Why numpy matrix multiplication gives wrong shape (and how to fix it)

Inconsistent matrix dimensions in numpy matrix multiplication usually appear in production datasets from APIs or logs, where matrices are dynamically generated. This results from incompatible dimensions, silently breaking downstream computations.

Quick Answer

Numpy matrix multiplication gives wrong shape when input matrices have incompatible dimensions. Fix by validating matrix shapes before multiplication.

TL;DR

  • Numpy matrix multiplication requires compatible dimensions
  • Validate matrix shapes before multiplication
  • Use np.matmul() or @ operator for clarity

Problem Example

import numpy as np

# Define matrices with incompatible dimensions
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[5, 6], [7, 8], [9, 10]])

# Attempt matrix multiplication
try:
    result = np.matmul(mat1, mat2)
    print(result)
except ValueError as e:
    print(e)
# Output: matrices are not aligned

Root Cause Analysis

Incompatible matrix dimensions cause numpy to raise a ValueError. This stems from numpy enforcing matrix multiplication rules, identical to linear algebra standards. Related factors:

  • Number of columns in the first matrix does not match the number of rows in the second
  • Using np.matmul() or the @ operator without validating matrix shapes
  • Insufficient error handling for matrix multiplication

How to Detect This Issue

# Check matrix dimensions before multiplication
mat1_rows, mat1_cols = mat1.shape
mat2_rows, mat2_cols = mat2.shape
if mat1_cols != mat2_rows:
    print('Matrix dimensions are incompatible')

Solutions

Solution 1: Validate matrix dimensions

if mat1.shape[1] == mat2.shape[0]:
    result = np.matmul(mat1, mat2)
else:
    print('Matrix dimensions are incompatible')

Solution 2: Use try-except block

try:
    result = np.matmul(mat1, mat2)
except ValueError as e:
    print(e)

Solution 3: Adjust matrix dimensions

# Example: transpose the second matrix if necessary
if mat1.shape[1] == mat2.shape[0]:
    result = np.matmul(mat1, mat2)
else:
    result = np.matmul(mat1, mat2.T)

Why validate Parameter Fails

Using numpy’s matrix multiplication functions will raise a ValueError when matrix dimensions are incompatible. This is not a bug — it is numpy enforcing linear algebra standards to prevent incorrect computations. If matrix dimensions need adjustment, consider transposing one of the matrices or padding with zeros.

Production-Safe Pattern

try:
    result = np.matmul(mat1, mat2)
except ValueError as e:
    print(f'Matrix multiplication failed: {e}')
    # Handle the error or adjust matrix dimensions

Wrong Fixes That Make Things Worse

❌ Ignoring the ValueError and proceeding with computations: This can lead to incorrect results

❌ Forcing matrix multiplication without validation: This can corrupt data and break downstream logic

❌ Not checking for compatibility in production code: This can cause runtime errors and crashes

Common Mistakes to Avoid

  • Not checking matrix dimensions before multiplication
  • Not handling ValueError exceptions properly
  • Assuming matrix multiplication is always possible

Frequently Asked Questions

Q: Why does numpy matrix multiplication give a ValueError?

When input matrices have incompatible dimensions, numpy raises a ValueError to enforce linear algebra standards.

Q: Is this a numpy bug?

No, this behavior follows standard linear algebra rules for matrix multiplication.

Q: How can I fix the ValueError in numpy matrix multiplication?

Validate matrix shapes before multiplication, ensuring the number of columns in the first matrix matches the number of rows in the second.

→ Fix numpy array reshape ValueError dimension mismatch → Fix numpy broadcasting shape mismatch → Fix How NumPy broadcasting aligns dimensions and avoids errors → Fix numpy broadcasting shape mismatch in array ops