Top 19 Python Tools Every Python Developer Should Know About

Have you ever seen a professional cricketer walk onto the field without a bat and helmet? It’s hard to imagine, right? Just as these vital tools help a cricketer perform at their peak, a Python developer needs the right set of tools to code with precision and efficiency. Without them, even the best ideas can fall short.

The use of Python in emerging technologies is rapidly increasing. For example, 75% of data scientists rely on Python. Its powerful tools—such as Pandas, NumPy, and Scikit-learn—play a crucial role in tasks like data cleaning and model building. 

In this blog, you’ll explore some of the top Python tools that are essential for tackling tasks in data science and machine learning. Let’s dive in!

Also Read: 10 Reasons Why Python is Popular With Developers

Best Python Tools for Developers

As the demand for Python grows, its ecosystem of tools continues to evolve. The most popular Python tools can increase productivity, facilitate collaboration, and streamline workflows.

Here’s a list of the best Python tools for developers.

1. Jupyter Notebook

Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, visualizations, equations, and explanatory text. 

Due to its ease of use for running Python code in a notebook-style interface, it is widely used in data science, education, and research.

Here are the key features of the Jupyter Notebook.

• Interactive coding environment

You can run Python code line by line, view results immediately, and visualize outputs.

• Extensibility

Supports plugins and extensions to increase functionality, such as integration with version control and cloud services.

• Rich text support 

You can combine code with formatted text, equations, and visualizations, which makes it suitable for teaching and presentations.

• Data visualization

It integrates seamlessly with popular libraries like Matplotlib, Seaborn, and Plotly to create visualizations directly in the notebook.

• Language support

It also supports languages like R and Julia via different kernels, making it compatible with multi-language workflows.

• Collaboration-friendly

Notebooks can be shared and collaborated with ease using platforms like JupyterHub or GitHub.

• Export options

You can convert notebooks to various formats like HTML, PDF, or Markdown for sharing purposes.

2. Pip

Pip is Python’s default package manager and one of the most essential tools for developers. It allows you to install, manage, and uninstall Python libraries or packages from the Python Package Index (PyPI). Pip makes it easy to choose and install the packages you need for your projects.

Here are the key features of the Pip tool.

• Simple package installation

You can install packages using a single command (pip install package_name) directly from PyPI.

• Version control

Using Pip, you can install specific versions of packages, providing control over the development environment and preventing compatibility problems.

• Package uninstallation

You can uninstall Python packages using pip uninstall package_name, making it easy to manage your project’s dependencies.

• Dependency management

Pip automatically installs dependencies needed for a specific package, ensuring everything is set up correctly.

• Upgrading packages

You can update your package with pip install --upgrade package_name, ensuring you're always working with the latest version.

Also Read: Built-in Functions in Python: Explained with Examples

3. VS Code

Visual Studio Code (VS Code) is a customizable, open-source code editor developed by Microsoft. Due to its rich ecosystem of extensions, debugging features, and active community support, it is preferred by Python developers. 

VS Code’s lightweight feature makes it an excellent choice for developers working on Python projects.

Here are the key features of the VS Code Python tool.

• Intelligent code completion

It offers smart autocompletion, function signatures, and quick information about code elements, helping improve developer efficiency.

• Integrated terminal

It provides an integrated terminal to run Python scripts, manage environments, and execute shell commands directly within the editor.

• Debugging support

The debugging tools allow you to set breakpoints, inspect variables, and explore your code, making it easier to address problems in the Python code.

• Customizable UI

The customizable user interface allows you to adjust themes, layouts, and key bindings to suit your workflow.

• Version control integration

You can manage version control directly within the editor. You can commit, push, pull, and view diffs without leaving VS Code.

• Live share

You can collaborate in real-time with others, share your code, and pair programs directly within the editor.

• Virtual environment integration

The tools support working with virtual environments seamlessly, helping you manage project dependencies and Python versions efficiently.

• Cross-platform compatibility

The tool is available on Linux, Windows, and macOS, making it suitable for developers across different operating systems.

Python is not just restricted to web development. Below are the Python tools available for web scraping.

Python Web Scraping Tools

With the rise in data-driven decision-making, the demand for efficient data extraction from websites has surged. Python’s vast libraries, such as Scrapy and Beautiful Soup, make scraping, processing, and storing data easier and more effective. 

Here are some popular Python tools for web scraping.

4. Requests

Requests is one of the most popular Python libraries for making HTTP requests. It can send HTTP/1.1 requests, handle redirects, and access APIs or download files from websites. 

Requests enables you to retrieve HTML or JSON content from a webpage that can be processed further.

Here are the key features of the Requests Python tool.

• Simple API

Requests straightforward API makes it easy for developers to send HTTP requests like GET, PUT, POST, and DELETE with minimal code.

• Automatic content decoding

Requests can automatically decode content to Unicode (UTF-8) or other formats, handling both text and binary data effortlessly.

• Session support

Due to its persistent request connections, it is more efficient for scraping multiple pages or handling login sessions.

• Multipart file uploads

It provides built-in support for uploading files via multipart encoding, making it suitable for interacting with forms and file-based APIs.

• Timeouts & retries

You can define timeout periods for requests and automatically retries requests on failure, helping you handle network instability.

• Cookie handling

Requests can automatically handle cookies, allowing you to maintain user sessions while scraping.

5. Beautiful Soup

Beautiful Soup can parse HTML and XML documents, making it widely used in web scraping to extract data from the HTML structure of web pages. 

Beautiful Soup’s unique way of parsing makes it easy for developers to find, search, and manipulate web page elements.

Here are the key features of the Beautiful Soup Python tool.

• Easy HTML/XML parsing

Beautiful Soup can automatically convert incoming HTML or XML into a well-structured tree of Python objects.

• Navigating HTML tags

You can navigate through HTML elements like <div>, <span>, <a>, etc., using simple Python expressions. 

• Support for multiple parsers

Suitable for parsers such as lxml and Python’s built-in html.parser, allowing flexibility in how the HTML is parsed.

• Simple syntax

The syntax is simple and intuitive, allowing even beginners to extract data quickly using familiar Python code structures.

• Filter and modify

You can easily filter elements based on attributes or content and modify them as needed.

• Error tolerance

It can handle broken or malformed HTML, which is common in real-world web scraping scenarios.

• Integration with other libraries

It can pair with other tools like Requests for fetching HTML and Pandas for data storage and manipulation.

6. Scrapy

Scrapy is a powerful and fast web scraping framework for large-scale data extraction. Scrapy can handle complex, asynchronous scraping jobs, which cannot be undertaken by smaller-scale libraries like Requests. 

Scrapy’s complete set of tools for managing scraping projects, from fetching pages to storing data, makes it ideal for robust web scraping applications.

Here are the key features of the Scrapy Python tool.

• Asynchronous scraping

Scrapy asynchronous networking library allows you to scrape multiple pages concurrently, thus increasing the speed of data extraction.

• Built-in data pipelines

The data pipelines in the tool allow for automatic data cleaning, processing, and storage after scraping.

• Automatic data extraction

Scrapy can extract structured data from websites using CSS selectors or XPath, which allows for precise data extraction.

• Crawler management

Scrapy’s framework can handle complex crawling logic, including URL filtering, request scheduling, and spider management.

• Project templates

Scrapy provides project templates that can quickly set up a new scraping project with a predefined folder structure and settings.

• Error handling

Scrapy’s built-in features can handle retries, timeouts, and logging, making it suitable for scraping large numbers of pages.

Also Read: How can a Data Scientist Easily Use ScRapy on Python Notebook?

7. LXML

LXML provides a fast and efficient way to process XML and HTML documents. Due to its speed and powerful support for XPath and XSLT (Extensible Stylesheet Language Transformations), it is one of the most widely used tools for web scraping in Python. 

LXML’s highly optimized nature allows it to handle large documents quickly, making it ideal for scraping large-scale websites or processing huge chunks of data.

Here are the key features of the LXML Python tool.

• High performance

LXML is fast, mainly due to its C-based implementation. It can parse and process large HTML and XML files faster than other libraries like Requests.

• XPath support

LXML provides excellent support for XPath, a powerful language for querying and navigating XML documents. 

• HTML Parsing

LXML can parse both well-formed XML and broken HTML documents. It uses an efficient parser to handle malformed documents.

• Tree traversal

LXML supports tree traversal methods that allow easy navigation and manipulation of parsed documents.

• Integration with other tools

LXML integrates easily with other libraries, such as Requests, allowing users to streamline their scraping workflows and automate data extraction.

• Memory efficiency

LXML’s memory efficiency makes it suitable for large-scale scraping operations that involve extracting huge amounts of data from complex websites.

A strong foundation in data structures is essential for developing efficient algorithms. Enrol in upGrad’s free course on data structures for a future career in programming.

Python Web Development Tools

Python’s wide range of tools cater to both beginners and advanced web developers. These tools simplify tasks such as building server-side logic, designing APIs, and integrating with databases, all while maintaining efficiency and scalability.

Here are the common Python tools used for web development.

8. Flask

Flask’s minimalist web framework allows developers to build web applications quickly and efficiently. It provides the essential tools needed to create a web app but gives developers options to choose their libraries or add more advanced features as needed. 

Flask is suitable for small to medium-sized applications and APIs, making it a good choice for developers who want flexibility.

Here are the key features of the Flask Python tool.

• Minimalistic framework

The minimalist framework allows developers to add only the components they need, which keeps the projects simple and efficient.

• Routing and URL management

Flask can map URLs to Python functions, providing a simple way to handle HTTP requests.

• Built-in development server

Flask’s built-in server for testing and development streamlines the development process.

• Templating with Jinja2

Integration with Jinja2 enables dynamic web pages by separating Python code from HTML.

• Extensible

You can add plugins for things like authentication, database integration, and form handling.

• Extensive documentation

Flask has the support of extensive documentation, making it easy to find resources, tutorials, and solutions for problems.

9. Streamlit

Streamlit is a popular Python library that helps developers create beautiful and interactive web applications for data science and machine learning projects. 

Streamlit allows you to build complex applications with minimal code, making it suitable for those looking to showcase dashboards, data insights, and machine learning models quickly.

Here are the key features of the Streamlit Python tool.

• Fast prototyping

It allows you to create web apps from Python scripts in minutes. It’s mainly useful for prototyping, data exploration, and rapid development.

• Interactive widgets

The built-in interactive widgets like sliders, buttons, and graphs make it easy to create dynamic and interactive web applications.

• No frontend coding is required

You can focus solely on Python code, as Streamlit handles the front-end coding automatically. 

• Seamless integration with data science libraries

It allows you to work with libraries like NumPy, Matplotlib, Pandas, Plotly, and TensorFlow, allowing for the smooth integration of data analysis and visualizations.

• Easy deployment

You can easily deploy the application to the cloud using platforms like Streamlit Sharing, AWS, or Heroku, making it easy to share your work with others.

10. FastAPI

FastAPI is a high-performance web framework for building APIs with Python. It can create REST APIs quickly and with minimal overhead. 

FastAPI is ideal for developers looking to create scalable, high-performance web services with automatic data validation and API documentation generation.

Here are the key features of the FastAPI Python tool.

• High performance

FastAPI is one of the fastest Python frameworks for building APIs, thanks to its use of Starlette for the web parts and Pydantic for data validation.

• Automatic interactive API documentation

FastAPI automatically generates interactive API documentation based on the code, saving developers time and effort.

• Security features

FastAPI supports built-in security features like OAuth2 authentication and JWT tokens, allowing developers to create secure APIs with minimal effort.

• Type hints

FastAPI uses Python’s type hints to make the code more readable and provide better editor support.

• Extensive documentation

The framework has detailed guides and examples to help both beginners and experienced developers get started with building APIs.

Also Read: Top 10 Python Frameworks for Web Development

Machine Learning Python Tools

Whether you're building a simple regression model or a complex deep learning network, Python tools provide efficient solutions for data preprocessing, model building, evaluation, and deployment.

Here are the important Python tools used in machine learning.

11. Scikit-learn

Scikit-learn offers a simple and efficient way to mine and analyze data. It is built on top of Python libraries like SciPy, NumPy, and matplotlib. 

Scikit-learn is suitable for beginners and experts alike due to its user-friendly API and vast range of algorithms.

Here are the key features of the Scikit-learn Python tool.

• Supervised learning algorithms

It contains tools for classification (SVM and random forests), regression (linear regression), and other supervised learning tasks.

• Unsupervised learning

It is suitable for clustering algorithms like K-means and hierarchical clustering, along with dimensionality reduction techniques such as Principal Component Analysis.

• Model Evaluation

Scikit-learn provides comprehensive functions for cross-validation, hyperparameter tuning, and performance evaluation through metrics like precision, accuracy, and recall.

• Preprocessing tools

It contains feature extraction, normalization, scaling, and encoding techniques to prepare data for machine learning models.

• Integration with other libraries

The tool integrates seamlessly with other scientific Python libraries such as Pandas, NumPy, and matplotlib, improving its utility for data analysis.

• Easy to use

Scikit-learn’s well-documented API allows for quick implementation and integration into existing codebases.

12. Keras

Keras is an open-source deep learning library that acts as an interface for the TensorFlow framework. It enables easy experimentation with deep neural networks. 

Keras is highly recommended for its simple and user-friendly API, making it an excellent option for building neural networks and experimenting with deep learning models.

Here are the key features of the Keras Python tool.

• High-level API

Keras’ easy-to-use interface is suitable for building and training deep learning models, taking out much of the complexity of TensorFlow.

• Support for neural networks

Keras supports building various neural network architectures such as feedforward, convolutional (CNN), and recurrent (RNN) networks.

• GPU Support

Keras can run on multiple Graphic Processing Units (GPU) for faster training and model evaluation, improving performance on large datasets.

• Compatibility

Keras works well with TensorFlow, CNTK backends, and Theano, providing flexibility for users who want to experiment with different frameworks.

• Pre-trained models

Keras provides access to a range of pre-trained models like VGG16, ResNet, and Inception for transfer learning.

13. PyTorch

PyTorch is an open-source library developed by Facebook’s AI Research lab. Due to its dynamic computational graph, it is particularly favored for deep learning applications. 

It is popular among academic researchers and industrial users due to its ease of use, flexibility, and powerful features.

Here are the key features of the PyTorch Python tool.

• Dynamic computational graph

PyTorch’s dynamic computation graph allows for easy and efficient debugging.

• Tensors

The core data structure is similar to NumPy arrays, allowing high-performance mathematical operations and easy integration with GPUs.

• Deep learning capabilities

It supports deep learning architectures such as CNNs, RNNs, and LSTMs, making it suitable for tasks like natural language processing (NLP), image processing, and reinforcement learning.

• Rich ecosystem

PyTorch’s robust ecosystem includes libraries like TorchVision for computer vision tasks and TorchText for NLP, further boosting its capabilities.

• GPU acceleration

PyTorch can move models and tensors to GPUs for faster computation and training, making it efficient for machine learning tasks.

Also Read: Keras Vs. PyTorch

14. OpenCV

OpenCV (Open Source Computer Vision Library) is popular for computer vision and image processing tasks. It supports image processing,  real-time computer vision, and machine learning.

You can use OpenCV for applications like facial recognition, object detection, augmented reality, and much more.

Here are the key features of the OpenCV Python tool.

• Real-time computer vision

OpenCV offers efficient algorithms for processing images and video feeds in real-time.

• Wide range of techniques

OpenCV can be used for basic and advanced image processing tasks, such as filtering, color space conversions, feature extraction, and edge detection.

• Computer vision algorithms

OpenCV supports popular models like Haar cascades for object detection and deep learning-based approaches.

• Integration with Deep Learning frameworks

OpenCV’s integration with deep learning libraries like TensorFlow allows seamless use of pre-trained models for image classification and object detection.

• Cross-platform support

You can develop applications on Linux, Windows, and macOS and deploy them on various devices.

• Extensive documentation

OpenCV is well-documented with tutorials and examples, and it has a strong developer community, making it easy to get started.

Are you looking to start a career in machine learning? Join upGrad’s free course on unsupervised learning to gain valuable insights.

Having examined various machine learning tools, you can explore the application of Python tools in data science.

Data Science Python Tools

Data science involves processing, analyzing, and obtaining insights from large datasets, and Python provides powerful tools to make these tasks easier and more efficient.

From mathematical computation to statistical analysis and automation, Python tools are crucial for building data science workflows.

Here are the important Python tools used in data science.

15. Theano

Theano is one of the oldest Python libraries for numerical computation and deep learning. It played a foundational role in the development of modern deep learning frameworks and has influenced libraries like Keras and TensorFlow. 

Theano allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently, making it useful for deep learning research.

Here are the key features of the Theano Python tool.

• Efficient computation

Theano can optimize mathematical expressions, allowing fast execution on CPUs and GPUs.

• Symbolic differentiation

Theano allows for symbolic differentiation, which is necessary for training machine learning models using backpropagation.

• GPU acceleration

Theano’s built-in support for GPU acceleration via CUDA allows faster computation for large datasets and complex neural networks.

• Customizable

You can define custom optimization routines and extend the framework to suit specific needs.

• Matrix computation

Theano’s optimization for matrix operations makes it suitable for a wide range of scientific and mathematical applications.

• Integration with other libraries

Theano integrates with libraries like NumPy, providing seamless usage for scientific computing.

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16. SciPy

SciPy is a scientific computing library that provides a wide array of functionalities for data science, such as optimization, integration, and interpolation. 

It is often used to solve complex mathematical problems and is a go-to tool for engineers, researchers, and data scientists.

Here are the key features of the SciPy Python tool.

• Optimization

SciPy provides algorithms for curve fitting, function minimization, and constrained optimization problems.

• Signal and image processing

The library can process signals and images, including Fourier transforms and filtering.

• Linear algebra

SciPy can solve linear algebra problems, such as eigenvalues and matrix decompositions.

• Statistical analysis

The tool offers statistical functions such as probability distributions and statistical tests.

• Integration and differentiation

SciPy supports numerical integration and differentiation, which is essential for solving differential equations.

• Sparse matrices

SciPy supports sparse matrices, allowing efficient storage and manipulation of large, sparse datasets.

17. Selenium

Selenium is widely used for web scraping, testing web applications, and automating repetitive web tasks. 

Selenium’s support for all major browsers and programming languages makes it a favorite among data scientists, web developers, and QA engineers.

The key features of the Selenium Python tool include the following.

• Cross-browser support

Selenium works with Chrome, Edge, Firefox, and Safari browsers, making it suitable for developers using different systems.

• Web scraping

The tool can extract data from websites by simulating real user interactions, such as clicking, scrolling, and form submission.

• Automated testing

Selenium can automate end-to-end tests of web applications to ensure their functionality and performance.

• Support for dynamic content

Selenium can interact with dynamic content loaded by JavaScript, making it ideal for scraping modern websites.

• Integration with testing frameworks

Selenium can integrate with testing frameworks like PyTest or unittest, allowing automated testing within larger test suites.

18. Robot Framework

Robot Framework– an open-source tool– is suitable for acceptance testing and robotic process automation (RPA). 

Its keyword-driven approach makes it accessible to both technical and non-technical users. The framework can integrate libraries and tools to handle different types of testing, including web testing, API testing, and more.

Here are the key features of the Robot Framework Python tool.

• Keyword-driven testing

Robot Framework’s keyword-driven syntax makes it easy to write and understand test cases, even for non-developers.

• Cross-platform

Robot Framework’s platform-independent feature allows it to run on Windows, macOS, and Linux.

• Integration with CI/CD tools

The framework can be integrated with continuous integration/continuous deployment (CI/CD) tools for automated testing in development pipelines.

• Reports and logs

The framework generates detailed reports and logs for test cases, making it suitable to analyze and debug failed tests.

• Test reusability

Test libraries and keywords can be reused across different test cases, improving test case maintainability and reducing redundancy.

Also Read: Robot Framework Interview Questions and Answers

19. TestComplete

TestComplete is a commercial testing tool that provides an interface for automating UI and functional, as well as regression testing of desktop, web, and mobile applications. 

TestComplete’s robustness and deep integration with other tools make it a popular choice for enterprise-grade software testing.

Here are the key features of the TestComplete Python tool.

• Cross-platform testing

TestComplete supports desktop, web, and mobile application testing across multiple operating systems.

• Keyword and scripted testing

It supports both keyword-driven testing and script-based testing using JavaScript, Python, VBScript, and other languages.

• Record and playback

TestComplete’s record-and-playback feature allows users to quickly record interactions with the application and generate test scripts automatically.

• Integration with CI/CD

It can be easily integrated with continuous integration/continuous deployment (CI/CD) tools like Jenkins, making it suitable for automated testing.

• Reporting and analytics

TestComplete generates detailed reports that can track test results, visualize trends, and identify issues more efficiently.

• Advanced object recognition

The tool uses advanced object recognition technology to identify and interact with UI elements, even if their properties alter dynamically.

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Having explored various Python tools, you can check potential career paths in Python programming.

How Can upGrad Help You Learn Python?

As Python continues to dominate fields like automation, data science, and software development, the need for skilled Python developers is growing rapidly. 

Learning Python on your own can be challenging, especially when trying to find structured content or understanding complex topics. 

UpGrad’s comprehensively designed courses can help you master Python and succeed in the tech industry.

Here are some courses offered by upGrad in Python Programming.

• Learn Basic Python Programming
• Programming with Python
• Learn Python Libraries
• Case Study Using Tableau, Python and SQL

Do you need help deciding which course to take to advance your career in Python Programming? Contact upGrad for personalized counseling and valuable insights.

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References:

https://datascience.utdallas.edu/fun-facts-about-data-science/#:~:text=75%25%20of%20data%20scientists%20use%20Python%20as%20the%20main%20coding%20language.