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How to improve a slow Python script?

I’ve run a Python script to collect statistics in a table. The script works with a.xlsx Excel file where tables are stored. The script is slow and I would like to speed it up. My first attempt was to use Pandas. Pandas runs well but the data I want to manipulate is not in tables, but in graphs.
So I’ve used numpy. Savefig can save the images but the problem is that the script has to wait for each plot to be plotted. Furthermore, once the script has finished, I’ll have to close the Python interpreter.
What I would like is a process that works as follows:

Open the file.
Plot the data in all the x and y axes.
Save the image in the.png format.
Close the Python interpreter.

Would you know of a solution?

A:

Some thoughts:

Depending on the size of your data, you could try to parallelize the calculation. For this you could look for a library that runs processes in parallel. In your case, I would consider using the multiprocessing library.
I would suggest wrapping you program in a class. That way you can easily switch between configurations or add new functionality.
It is hard to tell the difference between data in tables and data in graphs. Maybe you could save your plot as a.png and save the corresponding CSV file.

A couple of resources that may help:

Python Parallel Programming Cookbook:

S. Arora and N. Bhattacharya, High Performance Scientific Python, Manning Publications, 2011.

A:

If you are using a.xlsx file, would be better to convert it to.csv and then create the graphs in open office.
At the end, use bpy to save the graphs as.png

This would be my plan of attack as long as the library I was using couldn’t open.xlsx files natively…
In case

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