = DataFrame(d, index=ix)
tdf_err = DataFrame(d_err, index=ix)
# check time-series plots
ix = date_range("1/1/2000", "1/1/2001", freq="M")
tdf = DataFrame(d, index=ix)
tdf_err = DataFrame(d_err, index=ix)

kinds = ["line", "bar", "barh"]
for kind in kinds:
ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(tdf.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(tdf.plot, y="y", yerr=tdf_err["x"], kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(tdf.plot, y="y", yerr="x", kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)
ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)
ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)

# _check_plot_works adds an ax so catch warning. see GH #13188
axes = _check_plot_works(
tdf.plot, kind=kind, yerr=tdf_err, subplots=True
)
self._check_has_errorbars(axes, xerr=0, yerr=1)
ax = _check_plot_works(tdf.plot, yerr=d_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)

ax = _check_plot_works(tdf.plot, y="y", yerr=tdf_err["x"], kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)

ax = _check_plot_works(tdf.plot, y="y", yerr="x", kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=1)

ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
self._check_has_errorbars(ax, xerr=0, yerr=2)

# Do not use _check_plot_works as this function creates
# subplots inside, which led to uncaught warnings like this:
# UserWarning: To output multiple subplots,
# the figure containing the passed axes is being cleared
tdf.plot(kind=kind, yerr=tdf_err, subplots=True)
fig = plt.gcf()
axes = fig.get_axes()
for ax in axes:
self._check_has_errorbars(ax, xerr=0, yerr=1)

@pytest.mark.slow
@pytest.mark.parametrize("kind", ["line", "bar", "barh"])
def test_errorbar_plotting_twice_on_same_axis_warns(self, kind):
d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}

ix = date_range("1/1/2000", "1/1/2001", freq="M")
tdf = DataFrame(d, index=ix)
tdf_err = DataFrame(d_err, index=ix)

with warnings.catch_warnings(record=True) as w:
# _check_plot_works creates subplots inside,
# thus the warning about overwriting must be raised
axes = _check_plot_works(tdf.plot, kind=kind, yerr=tdf_err, subplots=True)
self._check_has_errorbars(axes, xerr=0, yerr=1)
assert len(w) == 1
assert issubclass(w[0].category, UserWarning)
msg = "the figure containing the passed axes is being cleared"
assert msg in str(w[0].message)

def test_errorbar_asymmetrical(self):

Expand Down