A compendium of useful, interesting, inspirational usages of Python Pandas library
Let’s talk about the Pandas package.
When you browse through Stackoverflow or reading blogs on Toward Data Science, have you ever encountered some super elegant solutions (maybe just one line) that can replace your dozens of lines codes (for loop, functions)?
I encountered that kind of situation a lot, and I was often like, “Wow, I didn’t know this function can be used in this way, TRULY amazing!” Different people will have different excitement point for sure, but I bet these moments have occurred to your before if you ever work in the applied data science field.
However, one thing that puzzles me is that there’s not a place or repository to store and record these inspirational moments and the associated real-world examples. That’s the reason why I want to take the initiative to construct a GitHub repository just focusing on collecting these interesting/impressive usages/examples specifically in the Pandas library that makes you want to shout out!
Here’s the link to the repository:
Now I will show you two concrete examples that happen in my life and why I think having a repository like this would be helpful.
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.
Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.
This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.
The outline of this post include:
We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)
The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.
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