Instruction: Explain the difference between 'cut' and 'qcut', and provide scenarios where each is useful.
Context: This question tests the candidate's knowledge on data discretization techniques in Pandas, which is crucial for certain types of data analysis and feature engineering.
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'cut' Function in Pandas:
The 'cut' function is used to divide the range of the data into discrete intervals. The key aspect of 'cut' is that it allows for uniform or custom-sized binning, based on either the number of bins you specify or the bin edges that you define. This means you have full control over the size and boundaries of each bin, making 'cut' extremely useful when you have specific intervals in mind or when the data distribution is not uniform, and you need to ensure certain data points fall into designated bins....