Cosine Similarity of Vectors
Compute the cosine similarity between two equal-length numeric vectors using pure Python.
One Hot Encode Labels
Given a list of categorical labels, return a list of one-hot encoded vectors based on the sort order of unique labels.
Dot Product of Vectors
Implement a function that returns the dot product of two numeric vectors.
Element-wise vector addition
Add equal-length numeric lists element-wise.
Scale a vector by scalar
Multiply every element by k.
Dot product of two vectors
Return sum of element-wise products.
Mean of a vector
Return arithmetic mean or 0.0 when empty.
Maximum element in vector
Return largest value or None when empty.
Minimum element in vector
Return smallest value or None when empty.
Clip vector to range
Clamp each value to [lo, hi].
Min-max normalize vector
Scale values to 0..1; equal values become all 0.0.
Sum each matrix row
Given rows, return each row sum.
Sum each matrix column
Return column totals for a rectangular matrix.
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Guide: free Python coding challenges
Practice Python by solving problems
PythonSkillset challenges are hands-on coding exercises from beginner to advanced. Open a challenge, read the problem, write Python in the split-pane editor, and run tests with Pyodide — no install required.
How to use the arena
- Pick a category — basics, algorithms, strings, and more
- Open a challenge, read the statement, and edit the starter code
- Run tests, fix failures, then try a related quiz or tutorial lesson
Challenges vs tutorials and quizzes
Challenges test what you can build under constraints. For guided teaching, use our Python tutorials. For quick checks, try quizzes or copy snippets from code samples.