[Tut] Python List append() vs extend()

[xSicKxBot]

Python List append() vs extend()

A profound understanding of Python lists is fundamental to your Python education. Today, I wondered: what’s the difference between two of the most-frequently used list methods:

Code:

append()

vs.

Code:

extend()

?

I shot a small video explaining the difference and which method is faster—you can play it as you read over this tutorial:



Here’s the short answer —

Code:

append()

vs

Code:

extend()

:

• The method
  Code:

  list.append(x)
  adds element
  Code:

  x
  to the end of the
  Code:

  list
  .
  
  
• The method
  Code:

  list.extend(iter)
  adds all elements in
  Code:

  iter
  to the end of the
  Code:

  list
  .
  

The difference between

Code:

append()

and

Code:

extend()

is that the former adds only one element and the latter adds a collection of elements to the list.

You can see this in the following example:

Code:

>>> l = [] >>> l.append(1) >>> l.append(2) >>> l [1, 2] >>> l.extend([3, 4, 5]) >>> l [1, 2, 3, 4, 5]

In the code, you first add integer elements 1 and 2 to the list using two calls to the

Code:

append()

method. (If you need a deeper understanding, check out my detailed article about the append() method on this blog.)

Then, you use the extend method to add the three elements 3, 4, and 5 in a single call of the

Code:

extend()

method.

[b]Which Method is Faster — extend() or append()?[/b]

To answer this question, I’ve written a short script that tests the runtime performance of creating large lists of increasing sizes using the

Code:

extend()

and the

Code:

append()

methods.

My thesis is that the

Code:

extend()

method should be faster for larger list sizes because Python can append elements to a list in a batch rather than by calling the same method again and again.

I used my notebook with an Intel® Core™ i7-8565U 1.8GHz processor (with Turbo Boost up to 4.6 GHz) and 8 GB of RAM.

Then, I created 100 lists with both methods, extend() and append(), with sizes ranging from 10,000 elements to 1,000,000 elements. As elements, I simply incremented integer numbers by one starting from 0.

Here’s the code I used to measure and plot the results: which method is faster—append() or extend()?

Code:

import time def list_by_append(n): '''Creates a list & appends n elements''' lst = [] for i in range(n): lst.append(n) return lst def list_by_extend(n): '''Creates a list & extends it with n elements''' lst = [] lst.extend(range(n)) return lst # Compare runtime of both methods list_sizes = [i * 10000 for i in range(100)] append_runtimes = [] extend_runtimes = [] for size in list_sizes: # Get time stamps time_0 = time.time() list_by_append(size) time_1 = time.time() list_by_extend(size) time_2 = time.time() # Calculate runtimes append_runtimes.append((size, time_1 - time_0)) extend_runtimes.append((size, time_2 - time_1)) # Plot everything import matplotlib.pyplot as plt import numpy as np append_runtimes = np.array(append_runtimes) extend_runtimes = np.array(extend_runtimes) print(append_runtimes) print(extend_runtimes) plt.plot(append_runtimes[:,0], append_runtimes[:,1], label='append()') plt.plot(extend_runtimes[:,0], extend_runtimes[:,1], label='extend()') plt.xlabel('list size') plt.ylabel('runtime (seconds)') plt.legend() plt.savefig('append_vs_extend.jpg') plt.show()

The code consists of three high-level parts:

• In the first part, you define two functions
  Code:

  list_by_append(n)
  and
  Code:

  list_by_extend(n)
  that take as input argument an integer list size
  Code:

  n
  and create lists of successively increasing integer elements using the
  Code:

  append()
  and
  Code:

  extend()
  methods, respectively.
  
  
• In the second part, you compare the runtime of both functions using 100 different values for the list size
  Code:

  n
  .
  
  
• In the third part of, you plot everything using the Python matplotlib library.
  

Here’s the resulting plot that compares the runtime of the two methods append() vs extend(). On the x axis, you can see the list size from 0 to 1,000,000 elements. On the y axis, you can see the runtime in seconds needed to execute the respective functions.



The resulting plot shows that both methods are extremely fast for a few tens of thousands of elements. In fact, they are so fast that the

Code:

time()

function of the time module cannot capture the elapsed time.

But as you increase the size of the lists to hundreds of thousands of elements, the

Code:

extend()

method starts to win:

For large lists with one million elements, the runtime of the

Code:

extend()

method is 60% faster than the runtime of the

Code:

append()

method.

The reason is the already mentioned batching of individual append operations.

However, the effect only plays out for very large lists. For small lists, you can choose either method. Well, for clarity of your code, it would still make sense to prefer

Code:

extend()

over

Code:

append()

if you need to add a bunch of elements rather than only a single element.

Your Coding Skills – What’s the Next Level?

If you love coding and you want to do this full-time from the comfort of your own home, you’re in luck:

I’ve created a free webinar that shows you how I started as a Python freelancer after my computer science studies working from home (and seeing my kids grow up) while earning a full-time income working only part-time hours.

Webinar: How to Become Six-Figure Python Freelancer?

Join 21,419 ambitious Python coders. It’s fun!