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Apply unit conversion to hist2d's x/y/range, fixing datetime input #32309

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Apply unit conversion to hist2d's x/y/range, fixing datetime input #32309

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5bdf851
Apply unit conversion to hist2d's x/y/range, fixing datetime input
AdityaJagtap18 Sep 6, 2026
5a7348e
Test user-facing hist2d output instead of internal date representation
AdityaJagtap18 Sep 7, 2026
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11 changes: 11 additions & 0 deletions doc/api/next_api_changes/behavior/hist2d_unit_conversion.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,11 @@
``Axes.hist2d`` now converts *x*/*y*/*range* through the axis unit converters
------------------------------------------------------------------------------

`~matplotlib.axes.Axes.hist2d` previously passed *x* and *y* directly to
`numpy.histogram2d` without applying the axes' unit converters, unlike
`~.Axes.plot`, `~.Axes.scatter`, and `~.Axes.hist`. This meant that, e.g.,
plotting ``datetime64`` data with `~.Axes.hist2d` produced bin edges in raw
(often nanosecond-scale) units instead of Matplotlib's internal date
representation, so the histogram did not line up with other artists plotted
on the same Axes. ``x``, ``y``, and ``range`` (if passed) are now converted
consistently with the other plotting methods.
12 changes: 12 additions & 0 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7992,8 +7992,20 @@ def hist2d(self, x, y, bins=10, range=None, density=False, weights=None,
Previously, `~.Axes.hist2d` would force the axes limits to match the
extents of the histogram; now, autoscaling also takes other plot
elements into account.

.. versionchanged:: 3.12
*x* and *y* (and *range*, if given) are now passed through the axes'
unit converters before binning, so e.g. datetime inputs are handled
the same way as in `~.Axes.plot` and `~.Axes.scatter`. Previously
they were passed unconverted to `numpy.histogram2d`.
"""

x, y = self._process_unit_info([("x", x), ("y", y)], kwargs)
if range is not None:
(xmin, xmax), (ymin, ymax) = range
range = (self.convert_xunits((xmin, xmax)),
self.convert_yunits((ymin, ymax)))

h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range,
density=density, weights=weights)

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40 changes: 40 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -2919,6 +2919,46 @@ def test_hist2d_autolimits():
assert ax.get_autoscale_on() # Autolimits have not been disabled.


def test_hist2d_datetime():
# Regression test for gh-17319: hist2d's returned bin edges for
# datetime input should be on the same numeric scale as
# matplotlib.dates.date2num (i.e. equivalent to converting the dates
# yourself and calling np.histogram2d directly), not raw
# (nanosecond-scale) datetime64 integers.
x = np.arange(np.datetime64('2020-01-01'), np.datetime64('2020-01-11'))
y = np.arange(10.)

ax = plt.figure().add_subplot()
h, xedges, yedges, pc = ax.hist2d(x, y, bins=5)

expected_h, expected_xedges, expected_yedges = np.histogram2d(
mdates.date2num(x), y, bins=5)
np.testing.assert_array_equal(xedges, expected_xedges)
np.testing.assert_array_equal(yedges, expected_yedges)
np.testing.assert_array_equal(h, expected_h)


def test_hist2d_datetime_range():
# The `range` parameter should be converted through the unit converters
# just like x/y, so passing datetime bounds is equivalent to manually
# converting them (e.g. via `date2num`) and passing the result.
x = np.arange(np.datetime64('2020-01-01'), np.datetime64('2020-01-11'))
y = np.arange(10.)
xlim_datetime = [np.datetime64('2020-01-01'), np.datetime64('2020-01-11')]
xlim_converted = mdates.date2num(xlim_datetime)

h_datetime, xedges_datetime, yedges_datetime, _ = (
plt.figure().add_subplot().hist2d(
x, y, bins=5, range=[xlim_datetime, [0, 10]]))
h_converted, xedges_converted, yedges_converted, _ = (
plt.figure().add_subplot().hist2d(
mdates.date2num(x), y, bins=5, range=[xlim_converted, [0, 10]]))

np.testing.assert_array_equal(xedges_datetime, xedges_converted)
np.testing.assert_array_equal(yedges_datetime, yedges_converted)
np.testing.assert_array_equal(h_datetime, h_converted)


class TestScatter:
@image_comparison(['scatter'], style='mpl20', remove_text=True)
def test_scatter_plot(self):
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