Top 20+ Python Frameworks and Tools to Learn in 2024

With the increasing popularity of Python over the years, most developers have switched their careers to become Python developers. Several reasons made Python one of the implemented languages, and its simplicity in creating complex code with grace is on the top.

Python’s simplicity, coupled with its vast ecosystem of libraries and frameworks, makes it a top choice for fields ranging from web development to data science and artificial intelligence.

As we move into 2023, it’s more important than ever for Python developers to stay updated with the latest tools that can make their work more efficient and effective.

Here, we delve into some of the most essential tools that every Python developer should get familiar with in 2023. So let’s get started.

Machine Learning Python Development Tools

1. Langchain

Source-

LangChain is a platform designed to ease the construction of complex language model applications. It equips users with a variety of tools, components, and interfaces, making it easier to handle language model interactions, link multiple components together, and merge extra resources such as APIs and databases.

Core features of LangChain-

• Language Model Management- Discusses managing and optimizing language models’ prompts, providing a universal interface for them and useful tools for handling these models.
• Interconnected Processes- Explores not just individual language model calls but also sequences involving various utilities, offering a standardized interface for these chains and extensive tool integrations for common use cases.
• Enhanced Data Generation- Concerns with specific chain types that incorporate external data sources for generation steps, such as text summarization and question/answer sessions over particular databases.
• Intelligent Agents- Looks at how a language model decides on and executes actions based on observations, continuously repeating this process. It outlines standard interfaces, agent options, and illustrative agent examples.
• Persisting Memory- Focuses on maintaining states between chain or agent calls, offering a standardized interface, a variety of memory solutions, and practical examples of memory use in chains/agents.
• Model Evaluation- Addresses the challenge of assessing generative models with traditional metrics by introducing the use of language models themselves for evaluations. Provides prompts and chains to assist in this endeavor.

Benefits to Python Developers-

• Adapting prompts to meet specific requirements
• Developing advanced components for chain links
• Tailoring code for unique application development
• Incorporating models for enhanced data generation and accessing premium language model applications, such as text-davinci-003
• Utilizing flexible components to assemble solutions for individual needs
• Controlling and directing context for enhanced precision and user experience

2. Pandas

Pandas is a popular Python library for data manipulation and analysis. It provides data structures and functions needed to manipulate structured data.

Core Features of Pandas-

• DataFrame Object- Pandas provides a DataFrame object for data manipulation with integrated indexing.
• Handling of Data- Pandas can handle a variety of data such as missing data, date, and time series data, etc.
• Data Alignment- Pandas allows intelligent label-based slicing, fancy indexing, and subsetting of large data sets.
• Flexible Reshaping and Pivoting of Data Sets- Pandas provides functionality to reshape and pivot data sets.
• High Performance- Pandas is highly optimized for performance, with critical code paths written in Cython or C.

Benefits to Python Developers-

• Pandas simplifies the handling and manipulation of structured data, like data loaded from CSV files or databases.
• Its powerful data structures make data manipulation and transformation easy and intuitive.
• The DataFrame object provided by pandas makes data manipulation more simple and convenient.
• Its functionality for handling missing data allows developers to easily clean and preprocess data.
• Pandas’ performance optimizations ensure efficient computation even for large datasets.

3. TensorFlow

is an open-source machine learning framework developed by Google. It’s highly scalable and allows developers to create dataflow graphs — structures that describe how data moves through a graph or a series of processing nodes.

Core Features of TensorFlow-

• Scalability- TensorFlow can run on multiple CPUs, and GPUs, as well as on mobile devices, and it can scale from a single machine to a whole cluster.
• Flexible- TensorFlow provides a flexible architecture, allowing you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device.
• Visual Debugging- TensorBoard, a suite of visualization tools provided by TensorFlow, enables you to understand, debug, and optimize the program.
• Large Community and Ecosystem- TensorFlow has a large community that contributes to expanding its rich ecosystem.
• Robust ML Production Anywhere- TensorFlow allows easy model building, robust ML production anywhere, and powerful experimentation for research.

Benefits to Python Developers-

• TensorFlow’s scalability allows developers to write code that can run on various machines without alteration.
• Its flexibility allows easy deployment of machine learning models across various platforms.
• TensorBoard assists in the debugging and optimization of machine learning models.
• The large community support means better documentation, regular updates, and more third-party libraries.
• It helps in building and deploying robust, production-ready machine learning models.

4. Hugging Face Transformers

Hugging Face Transformers is a state-of-the-art general-purpose library for Natural Language Processing (NLP). It provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, summarization, translation, text generation, and more.

Core Features of Hugging Face Transformers-

• Pretrained Models- Hugging Face Transformers provides a collection of thousands of pre-trained models in over 100 languages.
• Model Architectures- It includes most of the state-of-the-art models architectures like BERT, GPT-2, T5, DistilBert, and RoBERTa.
• Multi-Language- Hugging Face Transformers supports over 100 languages.
• Pipeline API- It provides a high-level, easy-to-use, API for doing inference over a variety of downstream tasks.
• Deep Learning Framework Compatibility- Hugging Face Transformers is compatible with both PyTorch and TensorFlow.

Benefits to Python Developers-

• Developers can leverage Hugging Face’s vast collection of pre-trained models to build applications with advanced NLP features.
• It provides state-of-the-art models which can be fine-tuned on specific tasks to achieve high accuracy.
• Hugging Face Transformers supports many languages, making it suitable for developing global applications.
• The easy-to-use API allows developers to quickly and efficiently use these models in applications.
• Its compatibility with popular deep learning frameworks like PyTorch and TensorFlow gives developers flexibility.

5. NumPy

NumPy (Numerical Python) is the foundational library for scientific computing in Python. It provides support for arrays, matrices, and a host of mathematical functions to operate on these data structures.

Core Features of NumPy-

• Multi-dimensional Arrays- NumPy provides support for multi-dimensional arrays and matrices, along with a host of functions to perform operations on these arrays.
• Mathematical Functions- NumPy provides useful mathematical functions like sine, cosine, and exponential, along with basic arithmetic operations like addition, multiplication, and square root.
• Random Number Capabilities- NumPy includes functions to generate random numbers, which can be extremely useful in data analysis and machine learning tasks.
• Interoperability- NumPy arrays can interoperate with a wide variety of data formats.

Benefits to Python Developers-

• NumPy’s array objects can be up to 50x faster than traditional Python lists, which is especially beneficial for large-scale calculations.
• Its ability to carry out complex mathematical operations enables developers to perform advanced AI computations.
• NumPy’s random number generation capabilities are useful in many machine learning tasks.
• Interoperability with different data formats ensures that developers can seamlessly convert data to NumPy arrays, perform operations, and convert back if necessary.

6. SciPy

SciPy is a powerful Python library used for mathematical and scientific computing. It builds on NumPy by adding more sophisticated capabilities and data structures.

Core Features of SciPy-

• Advanced Compute Capabilities- SciPy offers advanced computation capabilities such as integration, differentiation, optimization, and much more.
• Multi-dimensional Image Processing- SciPy provides multi-dimensional image processing capabilities.
• Signal Processing- It includes signal processing tools for filtering, spectral analysis, etc.

Benefits to Python Developers-

• SciPy’s advanced computational capabilities make it indispensable in mathematical, scientific, and engineering computations.
• Its image and signal processing tools enable developers to work on complex AI-related tasks such as image recognition and signal analysis.
• Being built on top of NumPy, it seamlessly works with NumPy arrays and provides more advanced computing capabilities.

7. Scikit-learn

Scikit-learn is one of the most popular machine-learning libraries in Python. It provides a range of supervised and unsupervised learning algorithms via a consistent interface.

Core Features of Scikit-learn-

• Supervised and Unsupervised Learning Algorithms- Scikit-learn provides simple and efficient tools for data mining and data analysis, including a variety of machine learning models, including SVM, Random Forests, Gradient Boosting, k-means, etc.
• Cross-validation- Scikit-learn has functions for cross-validation to better estimate the performance of your models.
• Feature Extraction- It provides valuable tools for feature extraction from images and text.
• Datasets- Scikit-learn includes several standard datasets, for instance, the iris and digits datasets for classification and the Boston house prices dataset for regression.

Benefits to Python Developers-

• Scikit-learn’s comprehensive collection of algorithms makes it invaluable for machine learning tasks.
• Its cross-validation feature helps developers create more accurate models by avoiding overfitting or underfitting.
• Scikit-learn’s feature extraction tools are especially useful in tasks such as natural language processing or image analysis.
• Its inclusion of standard datasets makes experimenting with different algorithms easy.

8. Python Ray

Python Ray is a powerful open-source library for parallel and distributed Python. It simplifies scaling your applications and helps speed up the computation process. With a robust set of features, Python Ray has become indispensable for Python developers.

Core Features of Python Ray-

• Simple Primitives for Building and Running Applications- Python Ray enables you to develop complex distributed applications using simple primitives like tasks (asynchronous functions) and actors (mutable state).
• Parallel and Distributed Computing- Ray provides support for both parallel and distributed computing, allowing you to run your code across multiple processors or even multiple machines with little modification.
• Scalability and High-Performance Execution- Python Ray is designed to scale from a single machine to a cluster without changes to your application code, providing high-performance execution on large-scale data.
• Built-in Libraries- Python Ray comes with several high-level libraries for reinforcement learning (RLlib), hyperparameter tuning (Tune), distributed training (Ray SGD), and more.
• Fault-Tolerance and Resource Management- Ray handles machine failures and manages computing resources, freeing developers from the necessity of manual intervention and increasing the resilience of your applications.

Benefits of Python Ray for Developers-

• Increased Efficiency- Python Ray allows developers to write distributed computations quickly and efficiently, significantly reducing the development cycle.
• Flexibility- With its flexible API, developers can build a wide variety of applications, ranging from simple scripts to complex distributed systems.
• Strong Community Support- Python Ray has a growing community of users and contributors, providing a wealth of knowledge, support, and regular updates.
• Seamless Integration- Python Ray can be easily integrated with existing Python libraries and frameworks, enabling developers to leverage its power without revamping their whole tech stack.
• Advanced Capabilities- Python Ray’s high-level libraries offer advanced capabilities like reinforcement learning and hyperparameter tuning, providing Python developers with cutting-edge tools for their tasks.

9. Streamlit

Streamlit is a fast, user-friendly open-source library for building custom web apps for machine learning and data science. It provides an easy and efficient way to turn data scripts into shareable web apps.

Core Features of Streamlit-

• Rapid Prototyping- Streamlit’s design philosophy prioritizes speed and ease of use, making it an excellent tool for quickly prototyping data apps.
• Simplicity- With Streamlit, creating a web app is as simple as writing Python scripts. There’s no need for front-end languages like HTML, CSS, or JavaScript.
• Data-Friendly- Streamlit supports various data formats, making it easy to work with data from different sources. It also provides robust support for visualizations, which are crucial for data exploration and analysis.
• Interactive Widgets- Streamlit includes interactive widgets such as sliders, buttons, and text inputs, allowing developers to build interactive applications without the need for a callback function.
• Hot-Reloading- Streamlit automatically updates and reruns your script each time you modify it, making the development process more seamless and efficient.

Benefits of Streamlit for Developers-

• Ease of Use- Streamlit’s intuitive design means developers can build interactive, data-centric web apps quickly and without needing extensive web development knowledge.
• Collaborative Environment- Streamlit apps can be shared easily, creating a collaborative environment where teams can work together and stakeholders can be kept informed.
• Time-Saving- With Streamlit, developers can focus on what matters most: the data and the underlying computations. This leads to increased productivity as they spend less time on boilerplate code.
• Efficient Iteration- Streamlit’s hot-reloading capability enables efficient iteration, as developers can instantly see the impact of their changes.
• Flexible Deployment- Streamlit apps can be deployed on most cloud platforms, making it possible to distribute apps to users with minimal hassle.

10. ELI5

ELI5 is a Python library that allows developers to visualize and debug machine learning classifiers and explain their predictions. It provides support for various machine learning frameworks and packages.

Core Features of ELI5-

• Model Explanation- ELI5 helps in understanding how machine learning models make predictions. It provides an explanation of prediction results in a human-readable format.
• Support for Multiple Libraries- ELI5 works with many popular machine learning frameworks such as scikit-learn, XGBoost, LightGBM, and others.
• Feature Importance- ELI5 provides ways to understand feature importance and how each feature contributes to the final prediction.
• Model Debugging- It offers various tools to diagnose potential issues in machine learning models and improve their performance.
• Integration with IPython- ELI5 integrates well with IPython, which is particularly useful when doing exploratory data analysis.

Benefits of ELI5 for Developers-

• Transparency- Developers can better understand how their machine learning models are working and explain the models’ behavior to non-technical stakeholders.
• Improved Model Performance- By understanding feature importance and model behavior, developers can debug their models and enhance their performance.
• Collaboration- ELI5’s explanations are easy to understand, which improves communication between team members and promotes collaboration.
• Speed- ELI5 provides quick and easy ways to inspect machine learning models, saving developers time.
• Flexibility- Developers can customize explanations to suit their specific needs and use cases.

11. Bob

Bob is a free signal-processing and machine-learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland.

Core Features of Bob-

• Easy to Use High-Level API- Bob provides an easy-to-use API to implement various machine learning algorithms, including support vector machines, k-means clustering, Gaussian mixture models, etc.
• Broad Functionality- Bob includes a wide range of functionalities, from basic data processing (like image and audio signal processing) to implementing and evaluating complex machine learning architectures.
• High Performance- Bob is designed for high performance, making it suitable for large-scale machine learning tasks.
• Support for Reproducible Research- Bob aids in reproducing scientific research, with a strong emphasis on clear protocols and a unified API.
• Interoperability- Bob works well with other Python libraries such as NumPy, SciPy, and Matplotlib.

Benefits of Bob for Developers-

• Comprehensive- Bob covers a wide array of tasks, from preprocessing to advanced machine learning, making it a comprehensive tool for developers.
• Reproducibility- Bob promotes reproducible research, which helps in validating the findings and reusing the code for future studies.
• Performance- Developers can run complex machine-learning tasks more efficiently using Bob’s high-performance functionalities.
• Consistency- The uniform API makes the use of Bob consistent and easy to learn.
• Community Support- Bob has a supportive community that contributes to its development and provides regular updates.

UI development Python Tools

12. Flask

Flask is a lightweight and flexible microweb framework for Python. It is termed a micro framework due to its simplicity and core focus on small, single-purpose services.

Core Features of Flask-

• Simplicity- Flask is simple to set up and use, making it ideal for beginners and small-scale applications.
• Flexibility- It provides flexibility to the developer to choose their tools and libraries.
• RESTful Request Dispatching- Flask provides a quick and straightforward method to build RESTful services.
• Unicode-Based- Flask supports Unicode which helps in processing text from HTTP requests.
• HTTP Request Handling- Flask provides a request object for easy handling of HTTP requests.

Benefits to Python Developers-

• Flask’s simplicity makes it easy for developers to quickly build web applications.
• Its extensibility and flexibility allow developers to use the tools they are most comfortable with.
• Flask’s Unicode support helps in the seamless processing and output of text data.
• With built-in support for unit testing, Flask helps in maintaining code quality.
• It promotes building lightweight, maintainable, and scalable applications.

13. Django

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel.

Core Features of Django-

• MTV Architectural Pattern- Django follows the Model-Template-View architectural pattern.
• Admin Interface- Django provides a ready-to-use admin interface for managing the application data.
• Database Support- It supports a wide range of databases, including PostgreSQL, MySQL, SQLite, and Oracle.
• Security- Django includes various protections against attacks like SQL Injection, Cross-site Scripting, Cross-site Request Forgery, and Clickjacking.
• Scalability- Django is designed to help developers take applications from concept to completion as quickly as possible, and it can scale to handle heavy traffic demands.

Benefits to Python Developers-

• Django allows developers to build web applications quickly with a clean and pragmatic design.
• Its powerful admin interface helps developers to manage application data effectively.
• Django’s security features help developers to build secure web applications.
• With its wide range of database support, Django gives developers flexibility in choosing the database that fits their needs.
• Django’s scalability ensures that your web applications can grow with your user base.

14. Pyramid

Pyramid is a small, fast, down-to-earth Python web framework. It is developed as part of the Pylons Project.

Core Features of Pyramid-

• Minimalistic- Pyramid is a lightweight framework that takes a “pay only for what you eat” approach.
• URL Generation- Pyramid can generate URLs, preventing the need for hardcoding URLs into your applications.
• Extensible Configuration- Pyramid provides a global configuration object that can be extended by the applications.
• Support for Security- Pyramid has built-in security features like authentication and authorization.
• Flexible Authentication and Authorization- The pyramid allows for a flexible approach to associating users with actions and resources.

Benefits to Python Developers-

• Pyramid’s minimalistic design makes it easy to understand and use, especially for beginners.
• Its URL generation helps maintain your application and keeps your code clean.
• Pyramid’s extensible configuration allows for better control over your application.
• It comes with built-in security features, which help developers create secure web applications.
• Pyramid’s flexibility allows developers to choose tools and libraries that best fit their project needs.

15. Bottle

Bottle is a speedy, straightforward, and lean micro web framework for Python that adheres to the WSGI standard. Its distribution is streamlined as a single module file and it operates independently, requiring no additional dependencies beyond the Python Standard Library.

Core Features of Bottle-

• URL Routing- The bottle is equipped with the ability to dispatch requests, inclusive of support for URL parameters.
• Template Support- Bottle endorses a swift, Python-oriented inbuilt template engine. In addition, it also accommodates mako, jinja2, and cheetah templates.
• Utility Functions- This framework facilitates easy access to numerous HTTP-related elements like form data, file uploads, cookies, headers, and other metadata.
• Server Compatibility- Bottle comes with an integrated HTTP development server and can be utilized with a range of other servers such as paste, fapws3, Bjoern, Google App Engine, cherrypy, or any WSGI-compliant HTTP server.

Benefits to Python Developers-

• Bottle’s simplicity makes it a good choice for building simple personal applications, prototyping, and learning the organization of web frameworks.
• Its built-in templating system allows developers to generate data-driven web pages.
• Bottle’s routing system allows for clean and dynamic URLs.
• As a microframework, Bottle is light on resources and can be used in resource-restricted environments.
• The built-in server is helpful for development, and it is easy to switch to a more robust server for production applications.

16. Kivy

Kivy is an open-source Python library for the rapid development of multi-touch applications. It’s highly adaptable and can run on Android, iOS, Linux, OS X, and Windows.

Core Features of Kivy-

• Multi-touch- Kivy is built to handle multi-touch, gestures, and other touch features.
• Cross-platform- Kivy applications can be developed for Android, iOS, Linux, OS X, and Windows.
• Custom UI- Kivy comes with a custom-built UI toolkit that provides multi-touch compatible widgets.
• GPU Acceleration- Kivy utilizes OpenGL to maximize hardware acceleration.
• Versatile- Kivy supports a myriad of input devices and protocols, you can even use it with the Raspberry Pi.

Benefits to Python Developers-

• Kivy allows Python developers to build multi-touch applications. It’s particularly good for applications that require multi-touch, gestures, and other modern touch features.
• Kivy runs on multiple platforms, and developers can run their code with little to no modifications on all supported platforms.
• The use of OpenGL for hardware acceleration provides a fluid and responsive user experience.
• Kivy’s custom UI toolkit allows for the development of unique user interfaces.

17. BeeWare

BeeWare is a collection of tools and libraries for building user interfaces. With BeeWare you can write your app in Python and then deploy it on multiple platforms, including iOS, Android, Windows, MacOS, Linux, Web, and tvOS.

Core Features of BeeWare-

• Cross-platform- Write your code once and deploy it on multiple platforms.
• Native User Interfaces- With BeeWare your apps have a native user interface, and they behave as users expect.
• Full-stack Python- BeeWare apps are just Python, so you can leverage the Python ecosystem.
• Graphical User Interface- BeeWare has tools and libraries to develop graphical interfaces.
• Sustainability- BeeWare is a community-driven project. It depends on financial contributions to pay for its development.

Benefits to Python Developers-

• BeeWare enables Python developers to build applications with native user interfaces.
• One significant advantage of using BeeWare is its ability to use Python’s ecosystem.
• BeeWare’s sustainability provides a long-term perspective for the project.
• BeeWare allows for cross-platform development, significantly reducing the time and resources needed to develop apps for different platforms.

Productive Utilities Python Tools

18. Black

Black is an uncompromising, opinionated code formatter that automatically formats your Python code to a consistent style, making your code more readable and maintainable.

Core Features of Black-

• Automatic Code Formatting- Black follows the PEP 8 style guide and formats your code automatically so you don’t have to.
• Consistent Code Style- With Black, code formatting is consistent across your entire project.
• Syntax Validation- Black checks your Python code syntax before formatting it.
• Fast and Deterministic- Black formats your code quickly and always produces the same output for the same code.
• Easy to Integrate- Black can be easily integrated with pre-commit hooks and continuous integration pipelines.

Benefits to Python Developers-

• Black saves time and effort by relieving developers from the mundane task of code formatting.
• By enforcing a uniform code style, Black improves the readability and maintainability of your codebase.
• As it integrates well with most development environments, using Black can enhance your overall development workflow.
• Black’s syntax validation helps in catching bugs earlier in the development cycle.
• Developers can focus more on logic and problem-solving, as opposed to stylistic concerns.

19. PyTest

PyTest is a full-featured, scalable testing framework for Python. It provides a robust model for creating simple and complex functional tests.

Core Features of PyTest-

• Easy-to-write- PyTest simplifies writing compact tests and enables you to write test cases in a very natural way.
• Powerful Fixture System- PyTest allows you to define and use fixtures for test setup and teardown, improving test isolation.
• Detailed Reports- PyTest provides detailed reports about test failures, making debugging easier.
• Automatic Discovery of Tests- PyTest can automatically detect your tests, provided they follow simple naming conventions.
• Parametrized Testing- PyTest supports parametrized testing, making it easy to test a function with multiple sets of input values.

Benefits to Python Developers-

• PyTest’s ease of use helps developers quickly create tests, ensuring thorough code coverage.
• It promotes clean and robust test cases that improve the stability of the codebase.
• Its detailed reports enhance debugging efficiency.
• PyTest’s automatic discovery feature streamlines the testing process.
• The parametrized testing feature ensures that the functions are tested exhaustively, leading to reliable, bug-free code.

20. Jupyter Notebook

Jupyter Notebook is an open-source web application that enables you to create and share documents that contain both code (e.g., python or R) and rich text elements (paragraphs, equations, figures, links, etc.).

Core Features of Jupyter Notebook-

• Interactive Coding Environment- Jupyter Notebook provides an interactive environment where you can write and execute code, explore results, visualize data, and communicate findings.
• Language Agnostic- While popular with Python users, Jupyter supports numerous languages such as R, Julia, and Scala.
• Rich Display System- It provides a rich display system for rendering your data in HTML, images, videos, LaTeX, etc.
• Easy to Share- Notebooks can be easily shared with others using email, Dropbox, GitHub, and the Jupyter Notebook Viewer.
• Integration with Big Data Tools- Jupyter Notebooks can be integrated with big data processing tools like Apache Spark.

Benefits to Python Developers-

• Jupyter Notebook provides an excellent platform for iterative development and data analysis.
• It promotes learning by doing, which is crucial for understanding complex data operations and algorithms.
• The notebooks are great for sharing and explaining code, which is particularly beneficial for collaborative projects and data science teams.
• It can execute shell commands, making it a powerful tool for combining software development and data analysis.
• By integrating with big data tools, Jupyter Notebook provides a unified workflow for data preprocessing, modeling, and visualization.

21. Poetry

Source

Python Poetry is an innovative tool that aids Python developers in managing dependencies and packaging. It incorporates an integrated virtual environment and superior conflict-resolution capabilities to streamline the development process. The tool is actively updated, offering new features and quick bug fixes. Poetry also simplifies package management and enforces Semantic Versioning, thereby ensuring structured version control and release cycles.

Core Features of Poetry-

• Simplified Dependency Handling- Poetry minimizes the need for manual setup, unlike Pip, making routine tasks simpler and more user-friendly.
• Enhanced Resolver Capability- With its advanced dependency resolution system, Poetry effectively deals with conflicts and avoids version pinning issues.
• Built-In Virtual Environment- Compared to Pip and Pipenv which rely on the ‘venv’ module, Poetry has a less complicated built-in virtual environment setup.
• Regular Updates and Maintenance- Being a newer platform, Poetry receives frequent updates, bug fixes, and additional functionalities, indicating active development.
• Efficient Package Management- With Poetry’s PyPI, package management becomes easier due to its integrated commands, saving significant time in the long run.

Benefits to Python Developers-

• Simplifies dependency management for streamlined project setup.
• Enhanced conflict resolution improves stability and reliability.
• An integrated virtual environment reduces configuration hassles.
• Frequent updates provide new functionalities and timely bug fixes.
• Efficient package handling saves developers time in the long run.

22. Pylint

Pylint is a Python static code analysis tool that looks for programming errors, helps enforce a coding standard, checks for code smells, and offers simple refactoring suggestions.

Core Features of Pylint-

• Code Errors Check- Pylint is capable of checking if a module, function, class, method, or variable is well-defined.
• Coding Standard- Pylint helps in enforcing coding standards. It’s highly configurable and customizable.
• Refactoring Suggestions- It provides suggestions to refactor code, improving its maintainability and readability.
• Extensibility- Pylint can be extended by writing Python modules and plugins.
• IDE Integration- Pylint can be integrated with various IDEs like PyCharm, VS Code, and Atom.

Benefits to Python Developers-

• Pylint assists in maintaining high coding standards and quality, which leads to less error-prone and more maintainable code.
• By providing refactoring suggestions, it helps improve the structure and design of your code.
• Pylint’s extensibility means it can be customized to better suit your project’s needs.
• Integration with popular IDEs allows for seamless usage within your existing development environment.
• It aids in continuous code review, which leads to better coding habits and practices.

23. SQLAlchemy

SQLAlchemy serves as a comprehensive SQL toolkit and Python-based object-relational mapping (ORM) system, offering a robust set of persistent patterns commonly used in enterprise-level applications.

Core Features of SQLAlchemy-

• ORM and Core- SQLAlchemy provides two modes of usage- ORM (Object Relational Mapper) and Core (SQL Abstraction Layer).
• Data Manipulation through Python Classes- SQLAlchemy allows the manipulation of SQL data through Python classes.
• SQL Expression Language- It provides a generic way to create, select, update, and delete statements.
• Schema Centric- SQLAlchemy uses a schema-centric approach, so the schema is defined in Python code.
• Transaction Management- It provides a transactional interface that can handle transactions and errors in a unified way.

Benefits to Python Developers-

• SQLAlchemy brings the full power of SQL to Python developers while providing them the convenience of Pythonic code.
• By allowing data manipulation through Python classes, it abstracts the nuances of SQL.
• Its Schema-centric approach provides more control to the developers over the database.
• The SQL Expression Language gives developers the full power and flexibility of SQL.
• Transaction management ensures data integrity and helps handle failures efficiently.

24. LightGBM

LightGBM is a gradient-boosting framework that uses tree-based learning algorithms. It’s designed to be distributed and efficient, with the aim to be considerably faster than other tree-boosting methods.

Core Features of LightGBM-

• High Performance- LightGBM is renowned for its speed and efficiency. It provides a higher training speed and lower memory usage than many existing gradient-boosting trees.
• Handling Large-Sized Data- LightGBM can handle large-sized data and takes lower memory to run, which makes it a perfect tool for large-scale machine learning tasks.
• Support for Parallel and GPU Learning- LightGBM offers excellent support for both parallel and GPU learning which can significantly speed up learning processes.
• Exclusive Feature Bundling (EFB)- EFB is a unique feature in LightGBM that allows categorical features to be bundled together, further increasing the speed and performance of the model.
• Gradient-based One-Side Sampling (GOSS)- This feature retains the accuracy of the full gradient strategy, while significantly reducing the data loading time.

Benefits of LightGBM for Developers-

• Speed and Efficiency- Developers can build models faster without sacrificing model performance.
• Scalability- LightGBM handles large datasets with ease, making it suitable for big data applications.
• Versatility- It supports classification, regression, and ranking tasks, providing a tool for a variety of machine learning tasks.
• Ease of Use- Despite its advanced capabilities, LightGBM has a user-friendly interface that makes it accessible for beginners.
• Flexibility- Developers can easily customize loss functions and gain more control over the model fitting process.

Develop Python Apps with OnGraph

Discover the finest Python development tools of 2023! Whether you’re just starting or an expert Python programmer, these tools are designed to supercharge your web development skills and increase productivity.

Python enthusiasts swear by these tools and libraries to speed up coding, ensure clean code, enjoy auto-completion features, effortlessly download large datasets, and neatly organize them for various applications.

Python’s popularity and versatility come to life through its vast collection of tools, catering to a wide range of needs and functionalities. After exploring the best Python tools and their features, you can confidently select the ones that best suit your requirements. OnGraph is a leading Python development company that will help you build a next-gen app leveraging the top tools.

Got questions? Feel free to ask in the comments section below! We’re here to help and engage in exciting Python discussions with you!

Additionally, check out a similar blog about 10+ Python Libraries for Data Science and Machine Learning.