ravel vs flatten

Both methods flatten arrays to 1D, but differ in memory behavior.

Method ravel

The ravel() method returns a view when possible.

1. Basic Usage

import numpy as np

def main():
    x = np.arange(6).reshape((3, 2))
    print(f"{x.shape = }")

    y = x.ravel()  # returns view
    print(f"{y.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (3, 2)
y.shape = (6,)

2. Memory Sharing

import numpy as np

def main():
    x = np.arange(6).reshape((3, 2))
    y = x.ravel()
    y[0] = -1
    print(f"{x[0, 0] = }")  # -1

if __name__ == "__main__":
    main()

3. View Behavior

Modifying the raveled array modifies the original.

Method flatten

The flatten() method always returns a copy.

1. Basic Usage

import numpy as np

def main():
    x = np.arange(6).reshape((3, 2))
    print(f"{x.shape = }")

    y = x.flatten()  # returns copy
    print(f"{y.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (3, 2)
y.shape = (6,)

2. Memory Independence

import numpy as np

def main():
    x = np.arange(6).reshape((3, 2))
    y = x.flatten()
    y[0] = -1
    print(f"{x[0, 0] = }")  # 0

if __name__ == "__main__":
    main()

3. Copy Behavior

Modifying the flattened array does not affect the original.

Side-by-Side Compare

Direct comparison of the two methods.

1. Comparison Code

import numpy as np

def main():
    x = np.arange(6).reshape((3, 2))

    # ravel returns view
    r = x.ravel()
    r[0] = 99
    print(f"After ravel modification: x[0,0] = {x[0, 0]}")

    # Reset
    x = np.arange(6).reshape((3, 2))

    # flatten returns copy
    f = x.flatten()
    f[0] = 99
    print(f"After flatten modification: x[0,0] = {x[0, 0]}")

if __name__ == "__main__":
    main()

Output:

After ravel modification: x[0,0] = 99
After flatten modification: x[0,0] = 0

2. Key Difference

ravel shares memory; flatten does not.

When to Use Each

Choose based on your memory requirements.

1. Use ravel When

• Memory efficiency matters
• You want changes to reflect in original
• Working with large arrays

2. Use flatten When

• You need data isolation
• Original must remain unchanged
• Returning from functions

3. Performance Note

ravel is faster because it avoids memory allocation.

Order Parameter

Both methods support different flattening orders.

1. C Order (Default)

import numpy as np

x = np.array([[1, 2], [3, 4]])
print(x.ravel(order='C'))  # [1 2 3 4]

Row-major order (C-style).

2. Fortran Order

import numpy as np

x = np.array([[1, 2], [3, 4]])
print(x.ravel(order='F'))  # [1 3 2 4]

Column-major order (Fortran-style).