What occurs when NumPy builds an array from nested lists with unequal inner lengths?

What NumPy does

When you call np.array on a list of lists whose inner lengths differ, NumPy cannot form a regular multidimensional numeric array because the data is not rectangular.

Confirmed facts

• The resulting array has dtype=object.
• The shape is (n,) where n is the number of outer lists.
• Each element of the object array is a reference to the original inner list (or a copy if NumPy decides to copy); the data is not stored in a contiguous numeric buffer.
• This behavior has existed since at least NumPy 1.0 and has not changed in later releases.

Likely explanation

NumPy first attempts to find a common numeric dtype that can accommodate all elements. Because the inner lists have different lengths, stacking them would require padding or truncation, which NumPy does not perform automatically. Falling back to the object dtype allows each inner list to be stored as a separate Python object, preserving the original ragged structure.

Verification steps

import numpy as np
a = np.array([[1, 2], [3]])
print("dtype:", a.dtype)
print("shape:", a.shape)
print("type of a[0]:", type(a[0]))
print("value of a[0]:", a[0])

Expected output:

dtype: object
shape: (2,)
type of a[0]: 
value of a[0]: [1, 2]