Python fixed size array

Python Int Array with Size: Different Methods to Initialize Arrays of Fixed Size

Learn how to create an int array with a fixed size in Python using different methods such as lists, NumPy, and the array module.

• Using a list to create a dynamic array
• Using NumPy to create an array of fixed size
• Using list comprehension to create an array of fixed size
• Using the array module to specify the type of an array
• Finding the size of an array
• Other code samples for initializing an int array of fixed size in Python
• Conclusion
• How do you give an array size in Python?
• How do I get the size of an int array?
• Does size () work on arrays?
• How do you declare an array size 10?

In Python, arrays are used to store data in consecutive memory locations. Unlike Java, there is no int [] arr syntax in Python. This blog post will explore various methods to initialize an array of fixed size in Python for users who are seeking information on how to do so.

Using a list to create a dynamic array

A list can be treated as a dynamic array in Python. The length of a list can be found using the len() function. To create a list of fixed size, we can use a for loop and range() function to initialize the list with a default value.

In the example above, a list of size 10 is initialized with all elements set to 0, as specified by the multiplication of [0] and 10 .

Using NumPy to create an array of fixed size

NumPy is a popular library for numerical computing in python. The numpy.zeros() function can be used to create an array of a specific size with all elements initialized to zero. The numpy.ones() function can be used to create an array of a specific size with all elements initialized to one. The numpy.full() function can be used to create an array of a specific size with all elements initialized to a given value.

import numpy as np arr = np.zeros(10, dtype=int) 

In the example above, a NumPy array of size 10 with data type int is initialized with all elements set to 0.

Using list comprehension to create an array of fixed size

List comprehension is a concise way to create lists in Python. It can also be used to create arrays of fixed size.

In the example above, an array of size 10 is initialized with all elements set to 0 using a list comprehension.

Using the array module to specify the type of an array

The array module can be used to create arrays with a specific data type. The type code for an integer array is ‘i’ .

import array arr = array.array('i', [0 for i in range(10)]) 

In the example above, an array of size 10 with data type int is initialized with all elements set to 0 using the array module.

Finding the size of an array

In Python, the size of an array can be found using the len() function. In NumPy, the size of an array can be found using the size() attribute.

import numpy as np arr = np.zeros(10, dtype=int) print(len(arr)) print(arr.size) 

In the example above, the size of the numpy array is printed twice, once using the len() function and once using the size() attribute.

Other code samples for initializing an int array of fixed size in Python

In Python case in point, array of 1 to 100 python code example

a = array.array('i',(0 for i in range(0,10)))

Conclusion

In conclusion, Python arrays are used to store data in consecutive memory locations. A list can be treated as a dynamic array in Python. NumPy is a popular library for numerical computing in python. The array module can be used to specify the type of an array. In Python, the size of an array can be found using the len() function or the size() attribute in NumPy. With the various methods outlined in this blog post, initializing an array of fixed size in python becomes an easy task.

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How to create a fix size list in python?

There are a few ways to create a fixed size list in Python, and each method has its own advantages and disadvantages. In this tutorial, we will go through some of the most common ways to create a fixed size list in Python.

Method 1: Using the list() function

fixed_list.append(1) fixed_list.append(2) fixed_list.append(3)

• Step 3 — To create a fixed size list, you can simply use the len() function to check the length of the list and limit the number of elements in the list.

MAX_SIZE = 5 if len(fixed_list) >= MAX_SIZE: fixed_list.pop()

Method 2: Using the array.array() class

The first argument to the array() class is the type code of the elements that you want to store in the array, and the second argument is an iterable containing the elements to store in the array.

from array import array fixed_list = array("i", [1, 2, 3])

• Step 2 — As with the previous method you can use the len() function to check the length of the list and limit the number of elements in the list.

MAX_SIZE = 5 if len(fixed_list) >= MAX_SIZE: fixed_list.pop()

Method 3: Using the collections.deque() class

• Step 1 — The collections module provides a deque() class that you can use to create a fixed size list

The first argument to the deque() class is an iterable containing the elements to store in the deque, and the second argument is an optional maximum size for the deque.

from collections import deque fixed_list = deque([1, 2, 3], maxlen=5)

• Step 2 — Once the maxlen is reached, new element will be added and the oldest one will be removed from the deque

This way it will maintain the fixed size.

fixed_list.append(4) fixed_list.append(5) fixed_list.append(6)

Method 4: Using List comprehension

• Step 1 — Using list comprehension, you can create a list of a fixed size by specifying the number of elements you want in the list

This can be done using a for loop and the range function.

fixed_list = [x for x in range(5)]

• Step 2 — You can also use list comprehension to create a fixed size list by specifying the number of elements and the value of each element in the list

fixed_list = [0 for x in range(5)]

Method 5: Using the * operator

• Step 1 — You can create a fixed size list by using the * operator to replicate a single element multiple times

multiplying a single element by the number of times you want it to be replicated in the list.

Method 6: Using itertools.repeat()

• Step 1 — The itertools module provides a repeat() function that you can use to create a fixed size list

The first argument to the repeat() function is the element you want to repeat, and the second argument is the number of times you want the element to be repeated.

import itertools fixed_list = list(itertools.repeat(0, 5))

Conclusion

In conclusion, creating a fixed size list in Python can be done using a variety of methods, each with its own advantages and disadvantages. The list() function, the array.array() class, the collections.deque() class, list comprehension, the * operator and the itertools.repeat() function are all commonly used methods for creating fixed size lists in Python. The best method to use will depend on the specific use case and the requirements of the project. It’s important to understand the behavior and constraints of each method and to choose the one that best fits your needs.

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Create a List With a Specific Size in Python

1. Preallocate Storage for Lists
2. Preallocate Storage for Other Sequential Data Structures

Preallocating storage for lists or arrays is a typical pattern among programmers
when they know the number of elements ahead of time.

Unlike C++ and Java, in Python, you have to initialize all of your pre-allocated storage with some values. Usually, developers use false values for that purpose, such as None , » , False , and 0 .

Python offers several ways to create a list of a fixed size, each with
different performance characteristics.

To compare performances of different approaches, we will use Python’s standard
module timeit .
It provides a handy way to measure run times of small chunks of Python code.

Preallocate Storage for Lists

The first and fastest way to use the * operator, which repeats a list a specified
number of times.

>>> [None] * 10 [None, None, None, None, None, None, None, None, None, None] 

A million iterations (default value of iterations in timeit ) take approximately
117 ms.

>>> timeit("[None] * 10") 0.11655918900214601 

Another approach is to use the range built-in function with a list comprehension.

>>> [None for _ in range(10)] [None, None, None, None, None, None, None, None, None, None] 

It’s almost six times slower and takes 612 ms second per million iterations.

>>> timeit("[None for _ in range(10)]") 0.6115895550028654 

The third approach is to use a simple for loop together with the list.append() .

>>> a = [] >>> for _ in range(10): . a.append(None) . >>> a [None, None, None, None, None, None, None, None, None, None] 

Using loops is the slowest method and takes 842 ms to complete a million iterations.

>>> timeit("for _ in range(10): a.append(None)", setup="a=[]") 0.8420009529945673 

Preallocate Storage for Other Sequential Data Structures

Since you’re preallocating storage for a sequential data structure, it may make a lot of sense to use the array built-in data structure instead of a list.

>>> from array import array >>> array('i',(0,)*10) array('i', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) 

As we see below, this approach is second fastest after [None] * 10 .

>>> timeit("array('i',(0,)*10)", setup="from array import array") 0.4557597979946877 

Let’s compare the above pure Python approaches to the NumPy Python package for scientific computing.

>>> from numpy import empty >>> empty(10) array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) 

The NumPy way takes 589 ms per million iterations.

>>> timeit("empty(10)", setup="from numpy import empty") 0.5890094790011062 

However, the NumPy way will be much faster for more massive lists.

>>> timeit("[None]*10000") 16.059584009999526 >>> timeit("empty(10000)", setup="from numpy import empty") 1.1065983309963485 

The conclusion is that it’s best to stick to [None] * 10 for small lists, but switch
to NumPy’s empty() when dealing with more massive sequential data.

Founder of DelftStack.com. Jinku has worked in the robotics and automotive industries for over 8 years. He sharpened his coding skills when he needed to do the automatic testing, data collection from remote servers and report creation from the endurance test. He is from an electrical/electronics engineering background but has expanded his interest to embedded electronics, embedded programming and front-/back-end programming.

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