Best Python Libraries in 2024

Best Python Libraries in 2024

With over 400 libraries, Python has established itself as one of the world's most flexible and versatile programming languages. Python has steadily gained popularity among data scientists, making it the top programming language for analytics projects. Its popularity stems from its simple syntax and numerous libraries, which handle complex calculations and computations.

This article introduces Python libraries for various computational and analytics projects.

What is a Python library?

A Python library is a collection of pre-written codes grouped into unit files called modules. Organizing these codes into modules allows you to reuse and reorganize them. Also, it allows you to make your code more readable and easier to understand.

Typically, Python libraries have two classes. The standard Python library is a collection of modules with the Python interpreter. These modules provide vast functionalities, from basic data types to complex algorithms.

On the other hand, third-party Python libraries are not included with the interpreter but can be downloaded and installed separately. These libraries provide additional functionality not found in the standard library.

Next, we’ll review some of our top picks for the best Python libraries in 2024.

Basic libraries for data science

As more data becomes available, the need for efficient ways to analyze and interpret this data becomes increasingly important. Data scientists can use various libraries and tools to work with data sets of all sizes. In this section, we will introduce some of the most basic and commonly used libraries for data science.


NumPy is a Python package whose name stands for Numerical Python. Scientists and engineers use this library to conduct scientific computing to provide a high-performance, multidimensional array of objects and tools.


  • Scientific calculations. NumPy has many mathematical functions for calculating your data’s matrix operations, statistical mean, median, standard deviation, and more.
  • Data types. NumPy supports many data types, making it ideal for working with numerical data.
  • Speed. NumPy is fast and efficient, making it excellent for high-performance computing.
  • Application

  • Modeling the spread of disease. NumPy helps model the spread of disease by simulating the interactions between individuals in a population.
  • Simulating Brownian motion. With NumPy, scientists can simulate Brownian motion by generating random numbers and using them to update the position of particles.
  • Keras

    The Python library, Keras, is a high-level API for building and training deep learning models. It is easy to use and can run on top of TensorFlow, Theano, or Microsoft CNTK. Data scientists use Keras to create and train neural networks.


  • Excellent documentation. Keras helps you easily find clear, concise explanations behind various deep learning concepts.
  • Well supported. Keras is constantly improving because many companies and organizations contribute to its development.
  • Easy to use. Even if you're just starting with deep learning, you can quickly build models with Keras after a few tutorials.
  • Application

  • Text generation. Developers can use Keras to build models that generate novels or poems (like Shakespearean sonnets).
  • Time series prediction. Keras is useful for building models that forecast the price of stocks and other commodities on the financial market based on past data.
  • PyTorch

    PyTorch is an open-source library developed by Facebook's AI research group. Thanks to its computation library, automatic differentiation, and intuitive API, PyTorch enables data scientists to perform complex computations and operations on data, especially for deep learning and machine learning tasks.


  • Simple API. PyTorch enables fast and efficient transfer of models from the central processing unit to the graphics processing unit with an easy-to-use API.
  • Custom loaders. PyTorch makes creating custom data loaders for your specific needs easy.
  • Numerous machine learning models. It also provides a suite of pre-trained models that can help with various machine learning tasks, including Image 6, natural-language processing, and time series forecasting.
  • Application

  • Self-driving cars. Many leading companies in the autonomous driving industry use PyTorch when developing self-learning models for self-driving cars.
  • Clinical diagnosis. Medical researchers use PyTorch to develop new AI-based methods for detecting and diagnosing diseases.
  • SciPy

    SciPy is a Python-based open-source software ecosystem for mathematics, science, and engineering. In addition, SciPy has packages for linear algebra, integration, interpolation, signal processing, and many other mathematics operations.


  • Optimization. SciPy offers various optimization algorithms to find the optimal solution to a given problem.
  • Linear algebra. SciPy contains many functions for performing linear algebra operations.
  • Statistics. The SciPy library offers numerous statistical functions, making it easy to compute the statistical properties of data sets.
  • Application

  • Image processing. SciPy can help perform various image processing tasks, such as denoising, segmentation, and registration (like in face recognition applications).
  • Scientific visualization. Data scientists use the SciPy library to create high-quality 2D and 3D data visualizations. With such visualizations, data scientists present insights in interactive forms for easier interpretation.
  • Pandas

    Like SciPy, the Pandas Python library builds on NumPy and features various data structures and operations for data analysis and manipulation. For example, you can use Pandas to clean your data and another library to build your machine learning models.


  • Operates with missing data. Pandas provides various methods for handling missing data, such as filling in missing values with a placeholder or dropping fields containing missing data.
  • Handles mismatched data types. Pandas handles mismatched data types, allowing data scientists to focus on the task instead of worrying about data type inconsistencies.
  • Data processing methods. Pandas provides various methods for aggregating and transforming data. Such methods include grouping data by certain columns and applying mathematical operations to the columns.
  • Applications

  • Stock prediction models. Data scientists use Pandas to analyze existing data and create predictive models for forecasting stock prices.
  • Processing unstructured data. Pandas efficiently handles large, unstructured data sets and provides several powerful features—such as join or merge and data frame operation functionality.
  • Advertising. Business intelligence analysts use Pandas to create machine learning models that analyze customer data and provide insights for more productive marketing efforts.
  • Matplotlib

    Matplotlib is a python library created to make 2D plotting easier. Matplotlib allows you to create complex plots with just a few lines of code and integrates well with other python libraries (like NumPy and SciPy).


  • Control image resolution. Image resolution control is important because you want your figures to be as clear and sharp as possible. The higher the resolution, the better.
  • Improve detail in plots. By default, matplotlib will try to plot everything in your data; you can tell it to simplify your plot by ignoring certain data points.
  • Create multiple subplots. Matplotlib can be useful if you compare different data sets side by side. You can also use subplots to create different plot types in the same figure.
  • Application

  • Charts and graphs development. Data analysts and scientists use Matplotlib to create various charts and graphs, such as line graphs, bar charts, and scatter plots. These charts help simplify complex data distributions, making them understandable at a glance.
  • Animation production. Data analysts also use Matplotlib to create various animations to improve data visualization and storytelling. You can use this library to create lifelike visualizations of complex processes.
  • Libraries for machine learning

    Machine learning allows computers to learn from data by identifying patterns, which they use to predict future events or make decisions. Here are some vital Python libraries for machine learning.


    Scikit-learn is an open-source Python library that helps with machine learning tasks like classification, regression, and clustering. The library builds on NumPy, SciPy, and matplotlib and features various statistical modeling, data processing, and machine learning algorithms.


  • Cross-validation. Scikit-learn supports cross-validation, which is necessary for tuning machine learning models.
  • Built-in machine learning algorithms. Scikit-learn includes implementations of many popular machine learning algorithms, including logistic regression and k-means clustering.
  • Comprehensive documentation. The scikit-learn documentation is comprehensive and includes a wealth of examples and tutorials.
  • Application

  • Predictive maintenance. Use scikit-learn in machine learning models to predict when equipment will need maintenance and avoid costly downtime.
  • Credit scoring. Credit scoring predicts a borrower's creditworthiness or likelihood of repaying a loan. With scikit-learn, you can apply and build these credit-scoring machine learning models.
  • Theano

    Theano is efficient at performing mathematical operations on large arrays of data. Such abilities make this library ideal for training neural networks.


  • Theano is efficient. It can perform computations faster than many other deep learning libraries. That's because it uses the Just-In-Time (JIT) compilation system, a technology that makes file compiling much faster.
  • Theano is stable. Theano only rarely crashes or produces incorrect results because it uses static typing. In other words, all variables in Theano have a specific type, which allows it to check for errors at compile time.
  • Theano is easy to use. The library has a simple API that makes it easy to perform common deep learning tasks. Additionally, Theano has several built-in functions and classes that make common deep learning operations easier to perform.
  • Application

  • Numerical optimization. Theano can be useful for finding the minimum value of a function by gradient descent. Developers use that feature in various situations, such as when training a machine learning model to find optimal parameters.
  • Natural language processing. Theano library is excellent for building models that can learn to read and write in different languages.
  • TensorFlow

    Google Brain team members originally developed TensorFlow for internal use at Google. TensorFlow provides a variety of capabilities for data preprocessing, model training, and model deployment. Recently, it has also been used for developing recommender systems, self-driving cars, and deep learning algorithms.


  • Eager execution. This feature allows developers to experiment with TensorFlow code without compiling and running a separate graph.
  • Automatic differentiation. TensorFlow can automatically differentiate between operations to optimize and improve performance.
  • Python API. TensorFlow's Python API makes it easy to develop machine learning models in Python.
  • Application

  • Image recognition. TensorFlow helps create artificial neural networks capable of recognizing patterns in images. It is a valuable tool for businesses that need to process large amounts of images, such as security firms or online retailers.
  • Time series analysis. TensorFlow is useful for creating systems that can identify patterns in time series data. That is valuable for businesses that make predictions based on historical data (e.g., Airbnb and Spotify).
  • Libraries for data mining and natural language processing

    Some Python libraries not only collect data from websites but can also integrate the data with various NPL and artificial intelligence projects. Below are a few examples.


    Scrapy is an open-source framework that extracts data from website pages and documents. The library works by creating web crawlers that harvest targeted structured data from websites (e.g., email, gender, and mobile number).


  • Speed. Scrapy can quickly crawl and extract data from websites thanks to its asynchronous design. This feature makes it ideal for large-scale data scraping projects.
  • Extensibility. Scrapy’s modular design lets users easily extend the library with custom functionality, making it possible to tailor Scrapy to fit the specific needs of any project.
  • Ease of use. The library provides a simple API that helps scrapers use minimal codes.
  • Application

  • Data mining. Data scientists use Scrapy to extract data from websites without an API or requiring authentication. They can then store this data in a database for later analysis.
  • Lead generation. Scrapy helps scrape websites for contact information, such as email addresses and phone numbers. Sales teams use such information to generate leads for a sales team.
  • Pattern

    Pattern is similar to other libraries, but it has several advantages that make it worth considering for your data mining and NLP needs. For one, it is faster than NLTK and other libraries when processing large amounts of data.


  • Machine learning. Pattern includes a wide range of machine learning algorithms that are relevant for tasks like classification, clustering, and regression tasks.
  • Natural language processing. Pattern includes tools for working with text data, such as tokenization, stemming, and part-of-speech tagging.
  • Data visualization. Pattern includes various tools for visualizing data, including scatter plots, bar charts, and heat maps.
  • Application

  • Finding data trends. Pattern helps find trends in data sets. For example, analysts use it to find stock prices, sales, and economic data trends.
  • Predictive modeling. Besides locating trends in datasets, data scientists and machine learning developers also use Pattern to build predictive models. These models make forecasts based on trends they spot in the datasets.
  • Libraries for plotting and visualizations

    Visualizing data allows you to gain insights you otherwise wouldn’t see. This section highlights some of the most popular Python libraries for captivating data visualization.


    Seaborn is a python data visualization library that helps create beautiful visualizations. Built on top of the popular plotting library matplotlib, Seaborn takes care of some common issues users face while plotting data using matplotlib. For example, it has several functions for visualizing univariate and bivariate distributions.


  • Versatility. Seaborn can easily load and process data from a variety of sources.
  • Statistical plotting functions. The library provides a high-level interface for drawing attractive and informative statistical graphics.
  • Customization. Seaborn comes with several prebuilt themes that make it easy to create stunning visualizations with just a few lines of code.
  • Application

  • Visualize relationships between variables. Data analysts use Seaborn to plot linear and non-linear relationships between variables to help determine an accurate conclusion for the analysis.
  • Build informative plots. Programmers use Seaborn on Google Colab to create various plot types, including heatmaps, time series, and scatter plots. These visualizations allow analysts and business executives to get insights on data distribution easily.
  • Bokeh

    Bokeh is a Python package that enables interactive data plotting and visualization. The library's primary function is to help people create aesthetically pleasing and interactive visualizations and plots.


  • Interactive visualizations. Bokeh visualizations are interactive by default. In other words, you can zoom, pan, and hover your visualizations without writing code.
  • Rich graphic capabilities. Bokeh includes a rich set of graphics capabilities for creating eye-catching visualizations. The library enables data scientists and analysts to create multicolumn layouts, CSS styling, HTML5 forms, and plots with a Python back end.
  • Flexible and powerful API. Bokeh has a flexible and powerful API, allowing you to create stylish and functional visualizations.
  • Application

  • Create interactive data visualizations. Bokeh can help create interactive visualizations you can embed into web applications. The library is particularly useful for creating visualizations of large datasets that would be too unwieldy to view in a static format, making the data more understandable for C-suite leaders to make informed decisions.
  • Create dashboards. Use Bokeh to create interactive dashboards. Dashboards are a great way for nontechnical people to track data and see trends over time.
  • NetworkX

    The NetworkX toolkit is unique for creating and studying networked data structures. Because of its extensive data processing tools, data scientists use it to generate visualizations for complex networked data structures.


  • Classic graph algorithms. NetworkX supports directed and undirected graphs and tools to implement classic graph algorithms.
  • Graph literacy. NetworkX can read and write graphs in various formats.
  • Flexible indexing. NetworkX’s flexible indexing enables investigations of the graph as a whole or by its components.
  • Application

  • Research. Scientists and network engineers use NetworkX to study the structure of complex networks and analyze the properties of real-world networks.
  • Testing algorithms. You can also apply NetworkX libraries when generating artificial networks for testing algorithms.
  • What are the most common use cases for a Python library?

    Here are some of the most common use cases for Python libraries and how you might use them in your projects.

  • Machine learning and predictive modeling. Developers can use several python libraries to build a wide variety of models, including linear models, neural networks, and support vector machines. Such libraries include scikit-learn, TensorFlow, and Keras.
  • Web scraping applications. Web scraping involves extracting and exporting information from a website in portable files. You can easily scrape data from HTML and XML files using python libraries like Beautiful Soup. Other popular Python libraries for web scraping include PythonRequest, Scrapy, and Selenium.
  • Data analysis and manipulation. Python libraries like Pandas, matplotlib, and NumPy are popular for data manipulation and analysis and can be used to perform various tasks. For example, you can use them to clean data, compute summary statistics, and even create visualizations.
  • Is Python easy to learn and use?

    Yes, the Python programming language is relatively easy to learn. It is more straightforward than other languages like JavaScript and R. The Python programming language is widely known for its ease of use, readability, and many libraries that make coding easier.

    With Python programming, anybody can create and publish libraries for diverse data science projects. However, you should discuss your needs with an expert developer to get the best possible outcome for your application. You can explore Upwork for qualified python developers or learn about big data basics.

    Upwork provides a platform for independent professionals to connect to data science project managers. Visit Upwork to apply for various data science and machine learning jobs.

    Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.


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