Bug report

Bug summary

Generating np.random.randn(1000) values, visualizing them with plt.hist(). Works fine with Numpy.

When I replace Numpy with tensorflow.experimental.numpy, Matplotlib 3.3.4 fails to display the histogram correctly. Matplotlib 3.2.2 works fine.

Code for reproduction

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow.experimental.numpy as tnp

# bad image
labels1 = 15 + 2 * tnp.random.randn(1000)
_ = plt.hist(labels1)

# good image
labels2 = 15 + 2 * np.random.randn(1000)
_ = plt.hist(labels2)

Actual outcome

Expected outcome

Matplotlib version

• Operating system: Windows 10
• Matplotlib version (import matplotlib; print(matplotlib.__version__)): 3.3.4
• Matplotlib backend (print(matplotlib.get_backend())): module://ipykernel.pylab.backend_inline
• Python version: 3.8.7
• Jupyter version (if applicable): see below
• Other libraries: see below

TensorFlow 2.4.1

jupyter --version
jupyter core     : 4.7.0
jupyter-notebook : 6.1.6
qtconsole        : 5.0.1
ipython          : 7.20.0
ipykernel        : 5.4.2
jupyter client   : 6.1.7
jupyter lab      : not installed
nbconvert        : 6.0.7
ipywidgets       : 7.6.3
nbformat         : 5.0.8
traitlets        : 5.0.5

Python installed from python.org as an exe installer. Everything else is pip install --user

Bug opened with TensorFlow on this same issue:

tensorflow/tensorflow#46274