Why use the NumPy Array Reshaping? explain with example.
Why use the NumPy Array Reshaping?
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12-Nov-2025
Updated on 27-Nov-2025
Anubhav Kumar
27-Nov-2025Below is a clear explanation:
1. To Prepare Data for Machine Learning Models
Most ML algorithms expect input in a certain shape.
Example
Neural networks expect:
(#samples, #features)CNNs expect:
(#samples, height, width, channels)If you have a flat array of 784 pixel values from a 28×28 image:
or for ML:
2. To Convert 1D Data into 2D or 3D Structures
Often data comes in a single dimension but represents a grid or matrix.
Example
Convert this:
To a 2×3 matrix:
3. To Merge or Split Data Without Copying
Reshaping reinterprets the same data in memory; it's very fast.
Example
Flatten 2D to 1D:
4. To Add or Remove Dimensions
Useful for batch processing or broadcasting.
Add a new axis:
Remove unnecessary axes:
5. For Matrix Operations
Linear algebra needs specific shapes.
Example
Matrix multiplication requires 2D arrays:
6. To Combine Multiple Features
If you have multiple columns stored as rows:
7. To Work Efficiently With Broadcasting
Broadcasting rules depend on array shape.
Example
Make a row vector:
Make a column vector:
Summary Table