Question 1

How does the algorithms library compare to using Python's built-in functions like sorted or heapq?

Accepted Answer

This library provides educational implementations to understand algorithm internals, whereas built-in functions are optimized for performance. It's better for learning, but for production, built-ins or specialized libraries are often faster.

Question 2

How to contribute to the algorithms project on GitHub?

Accepted Answer

Check the CONTRIBUTING.md file for guidelines. You can submit pull requests, report issues, or help with code triage via the CodeTriage badge mentioned in the README.

Question 3

Is this library good for preparing for coding interviews?

Accepted Answer

Yes, it's excellent for interview prep because it includes clean, documented implementations with complexity analysis, covering common topics like sorting, dynamic programming, and graph algorithms.

Question 4

How often is the algorithms library updated with new algorithms?

Accepted Answer

Updates depend on community contributions and the maintainer's activity. Since it's open-source, new algorithms are added as pull requests are merged, but there's no fixed schedule.

Question 5

Can I use the algorithms library in commercial projects?

Accepted Answer

Yes, it's licensed under MIT, which allows free use, modification, and distribution in commercial software, as stated in the LICENSE file.

Question 6

How to install and use the merge sort algorithm from the library?

Accepted Answer

Install via pip with 'pip install algorithms', then import it: 'from algorithms.sorting import merge_sort'. Use it as shown in the README example with a list input.

algorithms

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