Top 10 Python Libraries in 2024

Introduction

In today’s world, when technology plays an increasingly vital role in all aspects of our lives, we need to choose a programming language that can effectively solve real-world problems. Python is one such programming language. The popularity of Python has increased in recent years due to its use in various industries such as software engineering, machine learning, and data science, etc. The large number of libraries that Python offers is the reason for its popularity. Many of today’s emerging talents have been attracted to Python as their primary programming language for this reason. Therefore, through this article, we would like to impart information to our readers about the most famous Python libraries and their uses in today’s modern world.

What is a library?

A library is a collection of methods, classes, and utility modules that your application code can use to perform specific tasks without having to write the functions from scratch. Since libraries are usually narrow in scope (e.g., strings, I/O, and sockets), the API (Application Programming Interface) is smaller and requires fewer dependencies. It is nothing more than a list of class definitions. Now, the question that should arise in everyone’s mind is why do we need a library? The explanation is simple: code reusability. Code reusability is nothing more than using code that has already been produced or written by other people for our own purpose. For example, some libraries have a function called findLastIndex(char) that returns the last index of a character in a string. We may immediately call the library’s findLastIndex(charToFind) function and provide the characters whose positions we need to find as parameters. In the life of a programmer, libraries play the role of an angel, as they keep programmers from reinventing the wheel and actually focus on the real problem.

What is a Python library?

Let’s start with a quick overview of the Python programming language before we dive straight into the most popular Python libraries. It’s a foregone conclusion that you’ve probably heard of the name «Python.» The brainchild of Guido Van Rossum and dating back to the 1980s, Python has proven to be a game changer. It’s one of the most widely used coding languages today, used for a wide range of applications. Python’s popularity can be attributed to a number of factors:

• Python comes with a set of libraries.
• Python is a beginner programming language because of its ease and simplicity.
• Python wants its developers to be more productive in all aspects of development, deployment, and maintenance.
• Another reason for Python's immense popularity is that it is lightweight and usable on weak systems.
• Compared to C, Java, and C++, Python's programming syntax is simple to learn and has a high level of abstraction.

As mentioned in the first point above, Python’s popularity has a lot to do with its diverse and easy-to-use libraries. Python libraries are a collection of useful functions that allow us to write code without starting from scratch. With over 137,000 libraries, Python can be used to create programs and models in a variety of fields, for example, machine learning, data science, data visualization, image and data manipulation, and many more.

1. Pandas

Pandas is an open source library licensed under BSD (Berkeley Software Distribution). This popular library is widely used in the field of data science. They are mainly used for data analysis, manipulation, cleaning, etc. Pandas allows data modeling and data analysis operations without having to switch to another language like R. Python libraries typically support the following data types:

• Data in a dataset.
• Time series containing ordered and unordered data.
• The rows and columns of the matrix data are labeled.
• Unlabeled information.
• Any other type of statistical information.

2. NumPy

NumPy is one of the most widely used open source Python libraries focused on scientific computing. It has built-in mathematical functions for fast calculations and supports large matrices and multidimensional data. “Numerical Python” is defined by the term “NumPy”. It can be used in linear algebra, as a multidimensional container for general data, and as a random number generator, among other things. Some of the important functions in NumPy are: arcsin(), arccos(), tan(), radians(), etc. In Python, NumPy Array is preferred over Lists because it occupies less memory and is faster and easier to use.

Features:

1. Interactive: Numpy is a very interactive and user-friendly library.
2. Mathematics: NumPy simplifies the execution of difficult mathematical equations.
3. Intuitive: Makes coding and understanding topics easy.
4. High interaction: There is a lot of interaction in it because it is widely used, hence there is a lot of open source contribution.

The NumPy interface can be used to represent images, audio waveforms, and other raw binary streams as an N-dimensional array of real values for visualization. Knowledge of NumPy is required for full-stack developers to implement this library for machine learning.

3. Keras

Keras is an open source Python-based neural network library that allows us to quickly test deep neural networks. As deep learning becomes more popular, Keras is emerging as a great option because, according to the creators, it is an API (application programming interface) designed for humans, not machines. Keras has a higher adoption rate in the industry and research community than TensorFlow or Theano. It is recommended to install the TensorFlow Backend Engine before installing Keras.

Features:

1. It runs on both CPU (Central Processing Unit) and GPU (Graphics Processing Unit) without any problems.
2. Keras supports almost all neural network models, including fully connected, convolutional, pooled, recurrent, embedding, etc. These models can also be combined to create more complex models.
3. Keras' modular design makes it highly expressive, adaptable, and suitable for advanced research.
4. Keras is a Python-based framework that simplifies debugging and exploring various models and projects.

4. TensorFlow

TensorFlow is a high-performance numerical computing library that is open source. It is also used in deep learning algorithms and machine learning algorithms. It was created by researchers on the Google Brain team in Google's Artificial Intelligence organization and is now widely used by mathematics, physics, and machine learning researchers for complex mathematical calculations. TensorFlow is designed to be fast and uses techniques such as XLA (XLA or Accelerated Linear Algebra is a domain-specific compiler for linear algebra that can accelerate TensorFlow models without changing the source code.) to perform fast linear algebra calculations.

Features:

1. Responsive structure: We can easily visualize any part of the graph with TensorFlow which is not possible with Numpy or SciKit.
2. Versatile: One of the most essential features of Tensorflow is that it is flexible in its performance in relation to machine learning models, meaning it is modular and allows you to build parts of it on your own.
3. Training machine learning models in TensorFlow is simple: Machine learning models can be easily trained using TensorFlow on CPUs and GPUs for distributed computing.
4. Parallel neural network training: TensorFlow allows you to train multiple neural networks and GPUs simultaneously.
5. Open source and a large community: No doubt, if it is developed by Google, there is already a significant team of software experts working on continuous stability improvements. The best part about this machine learning library is that it is open source, meaning anyone with internet access can use it.

5. Scikit Learn

Scikit Learn is an open source library for machine learning algorithms that runs in the Python environment. It can be used with supervised and unsupervised learning algorithms. The library includes popular algorithms as well as NumPy, Matplotlib, and SciPy packages. Scikit Learn is most well-known for its use in music recommendations on Spotify. Let’s now explore some of the key features of Scikit Learn in depth:

1. Cross-Validation: There are various methods to check the accuracy of supervised models on unseen data with Scikit Learn, for example, the train_test_split method, cross_val_score, etc.
2. Unsupervised learning techniques: A wide range of unsupervised learning algorithms are available, from clustering, factor analysis, principal component analysis, and unsupervised neural networks.
3. Feature extraction: Extracting features from images and text is a useful tool (like Bag of Words).

6. Eli5

The predictions of machine learning models are often incorrect, and the Eli5 Python machine learning library helps in addressing this difficulty of incorrect predictions. It combines visualization and debugging of all machine learning models as well as tracing all the working processes of an algorithm. Some of the libraries supported by Eli5 are XGBoost, lightning, scikit-learn, and sklearn crfsuite.

Let's talk about some of the applications of Eli5 now:

1. ELI5 is a Python package used to examine machine learning classifiers and explain their predictions. It is commonly used to debug algorithms such as sklearn regressors and classifiers like XGBoost, CatBoost, Keras, etc.
2. Eli5 is essential where there are dependencies with other Python packages.
3. Eli5 is also used in various industries where legacy software and innovative approaches are implemented.

7. SciPy

Scipy is a free and open source Python library used for scientific computing, data processing, and high-performance computing. The library includes a large number of user-friendly routines for fast computing. It is based on the NumPy extension, which allows for data processing and visualization, as well as high-level commands. Scipy is used alongside NumPy for mathematical computations. NumPy enables sorting and indexing of array data, while SciPy stores the numerical code. Cluster, constant, fftpack, merge, interpolation, io, linalg, ndimage, odr, optimize, signal, sparse, spatial, eigen, and statistics are just a few of the numerous subpackages available in SciPy. To import them from SciPy, one can use “from scipy import subpackage-name”. However, NumPy, the SciPy library, Matplotlib, IPython, Sympy, and Pandas are essential SciPy packages.

Features:

1. The key feature of SciPy is that it is written in NumPy and its arrays make extensive use of NumPy.
2. SciPy uses its specialized submodules to provide all the efficient numerical algorithms such as optimization, numerical integration, and many more.
3. All functions are extensively documented under SciPy modules. SciPy's primary data structure is NumPy arrays, and it includes modules for a variety of popular scientific programming applications. SciPy easily handles tasks such as linear algebra, integration (calculus), solving ordinary differential equations, and signal processing.

8. PyTorch

PyTorch is a Python library first introduced by Facebook in 2017 that combines two high-level capabilities: significantly GPU-accelerated tensor computation (similar to NumPy) and deep neural network-based platforms that offer flexibility and speed.

Features:

1. Python and its libraries are supported by PyTorch.
2. Facebook's deep learning requirements made it necessary to use this technology.
3. It provides an easy-to-use API that improves usability and understanding.
4. Graphs can be dynamically set up and calculated at any point in code execution in PyTorch.
5. In PyTorch, coding is simple and processing is fast.
6. Since CUDA (CUDA is a parallel computing platform and application programming interface that allows software to use specific types of graphics processing units for general-purpose processing – an approach called general-purpose computing on GPUs) is supported, it can be run on GPU machines.

9. LightGBM

Gradient Boosting is a prominent machine learning package that helps developers develop new algorithms by redefining simple models such as decision trees. As a result, there are dedicated libraries that may be used to implement this gradient boosting method quickly and efficiently. LightGBM, XGBoost, and CatBoost are the libraries in question. All of these libraries are competitors that help solve a problem and may be used in almost the same way.

Features:

1. High production efficiency is guaranteed by very fast calculations.
2. It is user-friendly because it is intuitive.
3. Many deep learning libraries take longer to train than this library.
4. There will be no error when using NaN values and other canonical values.

10. Theano

Theano, like other math libraries, allows users to define, optimize, and evaluate mathematical expressions. It uses huge multidimensional arrays for efficient math processing. Standard C-based code becomes slower when dealing with large amounts of data. On the other hand, Theano enables fast code execution due to its rich library. Invalid expressions can be detected and evaluated, making the library more useful than NumPy.

Features:

1. PyName Integration: Theano can use NumPy arrays fully in Theano-compliant functions.
2. Using a transparent GPU: It can be used to perform data-intensive operations much faster than the CPU.
3. Efficient symbolic differentiation: Theano performs derivatives for functions with one or more inputs using efficient symbolic differentiation.
4. Optimized for speed and stability: For problems like getting the correct solution to log(1+x) even when x is very small, Theano works well. This is just one of many proofs of Theano's stability.
5. Generate C code that is dynamic: Theano can evaluate expressions quickly, leading to a significant increase in efficiency.
6. Extensive unit testing and automated verification: Theano can help identify and diagnose multiple types of problems and ambiguities in the model with extensive unit testing and automated verification.

Result

The easy-to-use Python programming language has found wide application in a variety of real-world applications. Since it is a high-level, dynamically typed, and interpreted language, it is rapidly expanding in the areas of error debugging. Python is increasingly used in global applications such as YouTube, Dropbox, etc. Moreover, with the availability of Python libraries, users can perform various tasks without having to write their own code. Therefore, learning about Python and its libraries is very important for every young talent today. The use of Python in various fields such as data science, machine learning, software engineering, etc. definitely makes it a language for the future.