When running any visualization the kernel dies and I get the message:

"The kernel appears to have died. It will restart automatically."

After restarting the kernel an hourglass continues to display on the Jupyter tab. This is true for Seaborn and Matplotlib. It does not matter the type of plot or size of data. I can execute any other function.

I am on Windows 10. Any example that results in failure is below. It does not matter if I get the data from a csv or generate it with numpy.

```
import numpy as np
# for the first data set define x and the number of bins
x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100]
num_bins = 5
# for the second data set define x as a distribution with mean 100 and sd of 15
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x_2 = mu + sigma * np.random.randn(10000)
num_bins_2 = 20
import matplotlib.pyplot as plt
# use the "hist" function to create the histogram in the default Figure and Axes
plt.hist(x, num_bins, facecolor='blue', alpha=0.5)
# Add in the title
plt.title('Simple Histogram using MatplotLib')
# Add in the labels
plt.xlabel('x-axis')
plt.ylabel('y-axis')
# display the graph
plt.show()
```

### >Solution :

This might be related to one of the dependencies being updated.

Have you seen this?

After conda update, python kernel crashes when matplotlib is used