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.
Flatten matrix to vector
Read rows left-to-right, top-to-bottom.
Square each element
Return element-wise squares.
Absolute value per element
Return element-wise absolute values.
Keep positive elements
Return values strictly greater than zero.
Prefix sum vector
Return running totals for each prefix of the vector.
Element-wise multiply
Multiply corresponding elements.
Count non-zero entries
Count elements not equal to zero.
Reverse vector order
Return elements in opposite order.
Transpose a matrix
Swap rows and columns for a rectangular matrix.
L2 norm of vector
Return Euclidean length.
Index of maximum value
Return index of largest value (first on tie).
Add scalar to vector
Add constant k to every element of the vector.
Inclusive numeric range vector
Return [start, start+step, ...] through end inclusive.
Count elements in matrix
Return total scalar count.
Showing 1–24 of 25 challenges · Numpy & Vectorized Computing
Numpy & Vectorized Computing — Python coding challenges
What you will find here
This page lists numpy & vectorized computing challenges — real Python problems you solve in the browser IDE with instant test feedback. Each challenge includes a clear brief, starter code, and automated checks.
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.