Python 2D Graph project (p2go)

Welcome to the "Python 2D Graphs" (p2go) project. It is a hackable, step-by-step for creating a 2D graph Python-object.

I classify the "p2go" as a "sandbox" or "toy" project. In other words, it is a fun, experimental project focusing on solving one problem. The problem is I spend a lot of time studying data, e.g., images, text, and audio. I am fatigued from using other people's graphs or library packages, e.g., Bokeh or Ggplot2. I want to build my own using "numpy" and "matplotlib."

I intend to create more charts and use "p2go" to plot graphs that are not in books, whitepapers, or blogs. It doesn't matter if the benefits are immediately apparent, such as why satisfy drawing the "Imagenet Cosine Proximity" chart like everyone else. Why not graph the "Image Tangent Proximity" chart or throw in the Softmax function before creating the graph?

The salient point is, why not do it yourself. You can start with a fun sandbox project, learn the basics, and improve your original-thinking rather than memorizing terminology and regurgitating how other people are doing it.

So if you are ready, let's take a collective calming breath … … and begin.

2 - The Journey

• As a good little programmer, we start by creating an object or class.

• When importing "numpy" and "matplotlib," my programing style is NOT using the global-space as in "import numpy *" NOR using the shorten name like "import matplotlibl.pyplot as ptl." I am NOT using Python's syntax shortcut because I'm switching between Python, Javascripts, and Swift.

• I use a notebook to interactively writing the code, and afterward, I copy the code into a Python project using Atom IDE. https://atom.io/

The journey continues in the Jupyter notebook.

• https://github.com/duchaba/python_graphs_p2go/blob/master/Python_Graphs_p2go.ipynb

Sample output-graphs from the notebook are as follows.

3 - Conclusion

I love spending time doing fun "sandbox" or "toy" projects. I can focus on one problem, and Jupyter notebooks make it easy to document the journey and share it with my friends, both real and virtual.

After leaving the rules-based expert-systems behind when I left Xerox PARC in my early youth, I am delighted to return to AI. It is due in large part by Jeremy Howard, Rachel Thomas, and Sylvain Gugger's courses. Their style of demystifying AI using code, and yet not dummy down, is one of the best I have learned. https://fast.ai

• If you read this on Jupyter notebook, I hope you have hacked it and created your specialized drawing-graph companion. The one request is you heading over to LinkedIn, giving a thumbs-up, and sending me a message.

• https://www.linkedin.com/pulse/demystify-python-charts-hackable-step-by-step-jupyter-duc-haba

• If you read this on LinkedIn, what are you waiting for? Heading over to Github, using Google Collab or your favorite Jupyter notebook option, and hacking away.

• https://github.com/duchaba/python_graphs_p2go/blob/master/Python_Graphs_p2go.ipynb