Append a Constant Column of Ones to a NumPy 2D Array the Right Way: Fixing List Comprehension Errors

Learn how to append a constant column of ones to a NumPy 2D array without errors. Use broadcast_to and hstack to avoid ValueError from list comprehensions.

Appending a constant column to a NumPy 2D array sounds trivial, yet it often trips people up when they mix Python list logic with NumPy semantics. Here is a compact guide to why the naive approach fails and how to solve it cleanly with NumPy tools.

Problem overview

We have a single-column 2D array and need to append a column of ones to the right. In other words, turn a feature column into a matrix with an extra bias column.

Problematic example

The data looks like this:

import numpy as np
vals2d = np.array([[6.575],
                   [6.421],
                   [7.185],
                   [6.998],
                   [6.43 ],
                   [6.012],
                   [6.172],
                   [5.631],
                   [6.004],
                   [6.377],
                   [6.03 ]])

A natural first attempt is to build a new array using a list comprehension:

augmented = np.array([[item, 1] for item in vals2d])

This raises an error of the form:

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (506, 2) + inhomogeneous part.

Why this fails

Iterating over a 2D NumPy array yields 1D arrays, not scalars. Each iteration here produces something like array([6.575]). The list comprehension therefore builds elements shaped like [array([6.575]), 1] instead of [6.575, 1]. When NumPy tries to assemble these into a single numeric array, it detects inhomogeneous content and refuses to coerce it. As a related pitfall, arrays are not meant to hold Python lists inside; that leads to object dtype, which you almost certainly shouldn’t use for this case.

Correct solution with NumPy

Stay in the NumPy world. Expand the scalar with broadcast_to and concatenate with hstack:

result = np.hstack([vals2d, np.broadcast_to(1, vals2d.shape)])

This produces:

array([[6.575, 1.   ],
       [6.421, 1.   ],
       [7.185, 1.   ],
       [6.998, 1.   ],
       [6.43 , 1.   ],
       [6.012, 1.   ],
       [6.172, 1.   ],
       [5.631, 1.   ],
       [6.004, 1.   ],
       [6.377, 1.   ],
       [6.03 , 1.   ]])

If you must fix the comprehension

The issue is that each item is a 1D array. Extract the scalar or unpack the row before building the new list. These forms avoid the inhomogeneity, although using loops for NumPy arrays is discouraged:

np.array([[val, 1] for val in vals2d[:, 0]])
# or
np.array([[*row, 1] for row in vals2d])

Why this matters

Using NumPy-native operations like broadcast_to and hstack prevents shape mismatches and avoids drifting into object dtype territory. It keeps your array well-formed and ensures operations downstream behave predictably.

Takeaways

When working with NumPy arrays, prefer vectorized operations over Python loops. If you need to add a constant column, broadcast the constant to the target shape and concatenate along the last axis. If you ever run into “inhomogeneous shape” errors, check what your iteration actually yields and ensure you are combining uniform numeric structures rather than lists or nested arrays.