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Fix check 1d #22141

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Merged
merged 3 commits into from
Mar 3, 2022
Merged

Fix check 1d #22141

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5052af5
FIX: accomodate pandas type that doesn't return numpy from .values
jklymak Jan 7, 2022
509626d
FIX: more holistic fix
jklymak Jan 27, 2022
6dfa93a
FIX: simplify a bit more
jklymak Feb 18, 2022
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44 changes: 5 additions & 39 deletions lib/matplotlib/cbook/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1300,47 +1300,13 @@ def _to_unmasked_float_array(x):

def _check_1d(x):
"""Convert scalars to 1D arrays; pass-through arrays as is."""
if hasattr(x, 'to_numpy'):
# if we are given an object that creates a numpy, we should use it...
x = x.to_numpy()
if not hasattr(x, 'shape') or len(x.shape) < 1:
return np.atleast_1d(x)
else:
try:
# work around
# https://github.com/pandas-dev/pandas/issues/27775 which
# means the shape of multi-dimensional slicing is not as
# expected. That this ever worked was an unintentional
# quirk of pandas and will raise an exception in the
# future. This slicing warns in pandas >= 1.0rc0 via
# https://github.com/pandas-dev/pandas/pull/30588
#
# < 1.0rc0 : x[:, None].ndim == 1, no warning, custom type
# >= 1.0rc1 : x[:, None].ndim == 2, warns, numpy array
# future : x[:, None] -> raises
#
# This code should correctly identify and coerce to a
# numpy array all pandas versions.
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings(
"always",
category=Warning,
message='Support for multi-dimensional indexing')

ndim = x[:, None].ndim
# we have definitely hit a pandas index or series object
# cast to a numpy array.
if len(w) > 0:
return np.asanyarray(x)
# We have likely hit a pandas object, or at least
# something where 2D slicing does not result in a 2D
# object.
if ndim < 2:
return np.atleast_1d(x)
return x
# In pandas 1.1.0, multidimensional indexing leads to an
# AssertionError for some Series objects, but should be
# IndexError as described in
# https://github.com/pandas-dev/pandas/issues/35527
except (AssertionError, IndexError, TypeError):
return np.atleast_1d(x)
return x


def _reshape_2D(X, name):
Expand Down Expand Up @@ -1649,7 +1615,7 @@ def index_of(y):
The x and y values to plot.
"""
try:
return y.index.values, y.values
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Do we need both this change and the additional exception handling above?

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I don't know, but I think we should be in the habit of using to_numpy() when possible?

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to_numpy came in via pandan 0.24 on Jan 25, 2019 so we can rely on it being there and https://stackoverflow.com/a/54508052/380231 makes an arguement in favor of no_numpy.

I think this change may be un-related (or fix this by chance) but I do not think it will avoid needing the other one and does no harm.

return y.index.to_numpy(), y.to_numpy()
except AttributeError:
pass
try:
Expand Down
14 changes: 9 additions & 5 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -1749,11 +1749,15 @@ def test_bar_hatches(fig_test, fig_ref):

def test_pandas_minimal_plot(pd):
# smoke test that series and index objcets do not warn
x = pd.Series([1, 2], dtype="float64")
plt.plot(x, x)
plt.plot(x.index, x)
plt.plot(x)
plt.plot(x.index)
for x in [pd.Series([1, 2], dtype="float64"),
pd.Series([1, 2], dtype="Float64")]:
plt.plot(x, x)
plt.plot(x.index, x)
plt.plot(x)
plt.plot(x.index)
df = pd.DataFrame({'col': [1, 2, 3]})
plt.plot(df)
plt.plot(df, df)


@image_comparison(['hist_log'], remove_text=True)
Expand Down