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Fix Axes.hist crash for numpy timedelta64 inputs #31203

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Merged
timhoffm merged 10 commits into from
Mar 11, 2026
+24 −0
Merged

Fix Axes.hist crash for numpy timedelta64 inputs #31203

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2275f59
Fix Axes.hist crash for numpy timedelta64 inputs
jayaprajapatii Feb 26, 2026
bb8ed97
Raise explicit TypeError for numpy.timedelta64 inputs in Axes.hist
jayaprajapatii Mar 2, 2026
df468b2
remove duplicate pytest import
jayaprajapatii Mar 2, 2026
5fa7cc0
raise TypeError for timedelta64 in Axes.hist and remove bin logic cha…
jayaprajapatii Mar 3, 2026
4d257c6
Handle numpy.timedelta64 and datetime.timedelta explicitly
jayaprajapatii Mar 3, 2026
6c45d7c
fix object check and formatting
jayaprajapatii Mar 5, 2026
62d0716
merge timedelta checks and added datetime.timedelta regression tests
jayaprajapatii Mar 6, 2026
ea2b96d
merge timedelta checks and added datetime.timedelta regression test
jayaprajapatii Mar 6, 2026
432f787
fix python style
jayaprajapatii Mar 6, 2026
c07a991
Fix indentation
timhoffm Mar 6, 2026
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26 changes: 15 additions & 11 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7416,7 +7416,14 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,

# Massage 'x' for processing.
x = cbook._reshape_2D(x, 'x')
nx = len(x) # number of datasets
nx = len(x)
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for arr in x:
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@timhoffm timhoffm Mar 2, 2026

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Overall, I'm not in favor of checking for all kinds of erronous inputs. This is not good, but it's possibly the least bad option (other than making timedelta work properlyI) because timedelta is an accepted input in other cases.

if hasattr(arr, "dtype") and np.issubdtype(arr.dtype, np.timedelta64):
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raise TypeError(
"ax.hist does not currently support numpy.timedelta64."
"Convert to numeric values (e.g., .total_seconds()) first."
)

# Process unit information. _process_unit_info sets the unit and
# converts the first dataset; then we convert each following dataset
Expand Down Expand Up @@ -7470,16 +7477,13 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
# does not do this for us when guessing the range (but will
# happily ignore nans when computing the histogram).
if bin_range is None:
xmin = np.inf
xmax = -np.inf
for xi in x:
if len(xi):
# python's min/max ignore nan,
# np.minnan returns nan for all nan input
xmin = min(xmin, np.nanmin(xi))
xmax = max(xmax, np.nanmax(xi))
if xmin <= xmax: # Only happens if we have seen a finite value.
bin_range = (xmin, xmax)
finite_data = [xi for xi in x if len(xi)]
if finite_data:
xmin = min(np.nanmin(xi) for xi in finite_data)
xmax = max(np.nanmax(xi) for xi in finite_data)
bin_range = (xmin, xmax)
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else:
bin_range = None
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# If bins are not specified either explicitly or via range,
# we need to figure out the range required for all datasets,
Expand Down
11 changes: 11 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -2461,6 +2461,17 @@ def test_hist_log_barstacked():
assert axs[0].get_ylim() == axs[1].get_ylim()


def test_hist_timedelta_raises():
import numpy as np
import matplotlib.pyplot as plt

arr = np.array([1,2,5,7], dtype="timedelta64[D]")
fig, ax = plt.subplots()

with pytest.raises(TypeError, match="does not currently support numpy.timedelta64"):
ax.hist(arr)

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@image_comparison(['hist_bar_empty.png'], remove_text=True)
def test_hist_bar_empty():
# From #3886: creating hist from empty dataset raises ValueError
Expand Down
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