Python Arrays - The Complete Guide

Arrays in Python are very powerful and widely used data structures that are designed to store a fixed number of elements of the same data type. They generally use efficient memory management and provide faster operations that make arrays a useful tool to optimize the overall code performance and boost tasks like scientific computing, image processing, and high-performance applications. Understanding the arrays in Python will significantly help Python developers write cleaner, faster, and more efficient code.

With this Python array tutorial, you will generally learn everything you need to know about Python Arrays from creating and accessing their elements to performing more complex operations like handling 2D Arrays and NumPy Libraries. With detailed examples and key comparisons, this tutorial is your go-to resource for using arrays in Python Programming Language. Now let’s learn the Python Arrays in detail.

Table of Contents:

• What are Arrays in Python
• How to Create an Array in Python
• Array Index in Python
• How to Access Elements in Python Arrays
• How to Input in Python Arrays
• Basic Operations of Arrays in Python
  • Traversing of an Array in Python
  • Insertion of Elements in an Array in Python
  • Deletion of Elements in an Array in Python
  • Searching Elements in an Array in Python
  • Updating Elements in an Array in Python
• Multi-dimensional Arrays in Python
• Common Array Programs in Python
• Slicing of Array in Python
• How to Convert a List to an Array in Python
• How to Convert a String to an Array in Python
• NumPy Arrays in Python
• Array Broadcasting in Python
• Array vs List – Key Comparison
• Array vs Lists in Python – Performance Benchmark Comparison
• Use Cases of Arrays in Python

What are Arrays in Python

Python arrays are one of the most utilized data structures that are generally used to store multiple values of the same type in a contiguous memory location. These Python arrays simply provide efficient storage and faster operations for any numerical data. While Python does not have any built-in array class like any other languages(C++, Java), you can use the Python array module or the Numpy arrays for more effective array-based operations.

Key Characteristics of Arrays in Python

1. Uniform Data Type: Arrays in Python generally store the elements having the same data type which simply ensures memory efficiency and helps in faster operations unlike mixed-type structures called lists.
2. Indexing: In Python, the elements inside an array can be accessed using indexes that generally start from zero(0). This simply makes it easier to access or update the particular element inside the array.
3. Memory Usage: Typically arrays use comparatively less memory than Lists because they are very tightly packed with the fixed size of identical elements.
4. Contiguous Memory: The elements of Arrays inside the arrays are stored in a contiguous memory block that makes them to perform read/write operations faster than any scattered memory structures.
5. Library Compatibility: Arrays are also compatible with libraries like Numpy which simply extends their functionality.

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How to Create an Array in Python

In Python, arrays are generally used to store multiple values of the same type in a single variable. The array module in Python allows you to create and initialize an array and for that, you first need to import it first. Now, let’s look at the example of declaring an array in Python.

To create an array, the basic syntax is:

Here, typecode is what you use to define the type of value that is going to be stored in the array and initialization denotes the list of values that will be stored in the array.

Understanding Typecodes for Arrays in Python

Whenever you create an array in Python, you basically need to define the type of data you want the array to contain which is generally done by type codes. A type code is basically a single character that represents a particular data type. In this table, we have mentioned the typecodes of their respective data types.

Now, let’s create a Python array using the above-mentioned syntax and typecode.

Example:

Output:

Array Index in Python

The index of a value in an array is that value’s location within the array. The counting of array indices in Python starts at 0 and ends at n-1, where n is the total number of elements in the array.

arr1 = [2,5,7,8]

How to Access Elements in Python Arrays

You can access the elements of an array in Python using the respective indices of those elements, as shown in the following example.

Output:

The index of the array elements starts from 0. When you printed the value of array1[0], it displayed the first element.

How to Input in Python Arrays

Taking input in Python can be easily done by using the input() function. You can also add a statement to ask users about the input type.

Example:

The output will be:

Basic Operations of Arrays in Python

Following are some of the basic operations supported by the array module in Python:

1. Traversing of an Array in Python

Iterating between elements in an array is known as traversing. You can easily iterate through the elements of an array using Python for loop as shown in the example below:

Example:

Output:

2. Insertion of Elements in an Array in Python

Using this operation, you can add one or more elements to any given index.

Example:

Output:

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3. Deletion of Elements in an Array in Python

Using this operation, you can delete any element residing at a specified index. You can remove any element using the built-in remove() method.

Example:

Output:

4. Searching Elements in an Array in Python

Using this operation, you can search for an element by its index or its value.

Example:

Output:

In the above example, you have searched for the element using the built-in index() method. Using index(3) returned the output 2 which means that 3 is at the index number 2 in array_1. If the searched value is not present in the array, then the program will return an error.

5. Updating Elements in an Array in Python

Using this operation, you can update an element at a given index.

Example:

Output:

In the above example, you have updated the already existing value at index 2 instead of adding a new element.

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Multi-dimensional Arrays in Python

Multi-dimensional arrays are generally used to organize data in multiple layers which makes them very useful for tasks like scientific research, machine learning, and image processing.

1. 2D Arrays in Python

A 2D Array is basically an array of arrays. In a 2D array, the position of an element is referred to by two indices instead of just one. So it can be thought of as a table with rows and columns of data.

Example:

Output:

2. 3D Arrays in Python

A 3D Array is an array of 2D arrays. It generally requires three indices to access a single element (depth, row, and column).

Example:

Output:

In the above example:

• matrix_3d[0][1][2] accesses the element 6 from the first 2D array at index [1][2].
• matrix_3d[1][0][1] accesses the element 8 from the second 2D array at index [0][1].

Array Programs in Python

Let’s go through some common Array programs in Python.

1. How to find the Length of an Array in Python

Use the len() method to return the length of an array (the number of elements in an array).

Output:

2. How to find the Sum of an Array in Python

Output:

3. How to Sort Arrays in Python

Python provides a built-in function to sort arrays. The sort function can be used to sort the list in both ascending and descending order.

Output:

4. How to Reverse an Array in Python

Output:

Slicing of Array in Python

To pull out a section or slice of an array, the colon operator is used when calling the index.

Output:

How to Convert a List to an Array in Python

To convert a list to an array in Python, you can use the array() method in the numpy module.

Example:

Output:

How to Convert a String to an Array in Python

To convert a string to an array in Python, you can use the string.split() method.

Example:

Output:

NumPy Arrays in Python

NumPy arrays are a key component of the high-performance library of NumPy that is widely used for numerical computation in Python. NumPy enables a simple and efficient way of storing and processing large datasets with math operations not only with increased performance but with less use of memory compared to Python lists or native Python arrays.

Example of Creating and Using NumPy Arrays:

Output:

Array Broadcasting in Python

In Python, the broadcasting of arrays generally allows NumPy to perform element-wise operations between two arrays of different dimensions in which the smaller array is extended virtually in order to match the size of the larger array.

Broadcasting Rules:

• If arrays have different numbers of dimensions, NumPy generally makes the smaller array with ones on the left to match the larger array’s dimensions.
• The size in both dimensions will have to be a match or 1 for a broadcast to occur. Otherwise, an exception is thrown when neither of these is met.

Example:

Output:

Here, the 1D array [1, 0] is broadcasted over each row in the 2D array

Array vs List – Key Comparison

Now below we have mentioned the key differences between arrays and lists that will generally help you to know when to choose which tool for solving your Python Programming Problems.

Array vs Lists in Python – Performance Benchmark Comparison

The code below basically compares the performance of the arrays and lists in Python Programming. We have taken the example of arithmetic operations i.e., Multiplication of each element by 2 on a large dataset. From this example, you will learn that arrays are expected to perform better than lists by taking less time due to their feature of memory efficiency and contiguous storage.

Example:

Output:

Use Cases of Arrays in Python

1. Mathematical and Scientific Processing: Arrays are generally preferred for mathematical problem-solving and scientific data processing, majorly when used with the Python NumPy library.
2. IoT Applications: In practical applications, arrays are used in IoT applications for designing Data Storage and Sensors for storing and processing data in real-time.
3. Image and Signal Processing: Arrays also can store the pixel values in image processing and waveforms in signal processing.
4. Performance and Memory Optimization: In terms of performance and memory optimization required field arrays are majorly preferred.
5. Gaming Applications: Arrays are also widely used in gaming applications to store game board states and larger data sets for simulations.

Conclusion

With this, we have come to the end of this module in this tutorial on Python Arrays. We have covered a wide range of important topics from learning how to create an array and perform operations on arrays to learning advanced concepts like 2D arrays, and Numpy libraries for Python Arrays. Whether you are optimizing the problems, analyzing the data, or solving complex data structure problems, arrays are worth mastering.

Further, check out our offers for Python training Courses and also refer to the trending Python developer interview questions prepared by industry experts.