ravel() collapses a multi-dimensional array into a single 1D sequence, reading elements in the given order — 'C' order, the default, reads row by row, while 'F', Fortran, order reads column by column. Because it prefers returning a view over a copy whenever the underlying memory layout allows it, ravel() is typically faster and more memory-efficient than ndarray.flatten(), which always returns a copy, but that also means modifying a raveled array can sometimes modify the original.
1Understanding np.ravel()
ravel() collapses a multi-dimensional array into a single 1D sequence, reading elements in the given order — 'C' order, the default, reads row by row, while 'F', Fortran, order reads column by column. Because it prefers returning a view over a copy whenever the underlying memory layout allows it, ravel() is typically faster and more memory-efficient than ndarray.flatten(), which always returns a copy, but that also means modifying a raveled array can sometimes modify the original.
Use ravel() when you just need to iterate over or read every element of an array in flattened order and don't need to guarantee an independent copy — reach for flatten() instead when you specifically need a safe, standalone copy.
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
flat = matrix.ravel()
print(flat)2Practical Example
Here is a real-world application of np.ravel() showing how it is used in production NumPy code.
import numpy as np
matrix = np.array([[1, 2], [3, 4]])
flat = matrix.ravel()
flat[0] = 99
print(matrix)3Best Practices
Follow these guidelines when working with np.ravel():
1. Use ravel() over flatten() when you don't need a guaranteed independent copy, for better performance
2. Use flatten() explicitly when you need to safely modify the flattened result without risk of also mutating the original array
3. Be explicit about order='C' vs order='F' when the array's element ordering actually matters for your calculation, rather than relying on the default
Tip: Use ravel() when you just need to iterate over or read every element of an array in flattened order and don't need to guarantee an independent copy — reach for flatten() instead when you specifically need a safe, standalone copy.
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
flat = matrix.ravel()
print(flat)