Python array append

append() adds a single element to the end of a Python array or list.

For array.array (from the array module), use arr.append(element) to add one item. The element must match the array's data type (e.g., 'i' for integers). Adding a mismatched type raises a TypeError.
For lists, use list.append(element) to add one item. Lists accept mixed data types.
To add multiple elements from another array or list, use extend() instead of append().
For NumPy arrays, use numpy.append() to create a new array with values appended. It returns a copy and does not modify the original array. Use axis to specify appending along a dimension.

Example:

import array
arr = array.array('i', [1, 2, 3])
arr.append(4)  # Adds integer 4
print(arr)     # Output: array('i', [1, 2, 3, 4])

Python appending array to an array

stackoverflow.com › questions › 40336601 › python-appending-array-to-an-array

You can append the elements of one list to another with the "+=" operator. Note that the "+" operator creates a new list.

a = [1, 2, 3]
b = [10, 20]

a = a + b # Create a new list a+b and assign back to a.
print a
# [1, 2, 3, 10, 20]


# Equivalently:
a = [1, 2, 3]
b = [10, 20]

a += b
print a
# [1, 2, 3, 10, 20]

If you want to append the lists and keep them as lists, then try:

result = []
result.append(a)
result.append(b)
print result
# [[1, 2, 3], [10, 20]]

Answer from stackoverflowuser2010 on Stack Overflow

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Python Array Add: How to Append, Extend & Insert Elements | DigitalOcean

April 15, 2025 - Learn how to add elements to an array in Python using append(), extend(), insert(), and NumPy functions. Compare performance and avoid common errors.

Stack Overflow

stackoverflow.com › questions › 40336601 › python-appending-array-to-an-array

Python appending array to an array - Stack Overflow

Top answer

1 of 4

149

You can append the elements of one list to another with the "+=" operator. Note that the "+" operator creates a new list.

a = [1, 2, 3]
b = [10, 20]

a = a + b # Create a new list a+b and assign back to a.
print a
# [1, 2, 3, 10, 20]


# Equivalently:
a = [1, 2, 3]
b = [10, 20]

a += b
print a
# [1, 2, 3, 10, 20]

If you want to append the lists and keep them as lists, then try:

result = []
result.append(a)
result.append(b)
print result
# [[1, 2, 3], [10, 20]]

2 of 4

Apart from + operator there's another way to do the same i.e. extend()

a = [1, 2, 3]
b = [10, 20]

a.append(b) # Output: [1, 2, 3, [10, 20]]
a.extend(b) # Output: [1, 2, 3, 10, 20]

You can use these 2 functions for manipulating a list as per your requirement.

Discussions

Append method not working

I ran a simple python code on jupyter notebook for just testing the append method It is not working. It gives none as output. The code is as given below. List = [1,2,3,4] Newlist = List.append(7) print(Newlist) Output:- None I am new to python so please help me with this code and correct the ... More on discuss.python.org

discuss.python.org

April 8, 2023

Append and write to an array?

x.append() will modify the list, I don't understand what you mean by "code itseld is unaffected" More on reddit.com

r/learnpython

September 28, 2022

What is Python's list.append() method WORST Time Complexity? It can't be O(1), right?

I've read on Stackoverflow that in Python Array doubles in size when run of space It's actually not double, but it does increase proportional to the list size (IIRC it's about 12%, though there's some variance at smaller sizes). This does result in the same asymptotics though, so I'll assume doubling in the following description for simplicity. So basically it has to copy all addresses log(n) times. Not quite. Suppose we're appending n items to an empty vector. We will indeed do log(n) resizes, so you might think "Well, resizes are O(n), so log(n) resizes is n log(n) operations, which if we amortize over the n appends we did means n log(n)/n, or log(n) per append". However, there's a flaw in this analysis: we do not copy "all addresses" each of those times. Ie. the copies are not O(n). Sure, the last copy we do will involve copying n items, but the one before it only copied n/2, and so on. So we actually do 1 + 2 + 4 + ... + n copies, which sums to 2n-1. Divide that by n and you get ~2 operations per append - a constant. I assume since it copies addresses, not information We are indeed only copying the pointer, but this doesn't really matter for the complexity analysis. Even if it was copying a large structure, that'd only increase the time by a constant factor. Does that mean that O(1) is the average time complexity? It's the amortized worst case complexity (ie. what happens over a large number of operations). While any one operation can indeed end up doing O(n) operations, there's an important distinction that over a large number of operations, you are guaranteed to only be O(1), which is a distinction just talking about average case wouldn't capture. More on reddit.com

r/learnpython

October 26, 2022

Numpy Array Append

Just so you know: A numpy array is not made to be appended to like a list is. In the background of np.append an entire new array has to be created, so it's expensive (slow). It's much better to use lists if you need to append data. More on reddit.com

r/learnpython

February 14, 2019

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