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Top 15 Python Frameworks for Data Science in 2024

19th Jun, 2024
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    Top 15 Python Frameworks for Data Science in 2024

    As a seasoned data scientist, I understand the pivotal role data plays in our field. Handling vast amounts of data is essential for maximizing career opportunities. The exponential growth of data, with 55-65 percent being unstructured, as reported by Forbes.com, poses significant challenges for analysis. Raw Python can be cumbersome and time-consuming to work with, underscoring the necessity for Python frameworks.

    Python frameworks streamline workflows by offering reusable pre-written code. They facilitate code structuring for improved readability and maintenance. With numerous frameworks available, each boasting unique advantages, selecting the right one is paramount. In this article, I delve into the most anticipated Python frameworks for data science, outlining their features and benefits. Join me as I navigate through the landscape of Python frameworks, empowering you to make informed choices for your projects.

    What is a Data Science Framework?

    A data science framework is a group of small programs or libraries we can use on any computer. They work by themselves, together, or with anything for big jobs that need to get done. The central part is ready to work, so you don't have to make anything new. It has been done already for you, and all you need is to learn how to change them as per your preferences while creating software made just for your business requirements. Thus, it is an easy-to-build program that uses parts you can use again. This helps make your projects faster and simpler to finish. As Data Science has become more prevalent, online Data Science courses are much in demand. These courses provide students with the skills and knowledge they need to enter the field and make an impact.

    List of 15 Python Frameworks for Data Science in 2024 

    For data scientists, Python is one of the most popular programming languages due to its large number of high-quality frameworks that can be used for various purposes. Here are 15 of the finest Python frameworks for data science. 

    1. NumPy
    2. Pattern
    3. Scikit-Learn
    4. Keras
    5. Shogun
    6. Chainer
    7. SciPy
    8. Dask
    9. Scrapy
    10. PyTorch
    11. TensorFlow
    12. XGBoost
    13. Theano
    14. Natural Language Toolkit
    15. Dash

    15 Best Python Frameworks for Data Science

    1. NumPy

    NumPy is a powerful Python framework that provides high-performance multidimensional array support. NumPy's main object is the homogeneous multidimensional array, which is a grid of values with the same type (usually numbers).

    It also provides a set of functions that allows for efficient operations on arrays, including Math, Linear Algebra, Fourier Transforms, and random number generation. In addition, NumPy supports integration with C and Fortran code, making it easy to use legacy code in Python programs. 

    NumPy is widely considered the best Python framework for scientific computing, and it is used by many major organizations, including NASA and Google. 

    Features of NumPy  

    • Tools for reading and writing array data to disk. 
    • An efficient array object for storing and manipulating numerical data. 
    • A wide range of mathematical functions for performing operations on arrays. 
    • A rich set of functions for Linear Algebra, Statistics, Fourier Transforms, and random number generation.
    • It can be easily integrated with other scientific Python libraries such as SciPy and Matplotlib making it an ideal tool for numeric computing in Python. 

    2. Pattern 

    Among the various Python frameworks available, Pattern is particularly well-suited for Data Science tasks. It provides a comprehensive set of tools for Data Mining, Machine learning, and Natural Language Processing. In addition, Pattern is designed to be easy to use, with a focus on intuitive interfaces and straightforward syntax. 

    It also supports both vectorized and parallel computing, which is essential for working with large datasets. Additionally, pattern is open source and it is constantly being improved by a community of developers. 

    Pattern strikes the perfect balance between power and ease of use. It is an ideal choice for those who are just getting started with data science. For more experienced users, Pattern's powerful features help to accelerate the process of scientific discovery.

    Features of Pattern 

    • Pattern enables rapid development and helps you to get your Data Processing and Machine learning projects up and running quickly.
    • It provides a simple, consistent interface for working with data, robust support for vectorization and parallelization, and built-in support for popular Machine learning algorithms.
    • It is designed to be extensible, so you can easily add new features or integrate with other tools and libraries. 

    3. Scikit-Learn

    Scikit-Learn is one of the best Python Data Science frameworks for Machine learning. It provides a range of algorithms for tasks such as classification, regression, and clustering. It also includes tools for model selection and evaluation.

    Scikit-Learn is well-documented and easy to use, making it a good choice for both experienced data scientists and newcomers to Machine learning. In addition, the Scikit-Learn community is large and active, meaning that there is always someone to ask for help if you encounter problems. 

    Features of Scikit-Learn  

    • Access to a wide range of algorithms, including both traditional and deep learning models. 
    • Easy data preprocessing and normalization. 
    • Ability to handle both numerical and categorical data. 
    • Support for multiple evaluation metrics. 
    • Hyperparameter tuning tools to optimize model performance. 

    4. Keras

    Keras is a top-level API that makes it easy to build complex deep-learning models. With just a few lines of code, you can define and train complex neural networks. Keras is also designed to be extensible, so you can easily add new layers, models, and optimizers.

    The core data structure of Keras is a tensor, which is a multi-dimensional array. Tensors are the fundamental building blocks of deep learning. It also provides a number of convenient functions for pre-processing and loading data, as well as for visualizing results. In addition, Keras can be used with other frameworks such as TensorFlow and Theano to create even more powerful models. 

    Features of Keras  

    • Runs seamlessly on CPU and GPU. 
    • Allows quick and easy prototyping via user-friendliness, modularity, and extensibility.
    • A total of 10 pre-trained image classification models are included. 
    • Comes pre-packaged with industry-standard Machine learning datasets. 

    5. Shogun

    Shogun is a powerful, open-source Machine learning library that offers a wide range of algorithms for data analysis and predictive modeling. The library is written in C++ and provides bindings for several programming languages, including Python.

    Shogun is designed to be efficient and scalable, and it supports both linear and nonlinear models. Additionally, the library offers a number of features for preprocessing data, such as feature selection and dimensionality reduction.

    As a result, Shogun is well-suited for a wide variety of tasks, such as image classification and text mining. In addition, the library is constantly being updated with new features and improvements, bringing it to the top of the Python frameworks list. 

    Features of Shogun  

    • Support for a variety of data types, including real-valued, sequence, graph, and text data. 
    • A rich set of methods for preprocessing and visualizing data.
    • A wide range of classification, regression, and clustering algorithms.
    • Support for streaming data and online learning.
    • A flexible plugin system that allows users to extend the toolbox with their own algorithms and data types. 

    6. Chainer

    Chainer is a Data Science Python framework that was created in Tokyo by a robotics startup. Compared to frameworks like Tensorflow, it is much faster.

    Chainer helps a lot in debugging a neural network via its define-by-run neural network definition. Additionally it also supports CUDA implementation. You could run your code on multiple GPU’s with minimal effort. 

    Features of Chainer 

    • Chainer is faster compared to more popular frameworks like Tensorflow. 
    • We can use GPU easily with just few lines of code. 
    • It makes debugging a neural network easier. 
    • Highly flexible framework supporting different type of neural networks. 

    7. SciPy 

    As a data scientist, you are often engaged in tasks such as statistical modeling, data visualization, and Machine learning. While there are many different tools that you can use for these tasks, SciPy provides a powerful Python-based framework that can help you to accomplish these tasks more effectively.

    SciPy is a collection of modules that offer common functions for scientific computing. This includes modules for Linear algebra, Optimization, Integration, and Statistics. In addition, SciPy also provides extensive support for Data visualization and Machine learning. As a result, it is an essential tool for any data scientist as you can work more efficiently and unlock the full potential of your data. 

    Features of SciPy  

    • A free and open-source Python framework for Data Science that features various tools for scientific computing, numerical analysis, and Machine learning. 
    • Built on top of NumPy, and together they form a powerful combination for Data Science applications. 
    • It features many modules for doing things like Optimization, Linear algebra, Integration, Interpolation, and Statistics.
    • It also has a well-developed ecosystem of third-party packages that extend its functionality.

    8. Dask 

    Dask is a robust Python framework for data analysis and Machine learning. It provides high-level APIs for working with large datasets, allowing you to efficiently manipulate and process your data. Additionally, it allows you to parallelize your code, making it easy to scale your computations across multiple cores or servers.

    As a result, Dask can help you achieve significant speedups on your data processing tasks. In addition to its scalability, Dask also offers a number of other benefits. It integrates seamlessly with the popular Pandas library, making it easy to use Dask with your existing codebase. 

    It also comes with robust diagnostics and debugging tools, allowing you to quickly identify and fix issues in your code. Overall, Dask is an extremely powerful tool that greatly improves productivity when working with large datasets. 

    Features of Dask  

    • Dask allows you to work with large datasets in a distributed manner. 
    • Easy to use and can be installed with pip. 
    • It integrates well with other Python libraries such as NumPy, Pandas, and Scikit-learn. 
    • Includes support for distributed file systems, making it easy to scale up your computations to multiple machines. 
    • It provides a variety of methods for parallelizing your code, including multi-threaded and multi-process approaches.

    9. Scrapy 

    Data Science is a field that is all about extracting valuable insights from data. Scrapy being a powerful Python framework, makes it easy to scrape data from websites and other online sources.

    Scrapy works by crawling through websites and extracting the desired information. The extracted data can then be used for a variety of purposes, such as creating a database or generating reports.

    With Scrapy, data scientists can quickly and easily gather the data they need for their analysis. It is designed to be fast and efficient, and it provides many features that make web scraping easier, such as the ability to follow links and extract data from multiple pages automatically.

    Features of Scrapy  

    • Scrapy is a fast and efficient way to get data from websites. 
    • It has a simple interface that can be easily understood by non-programmers. 
    • You can use it to extract data from dynamic and static pages. 
    • Scrapy can also be used to scrape data from APIs. 
    • It is very flexible and can be customized to meet the specific needs of a project. 
    • It is well suited for large-scale data scraping projects. 

    10. PyTorch 

    PyTorch is a deep learning framework that puts Python first. It provides maximum flexibility and speed to make the most of your data. From data loading, preprocessing and visualization to creating and training models, everything in PyTorch is optimized for ease of use and extensibility. 

    PyTorch also supports distributed training, meaning you can train your models on multiple GPUs with ease. The framework also comes with a large number of pre-trained models that can be used for tasks such as image classification, natural language processing, and object detection.

    Features of PyTorch  

    • PyTorch offers a rich set of tools for debugging and optimization. 
    • Offers an intuitive API that makes it easy to develop complex models. 
    • Provides support for CUDA, which allows developers to take advantage of GPU acceleration. 
    • Integrates seamlessly with other popular Python libraries, such as NumPy and Pandas

    11. TensorFlow 

    TensorFlow is a powerful Machine learning platform that enables data scientists to build and train sophisticated models to improve the accuracy of predictions. It was originally developed by Google Brain team members. While TensorFlow can be used for a variety of tasks, it is primarily used for Deep learning and Artificial intelligence applications.

    The platform is highly scalable and can be deployed on a number of devices, from mobile phones to servers. TensorFlow provides a number of advantages for data scientists, including its flexibility, ease of use, and ability to handle complex data sets.

    In addition, TensorFlow is backed by a strong community of developers who are constantly improving the platform for users of Python frameworks. As a result, TensorFlow is an increasingly popular choice for data scientists who want to build and train Machine learning models. 

    Features of TensorFlow  

    • A rich set of libraries for data manipulation, preprocessing, and visualization. 
    • Support for a wide range of data types, including images, text, and time series data. 
    • GPU support for faster training of deep learning models. 
    • Automatic differentiation makes it easy to implement complex algorithms. 
    • TensorFlow is also highly scalable, making it possible to train large models on distributed systems. 

    12. XGBoost 

    XGBoost is a versatile Python framework for data scientists, providing them with the ability to perform complex computations on large datasets with ease. The framework is designed to be highly efficient, scalable, and easy to use, making it an ideal tool for data-intensive tasks such as Machine learning and Predictive modeling.

    XGBoost is built on top of the popular gradient boosting algorithm and offers several advantages over other similar frameworks. It is able to handle missing values in the data more effectively and has built-in support for parallel computing, making it well-suited for working with very large datasets.

    In addition, the XGBoost algorithm has been shown to outperform other gradient-boosting algorithms in terms of both accuracy and efficiency. As a result, XGBoost is quickly becoming the standard tool for data scientists who need to perform complex computations on large datasets. 

    When you become a successful data scientist with Data Science Bootcamp Certification, you are required to tweak every aspect of plots and charts, from the colors and fonts to the layout and design for your project. XGBoost is an incredibly powerful tool among Python frameworks for data visualization and making Data Science less complex. 

    Features of XGBoost  

    • XGBoost provides a convenient way to parallelize training across multiple cores, which can dramatically reduce training time. 
    • The efficient handling of data results in faster training times, while its scalability allows it to handle large datasets with ease.
    • Its flexibility allows it to be easily customized to fit the needs of specific applications. 

    13. Theano

    There are many different Python frameworks available for Data Science projects, but Theano is widely considered to be the best. Theano is a robust Python library for defining, optimizing, and evaluating mathematical operations on multidimensional arrays. It can also be used to create efficient Machine learning models.

    The main advantage of Theano over other frameworks is its ability to optimize code for speed. This is essential for Data Science projects where computationally expensive operations need to be performed repeatedly.

    Theano has excellent support for GPU computing, which can further speed up code execution. In addition, Theano comes with a number of built-in mathematical functions that make it easy to perform numerical operations on arrays. 

    Features of Theano 

    • Automatic Differentiation: Theano can automatically compute the gradients of mathematical expressions with respect to variables.
    • Support for NVIDIA CUDA GPUs: Theano can leverage the computational power of NVIDIA GPUs to speed up matrix operations. This is especially helpful when working with large datasets. 
    • Portable and Extensible: Theano is written in portable Python code and can run on many different platforms. It is also extensible, allowing users to define their own custom operations. 

    14. Natural Language Toolkit

    The Natural Language Toolkit (NLTK) is a powerful Python framework for Data Science. It provides extensive libraries and tools for working with text data, including support for tokenization, part-of-speech tagging, and parsing. NLTK also includes a wide range of datasets and educational resources.

    NLTK community has created a number of open-source projects that extend the framework's functionality. It is an ideal tool for any data scientist who needs to work with text data. With its extensive libraries and tools, it makes working with text data easy and efficient. In addition, NLTK community has a wealth of knowledge and experience that can be invaluable to any data scientist. 

    Features of Natural Language Toolkit 

    • NLTK gives you access to more than 50 corpora and tools to help you work with data. 
    • NLTK is widely used in academic and commercial settings and is open source. 
    • It consists of a number of modules, each of which addresses a different task in natural language processing.
    • It also includes a collection of sample data, which can be used to get started with the library and for developing and testing new algorithms. 

    15. Dash

    Dash is a powerful Framework for Python Data Science for building web applications. It is built on top of Flask, Plotly.js, and React.js. Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It is also suitable for powering complex dashboards that require multiple data sources and user interactions.

    Dash framework is easy to read and understand, making it a great choice for beginners and experienced developers alike. What sets Dash apart from other Python frameworks is its use of declarative syntax, which makes it easy to build complex apps without having to write extensive amounts of code. In addition, Dash apps are interactive by default, which makes them more engaging and informative than static web pages. 

    Features of Dash  

    • Built-in support for Plotly.js charts and maps. 
    • A rich set of interaction components (including buttons, sliders, and dropdowns). 
    • Support for multiple users with different permissions. 
    • Customizable themes and layouts. 
    • A built-in development server for previewing your app before deployment. 

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    Why Python for Data Science?

    Here are some key reasons why the Python framework for data science is a prominent choice for developers:

    1. Ease of Learning and Use

    Python is easy to understand and use, making it good for newbies. It lets developers work fast, too. People find it simple to learn, which makes it a nice option for those joining the data science world.

    2. Rich Ecosystem of Libraries

    Python has an extensive collection of tools and libraries for data science and machine learning, which makes it easier to work and create code-efficient code. Some of the most popular libraries and Python frameworks for data science include:

    • NumPy and Pandas: For data analysis and manipulation.
    • Matplotlib and Seaborn: For data visualization.
    • Scikit-learn: For machine learning tools and ways.
    • TensorFlow and PyTorch: For deep learning.

    3. Versatility

    Python is a flexible language that isn't only used for data science. It can be used in many things, like making websites, automating tasks, and writing scripts. Python is useful because it can mix data science jobs with other parts of a project or group. This makes it easier to work together and get things done better. I have included and explained the data science Python framework list in the blog below.


    As an experienced data scientist, I've found Python's versatility invaluable for various tasks, from data wrangling to machine learning. Python's framework enhances efficiency and code organization, making it indispensable in our field.

    In this article, I'll highlight 15 popular Python frameworks for data science. While these options aren't exhaustive, they provide a solid foundation for your projects. Join me as I delve into the world of Python frameworks, sharing insights gained through firsthand experience.

    If you're looking to upskill in this area, KnowledgeHut's online Data Science courses will teach you everything you need to know about Data Science using Python. With our help, you will be on your way to mastering one of the most versatile and powerful programming languages out there.

    Frequently Asked Questions (FAQs)

    1Which Python IDE is best for Data Science?

    When it comes to Python IDEs for Data Science, there are a few key features to look for. First and foremost, IDE should support scientific computing libraries like NumPy and pandas.

    It should also offer features like code completion and debugging to save time and avoid errors. With these criteria in mind, let's take a look at three of the most popular Python IDEs for Data Science. 

    1. Spyder: It is a free and open-source IDE that was specifically designed for Data Science. It can be customized to work with different libraries and tools.
    2. PyCharm: It is another popular Python IDE that offers powerful development capabilities.
    3. Visual Studio Code: It is a general-purpose IDE that offers good support for scientific computing libraries. 
    2Is Python or C++ better for Data Science?

    There is no definitive answer to this question as it depends on the specific needs of the data scientist. That said, Python is generally considered to be the most versatile language, while C++ is more performant.

    For example, due to its concise syntax and extensive libraries, Python is widely used for prototyping and quick development. C++, on the other hand, is better suited for production code as it offers cleaner abstractions and higher compile-time safety.

    In terms of specific Data Science applications, Python is often used for Machine learning tasks such as training and testing models, while C++ is more commonly used for implementing algorithms and processing large datasets. 

    3Can we use Django for Data Science?

    Django is a versatile framework that can be used for a variety of tasks, including Django Data Science. Django's built-in ORM (Object Relational Mapper) makes it easy to work with databases, and its powerful templating system makes it ideal for creating dynamic web pages. 

    In addition, Django's extensive library of third-party packages gives data scientists access to a wide range of tools and libraries. However, it should be noted that Django is not a dedicated Data Science tool.

    Some features that are commonly used in Data Science, such as advanced statistics and Machine learning, are not readily available. For this reason, data scientists considering using Django should be aware of the limitations. Nevertheless, Django remains an attractive option for Data Science due to its flexibility and ease of use. 

    4How many Python frameworks are there?

    While there is no definitive answer, it is safe to say that there are at least dozens of Python frameworks for data analysis in active development. Some of the more popular ones include Django, Flask, and Pyramid. However, new frameworks are constantly being created, and it can be difficult to keep track of all of them.

    In addition, many frameworks are designed for specific purposes, such as web development or scientific computing. As a result, the number of frameworks is constantly changing, and it is difficult to provide an exact figure. Despite this, one thing is clear: there is no shortage of Python frameworks to choose from. 


    Abhresh Sugandhi


    Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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