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Asked 1 month ago by VenusianEngineer319

Stack OverflowFrom Stack Overflow

One-liner to Zero Pad an n-Dimensional NumPy Array Regardless of Data Ownership

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I need a one-line solution to zero pad an n-dimensional NumPy array, even when the array does not own its data.

I’m not referring to the 1D case from my previous question; here the challenge is to handle arrays with any number of dimensions. For example:

PYTHON
import numpy as np

a = np.array([1, 2, 3])
zeropad(a, 8)  # [1, 2, 3, 0, 0, 0, 0, 0]

b = np.array([[1, 2], [3, 4], [5, 6]])
zeropad(b, (5, 2))  # [[1, 2], [3, 4], [5, 6], [0, 0], [0, 0]]

Using b.resize((5, 2)) works in some cases, but sometimes it throws a:

PYTHON
ValueError: cannot resize this array: it does not own its data

How can I zero pad a NumPy array in one line so that it works regardless of data ownership?

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Answers 4

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4 Answers

Answer by StackOverflowTychoAI Agent • 1 month ago

0

You can solve this in one line with NumPy’s built‑in pad function. For example, you can define:

PYTHON
zeropad = lambda a, s: np.pad(
    a, 
    [(0, n - d) for d, n in zip(a.shape, (s,) if isinstance(s, int) else s)],
    mode='constant'
)

Usage examples:

PYTHON
import numpy as np

a = np.array([1, 2, 3])
print(zeropad(a, 8))  # Output: [1 2 3 0 0 0 0 0]

b = np.array([[1, 2], [3, 4], [5, 6]])
print(zeropad(b, (5, 2)))
# Output:
# [[1 2]
#  [3 4]
#  [5 6]
#  [0 0]
#  [0 0]]

Explanation

  • The lambda checks if “s” is an integer (for 1D arrays) or a tuple (for n‑dimensional arrays).
  • It uses a list comprehension to calculate how many zeros to pad at the end along each dimension: for each dimension, the pad width is (0, target_size – current_size).
  • np.pad returns a new array with the specified padding, so it doesn’t require the array to own its data.

This one‑liner works for any NumPy array regardless of ownership.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by NeutronResearcher085 • 1 month ago

0

Here is another one-liner

PYTHON
import tensorflow as tf
tf.pad(b, ([0,2],[0,0])).numpy()

which gives

PYTHON
array([[1, 2],
       [3, 4],
       [5, 6],
       [0, 0],
       [0, 0]])

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Answer by CosmicTraveler733 • 1 month ago

0

I am curious if there is a more direct Numpy built-in way nowadays, but this works:

PYTHON
def zeropad(arr, shape):
    return np.pad(arr, [(0, max(s - dim, 0)) for s, dim in zip(shape, arr.shape)],
                  mode='constant', constant_values=0)

No comments yet.

Answer by PlutonianSentinel449 • 1 month ago

0

Instead of using pad, since you want to pad after, you could create an array of zeros and assign the existing values:

PYTHON
out = np.zeros(pad, dtype=arr.dtype)
out[np.indices(arr.shape, sparse=True)] = arr

Or, if you only want to pad the first dimension, with resize. Just ensure that the array owns its data with copy:

PYTHON
out = arr.copy()
out.resize(pad)

Output:

PYTHON
array([[1, 2],
       [3, 4],
       [5, 6],
       [0, 0],
       [0, 0]])

I really want a one-liner resize:

IMO there is no good reason for that, but you could always use an assignment expression (python ≥ 3.8):

PYTHON
(out:=arr.copy()).resize(pad)

Output for a different pad

PYTHON
arr = np.array([[1, 2], [3, 4], [5, 6]])
pad = (5, 3)

# output zeros + assignment
array([[1, 2, 0],
       [3, 4, 0],
       [5, 6, 0],
       [0, 0, 0],
       [0, 0, 0]])

# output resize
array([[1, 2, 3],
       [4, 5, 6],
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])

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How can I one-line zero pad a numpy n-dimensional array regardless of data ownership?