Expanding Dimensions

Adding new axes to arrays is essential for broadcasting and batch operations.

np.expand_dims

The np.expand_dims function inserts a new axis at the specified position.

1. Axis Parameter

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    print(f"{x.shape = }", end="\n\n")

    y0 = np.expand_dims(x, axis=0)
    y1 = np.expand_dims(x, axis=1)
    y2 = np.expand_dims(x, axis=2)
    y3 = np.expand_dims(x, axis=3)
    print(f"{y0.shape = }")
    print(f"{y1.shape = }")
    print(f"{y2.shape = }")
    print(f"{y3.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)

y0.shape = (1, 2, 3, 4)
y1.shape = (2, 1, 3, 4)
y2.shape = (2, 3, 1, 4)
y3.shape = (2, 3, 4, 1)

2. Axis Meaning

The axis specifies where the new size-1 dimension is inserted.

Using np.newaxis

The np.newaxis constant adds dimensions via indexing syntax.

1. Explicit Slicing

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    print(f"{x.shape = }", end="\n\n")

    y1 = x[np.newaxis, :]
    y2 = x[:, np.newaxis]
    y3 = x[:, :, np.newaxis]
    y4 = x[:, :, :, np.newaxis]
    print(f"{y1.shape = }")
    print(f"{y2.shape = }")
    print(f"{y3.shape = }")
    print(f"{y4.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)

y1.shape = (1, 2, 3, 4)
y2.shape = (2, 1, 3, 4)
y3.shape = (2, 3, 1, 4)
y4.shape = (2, 3, 4, 1)

2. With Ellipsis

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    print(f"{x.shape = }", end="\n\n")

    z1 = x[np.newaxis, ...]
    z2 = x[..., np.newaxis]
    print(f"{z1.shape = }")
    print(f"{z2.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)

z1.shape = (1, 2, 3, 4)
z2.shape = (2, 3, 4, 1)

Using None

None is an alias for np.newaxis in indexing.

1. Explicit Slicing

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    print(f"{x.shape = }", end="\n\n")

    y1 = x[None, :]
    y2 = x[:, None]
    y3 = x[:, :, None]
    y4 = x[:, :, :, None]
    print(f"{y1.shape = }")
    print(f"{y2.shape = }")
    print(f"{y3.shape = }")
    print(f"{y4.shape = }")

if __name__ == "__main__":
    main()

2. With Ellipsis

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    print(f"{x.shape = }", end="\n\n")

    z1 = x[None, ...]
    z2 = x[..., None]
    print(f"{z1.shape = }")
    print(f"{z2.shape = }")

if __name__ == "__main__":
    main()

Method Comparison

Three equivalent ways to add dimensions.

1. np.expand_dims

Most explicit and readable for complex insertions.

2. np.newaxis

Standard NumPy idiom, clear intent.

3. None Shorthand

Shortest syntax, common in concise code.