Concatenating two one-dimensional NumPy arrays

python

numpy

array-manipulation

performance-optimization

byAlex Kataev·Dec 10, 2024

To combine two one-dimensional arrays, use numpy.concatenate():

import numpy as np
# Start your engines... or rather, your arrays
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

# Concatenating - It's like a zip... for arrays!
result = np.concatenate((a, b))

# If you're reading this, it's no error, just a bad pun!
print(result)  # Output: array([1, 2, 3, 4, 5, 6])

The key things to remember:

The arrays should be passed as a sequence (tuple/list)
This approach helps avoid common TypeError issues typically encountered
Importance of standard conventions, e.g., using np when importing NumPy.

Why do we concatenate?

Concatenation is vital when you need to merge different data sets, combine result arrays or even append further computations to an existing array. In a nutshell, late to the party? Concatenate!

Outside of the box - alternatives and performance

np.concatenate() is the go-to function for concatenation — a handy swiss army knife in an analyst's toolkit, but np.hstack(), np.r_, np.stack() provides variety catering to different use-cases like horizontal stacking or adding a new axis for concatenation.

A pro tip - np.concatenate() might show better results for small array sizes. For larger arrays remember to test, not guess. Use perfplot or any other performance plotting tool.

Making arrays play nice

Concatenating two arrays is an end-to-end process, and it will only work if the dimensions match across both arrays. If they don't match, then you might have to show them who's boss. This is where transpose comes in handy.

A step further - advanced techniques

For those who enjoy exploring, np.r_ and np.c_ allow for more intricate array manipulation. These shorthand notations let you concatenate inline, keeping your code shorter and cleaner.

Step-by-step visual guide to performance

Creating performance plots can help identify which function scales better with increasing array size. Use np.random.rand(n) to generate arrays, and plot the execution time vs array size. Just remember to label the axes!

Debugging and error handling

Encountered errors while concatenating? Ensure you are:

Passing the arrays as a sequence like tuple or list.
Using parentheses mindfully (they're particular about this).
Specify the axis if using np.stack() to avoid those pesky dimension mismatch errors.