"cloning" row or column vectors

python

performance

numpy

data-cloning

byNikita Barsukov·Feb 2, 2025

Whether you want to duplicate a row or column vector in Python, NumPy is your friend. The np.broadcast_to function is your go-to for memory efficiency, whereas np.tile is ideal for creating actual copies. Let's see this in action with a column vector:

import numpy as np

# Original column vector (aka lonely matrix 😄)
vector = np.array([[1], [2], [3]])

# Clone using broadcasting (like a free Xerox machine 🖨️)
broadcast_clone = np.broadcast_to(vector, (3, 4))

# Clone using tiling (actual hard copies 📇)
tile_clone = np.tile(vector, (1, 4))

print(broadcast_clone)
print(tile_clone)

np.broadcast_to provides a scaled version of the vector with no data duplication. On the other hand, np.tile duplicates the vector to form a larger array. Choose wisely for your memory and performance needs!

To clone a row vector, Python list replication has your back:

# Original row vector
row_vector = np.array([1, 2, 3])

# Aye aye, Captain, ready for cloning! 👩‍✈️
replicated_row = np.array([row_vector] * 3)

print(replicated_row)

For the column vector cloners out there, transpose is here to save the day:

# Prepare for duplication, me hearties! ☠️⛵
replicated_column = np.repeat(row_vector[:, np.newaxis], 3, axis=1)

print(replicated_column)

Performance matters in vector cloning

When dealing with Big Data or speed-critical applications, efficiency is king. Let's run a horse race between np.tile, np.repeat, and np.broadcast_to using %timeit:

%timeit np.tile(vector, (1, 1000))
%timeit np.repeat(vector, 1000, axis=1)
%timeit np.broadcast_to(vector, (3, 1000))

np.broadcast_to may lap the competition since it avoids real memory duplication—it's a mere apparition, a ghost of the original data 👻. If you need a physical entity to poke and prod, maybe opt for np.tile or np.array(np.broadcast_to()) to force the data into reality.

Zero-cost options like np.broadcast_to come with a complementary beverage and small bag of peanuts—just kidding, but they could save valuable memory space if your data isn't changing later on!

Which tool for the job?

`np.broadcast_to`: Use when

You're running a shoestring memory budget
You only need to read the data
You're not taxing your data with heavy calculations post-cloning

`np.tile` or `np.array(np.broadcast_to())`: Use when

You're planning on changing the data later on
Each entity needs to be an independent individual
Your clones need to muster the energy for complex arithmetic acts

3D Space: The Final Frontier of Cloning

If you feel row and column cloning isn't enough, here's how you can perform full-stack, 3D cloning with NumPy:

# Cloning a row vector into a 3D matrix
vector_3D_row_clone = np.tile(row_vector, (3, 3, 1))

# Cloning a column vector into a 3D matrix
vector_3D_column_clone = np.tile(vector, (1, 3, 3))

print(vector_3D_row_clone)
print(vector_3D_column_clone)

But, tread carefully! Ensure your shapes are well matched and you're setup for the increased memory footprint.

Bare-bones Python Style

If you're stuck in a desert island with nothing but Python's core, list and zip functions might save you. Here's how:

# Cloning a row vector with list comprehension
row_vector_simple = [1, 2, 3]
replicated_row_simple = [row_vector_simple for _ in range(3)]

# Cloning a column vector with zip
column_vector_simple = (1, 2, 3)
replicated_column_simple = list(zip(*[column_vector_simple]*3))

print(replicated_row_simple)
print(replicated_column_simple)