Given test = np.array([[1, 2], [3, 4], [5, 6]]), test[i] returns the ith row. How to extract the ith column like [1, 3, 5] efficiently? Is it computationally expensive?

Use slicing test[:, i] to access the ith column efficiently. It returns a view (no copy), making it computationally cheap and fast even for large arrays. Avoid loops for vectorized performance.

Programming

How to Access Ith Column of NumPy Multidimensional Array

Learn how to efficiently access the ith column in a NumPy array using slicing like test[:, i]. Get the first column [1, 3, 5] from [[1,2],[3,4],[5,6]]. Fast views, no copying, perfect for large numpy arrays and indexing.

1 answer• 1 view

01/18/2026, 04:25 PM

How to access the ith column of a NumPy multidimensional array?

Given:

python

test = np.array([[1, 2], [3, 4], [5, 6]])

test[i] returns the ith row (e.g., [1, 2]). How do I extract the ith column (e.g., [1, 3, 5]) efficiently? Is this operation computationally expensive?

To grab the ith column from a NumPy multidimensional array like your test example, slice it with test[:, i]—that pulls out all rows (:) for just the ith column, giving you [1, 3, 5] for i=0. It’s blazing fast because NumPy returns a view of the data (no copying unless you force it), making this operation computationally cheap even on huge arrays. Forget loops; slicing handles it vectorized and efficiently.

Basic Column Access in NumPy Arrays
Slicing Syntax for the Ith Column
Hands-On Example with Your Array
Performance and Efficiency Breakdown
Advanced Column Extraction Techniques
Sources
Conclusion

Basic Column Access in NumPy Arrays

NumPy arrays shine for multidimensional data, but rows are straightforward (test[i]), while columns need a twist. Why? Arrays store data in row-major order, so column access isn’t direct like lists. Enter slicing: arr[:, i] selects every row from the ith column.

This works on 2D arrays (matrices) and even higher dimensions. Quick test in your IPython session—import numpy as np, build that array, slice away. Boom, column in hand.

No fuss, no loops. But what if i varies? Dynamic indexing keeps it flexible.

Slicing Syntax for the Ith Column

The magic colon before the comma is key: : means “all rows,” and i pins the column. For a 2D numpy array, it’s arr[:, i]. Want multiple columns? arr[:, i:j] grabs from i to j-1.

From the NumPy indexing docs, this uses basic slicing rules—integers for exact picks, slices for ranges. Integer i? Gets a 1D view. Slice like 1:3? Still a view, shape preserved.

Pro tip: Negative indices work too (arr[:, -1] for the last column). Handles edge cases like empty slices gracefully.

Hands-On Example with Your Array

Let’s run your exact setup:

python

import numpy as np

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

See? test[:, 0] nails the first column. Stack Overflow users swear by this for quick extracts, as in this classic thread. Matches GeeksforGeeks examples perfectly.

Scale it up—10,000 rows? Same syntax, no sweat.

Performance and Efficiency Breakdown

Is [:, i] expensive? Nope. NumPy slicing creates a view, not a copy—zero data duplication, O(1) time. Only triggers a copy on fancy indexing (e.g., arr[[0,2], i]) or assignments.

Benchmarks from Delft Stack confirm: slicing beats loops by orders of magnitude. Your 3x2 array? Microseconds. Million-row beast? Still instant, thanks to C-level speed.

But watch out—modifying the view affects the original. Call copy() if needed: col = test[:, i].copy().

Advanced Column Extraction Techniques

Beyond basics, transpose for row-like access: test.T[i] flips to columns-as-rows. Handy, but slicing’s usually faster.

For non-contiguous columns, np.take(arr, indices, axis=1). Or boolean masks: arr[:, mask]. Sling Academy covers these with code.

3D arrays? arr[:, :, i] for “ith slice along axis=2.” W3Schools nails the comma syntax for dims.

Real-world? Data analysis—pull features fast for models.

Sources

Conclusion

Stick to arr[:, i] for ith column access in NumPy arrays—simple, efficient, and view-based for top speed. Your example flips from row grabs to column pulls effortlessly, scaling to massive datasets without breaking a sweat. Master this, and numpy array indexing becomes second nature; experiment in a notebook to feel the power.

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