Bug report

Bug summary

Generatingnp.random.randn(1000) values, visualizing them withplt.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

importmatplotlib.pyplotaspltimportnumpyasnpimporttensorflowastfimporttensorflow.experimental.numpyastnp# bad imagelabels1=15+2*tnp.random.randn(1000)_=plt.hist(labels1)# good imagelabels2=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 --versionjupyter core     : 4.7.0jupyter-notebook : 6.1.6qtconsole        : 5.0.1ipython          : 7.20.0ipykernel        : 5.4.2jupyter client   : 6.1.7jupyter lab      : not installednbconvert        : 6.0.7ipywidgets       : 7.6.3nbformat         : 5.0.8traitlets        : 5.0.5

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

Bug opened with TensorFlow on this same issue:

tensorflow/tensorflow#46274