arr.shapegives the size of each dimension, e.g.(2, 3).arr.reshape(rows, cols)returns the same data in a new shape (the total size must match).- Use
-1to let NumPy work out one dimension. arr.flatten()turns any array into 1-D.
NumPy Shape & Reshape
Change the dimensions of an array.
✍️ 3 exercises + code challenge in this lessonKey Takeaways: NumPy Shape & Reshape
Mastering NumPy Shape & Reshape helps you write clean and reliable NumPy code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
NumPy Shape & Reshape Code Example
NumPyimport numpy as np
arr = np.arange(1, 13)
print(arr.shape)
m = arr.reshape(3, 4)
print(m)
print(m.shape)
print(arr.reshape(2, -1).shape)Output
(12,) [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] (3, 4) (2, 6)
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NumPy Shape & Reshape Practice Exercises
Check what you learned in “NumPy Shape & Reshape”.
/ 3 solved
Fill in the blank
Reshape 12 numbers into 3 rows and 4 columns.
m = arr.(3, 4)
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Multiple choice
What does -1 mean in reshape(2, -1)?
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Code challenge
Reshape np.arange(6) into 2 rows and 3 columns and print the shape.
Write the code in the live editor. Your answer is checked automatically.
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