Confused to Understand the Difference of Pandas DataFrame Filtering Operation based on Lower Bound and Upper Bound

I'm currently working on some data analysis tasks using Pandas in Python, and I came across four different lines of code used to filter a DataFrame based on lower and upper limits. While I understand the basic intent of each line, I'm curious about the specific differences and implications they may have on the resulting DataFrame.

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Here are the four lines of code:

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1. \`df\_cleaned = df\[\~((df < lower\_limit) | (df > upper\_limit)).any(axis=1)\]\`

2. \`df\_cleaned2 = df\[((df >= …

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