10 Best Python Libraries Every Developer Should Know

Python’s popularity as one of the best server-side programming languages is undeniable. Part of this success comes from its vast ecosystem of powerful libraries. A Python library acts as a pre-written building block, so developers can tackle complex tasks without reinventing the wheel.

Choosing the right library can make all the difference in the efficiency and elegance of your web application. But with so many options available, how do you pick the perfect library for your project?

Well, in this blog, you will learn about the top libraries used by professional Python development services. But first, let’s see what Python libraries are and what they do.

What is a Python Library?

A Python library is a collection of pre-written code bundled together to perform specific tasks. These libraries can be used in different programs, saving time and effort from repeatedly writing the same code. They are similar to a traditional library where you can find resources and reuse them for your purposes.

There are two main types of Python libraries: standard and third-party. While the standard libraries come pre-installed with Python, the third-party libraries can be easily installed for additional features.

Key Features of Python Libraries

Python libraries offer a range of features for developers by enhancing code reusability and flexibility. Here’s a breakdown of the key features:

  • Modularity: Libraries are built from smaller, reusable units called modules. These modules group related functionalities together, making the library organized and easier to navigate.
  • Reusability: The core feature of libraries is their reusability. You can import a library into your Python program and use its functions and classes within your code. This saves you time and effort from writing the same code repeatedly for common tasks.
  • Cross-platform compatibility: Many libraries work on various operating systems without requiring major code modifications. So you can develop code that can run seamlessly across various platforms.
  • Extensibility: Some libraries are designed to be extensible. That lets you create custom modules that enhance the core functionalities. And you can tailor the library to your specific needs.
  • Version control: Most libraries use version control systems like Git, so developers can collaborate and easily revert to previous versions if necessary. This ensures stability and facilitates troubleshooting.
  • Dependency management: Python’s package manager, pip, simplifies installing, updating, and managing libraries within your project. It helps ensure compatibility between different library versions and avoids conflicts.

These features make development faster and easier. So the Python experts use them to build high-quality, robust, and scalable websites. But which libraries are the best? Let’s take a look.

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Best Python Libraries for Web Developers

With Python, there are several built-in and third-party libraries available. Each comes with its own set of features and benefits. But which would be the best for your web app or website?

Well, the “best” depends on your intended use. But here’s a list of some of the top libraries used by the professional developers, along with their key features and ideal use:

1. Matplotlib


Matplotlib is a library for creating static, animated, and interactive visualizations in Python. It offers a variety of plots, such as bar charts and scatter plots, to explore and understand data. The customization options allow users to style plots with colors, labels, and annotations.

It integrates well with other libraries like NumPy and pandas. That makes it a key tool for data visualization and analysis in scientific and research applications.

Key Features

  • Variety of Plots: Create line plots, scatter plots, bar charts, histograms, pie charts, and more to represent diverse data.
  • Customization Options: You can control plot elements like colors, labels, markers, and legends for tailored visualizations.
  • Annotation Capabilities: Add text annotations, arrows, and other elements to enhance clarity and highlight specific data points.
  • Subplots and Layouts: Arrange multiple plots within a single figure for efficient comparison and presentation.
  • Interactive Visualization: Create interactive plots that allow users to zoom, pan, and explore data dynamically.

Ideal for: Data scientists and analysts who must create detailed and customizable visualizations for analysis and presentation.

2. NumPy


NumPy library is the backbone for numerical computing in Python. It provides support for large multi-dimensional arrays and matrices. That includes a vast collection of mathematical functions to operate on these arrays efficiently.

NumPy’s capabilities include broadcasting, vectorization, and integration with Fortran code. Due to its fast and reliable computation, it’s a core library for many other data science and machine learning libraries.

Key Features

  • Multi-dimensional Arrays: Efficiently handle and manipulate data structures like matrices and n-dimensional arrays.
  • Broadcasting: Perform element-wise operations on arrays of different shapes under certain conditions, simplifying calculations.
  • Linear Algebra Functions: Extensive support for matrix operations, vectorized calculations, and algebra algorithms.
  • Mathematical Operations: Rich set of mathematical functions for trigonometric functions, statistical functions, and more.
  • Random Number Generation: Generate random numbers from various statistical distributions for simulations and data exploration.

Ideal for: Scientists, engineers, and developers who require fast and efficient numerical computations and array manipulations.

3. TensorFlow


TensorFlow is an open-source library for machine learning developed by Google. It offers a flexible ecosystem of tools to build, train, and deploy machine learning models. The library supports various tasks, including neural network training, model optimization, and more.

Its scalability and extensive documentation make it suitable for research and production environments. It’s an essential tool for advancements in artificial intelligence and machine learning.

Key Features

  • Flexible Architecture: Build complex deep learning models with layers such as convolutional, recurrent, and dense.
  • Automatic Differentiation: Simplifies gradient calculation, a crucial step in training deep learning models by automatically computing gradients.
  • TensorBoard Integration: Visualize model training progress, monitor metrics, and analyze model behavior using TensorBoard.
  • Production Deployment: Deploy trained models for real-world applications using tools like TensorFlow Serving for inference.
  • Pre-trained Models: Leverage pre-trained models for tasks like image recognition or natural language processing as a starting point for fine-tuning.

Ideal for: Machine learning engineers, data scientists, and AI researchers developing and deploying machine learning models.

4. Pandas


Pandas is a data manipulation and analysis library for Python, built on top of NumPy. It introduces two primary data structures: Series (1-dimensional) and DataFrame (2-dimensional). These structures facilitate efficient data handling, cleaning, and transformation. 

Pandas offers functions for reading and writing data from various file formats, such as CSV, Excel, and SQL databases. Its robust indexing, merging, and reshaping capabilities make it essential for data science and machine learning projects.

Key Features

  • DataFrames and Series: DataFrames for labeled, tabular data and Series for one-dimensional labeled arrays.
  • Data Cleaning and Transformation: Offers functionalities for handling missing values, filtering, and transforming data using various methods.
  • Indexing and Selection: Efficiently select specific rows, columns, or subsets of data based on various criteria for focused analysis.
  • Merging and Joining: Combine data from multiple DataFrames based on shared keys for analysis.
  • Time Series Analysis: Provides functionalities for working with time series data, including date/time manipulation and time zone handling.

Ideal for: Data analysts, data scientists, and anyone working with structured data needing efficient data manipulation and analysis.

5. Scikit-Learn


Scikit-Learn is a library for machine learning tasks. It offers a wide range of classification, regression, clustering, and model selection algorithms. Plus, it provides simple and efficient data mining and analysis tools.

It’s known for its user-friendly API and ease of use. Built on NumPy, SciPy, and matplotlib, Scikit-Learn facilitates the development of predictive data models and machine learning applications.

Key Features

  • Supervised Learning Algorithms: Implements various algorithms for classification (e.g., Decision Tree) and regression (e.g., Random Forest).
  • Unsupervised Learning Algorithms: Offers algorithms for clustering and reducing data complexity.
  • Model Selection and Tuning: This includes tools for evaluating model performance using metrics like F-score and hyperparameter tuning for optimal results.
  • Feature Engineering: Provides feature scaling, normalization, and transformation tools to improve machine learning model performance.
  • Pipelines: Create and manage pipelines that chain data preprocessing steps and model fitting into a single workflow.

Ideal for: Beginners to advanced users in machine learning who need a toolkit for building and evaluating machine learning models.

6. Requests


Requests is a user-friendly HTTP library for Python designed to simplify web requests. It supports HTTP methods such as GET, POST, and PUT and handles cookies, headers, and authentication seamlessly. With its simple API, Requests enables developers to interact with web services and other APIs effortlessly.

It is known for its simplicity and readability, making it a preferred choice for RESTful API interactions and automated web requests.

Key Features

  • Simplified HTTP Requests: Send various HTTP requests with minimal code, streamlining communication with web services.
  • Automatic JSON Handling: Easily work with JSON data by encoding and decoding data seamlessly.
  • Session Management: Manage cookies and sessions to maintain state across multiple HTTP requests. This is essential for user authentication workflows.
  • Timeout and Retry Options: Set timeouts for requests to prevent hanging applications and implement retry mechanisms for handling network errors.
  • Connection Pooling: Improves performance by reusing existing connections for requests, reducing connection overhead.

Ideal for: Web developers and engineers who need to interact with web APIs and perform web scraping.

7. PyTorch


PyTorch is a dynamic computational graph-based deep learning framework. Developed by Meta’s AI Research lab, it supports GPU acceleration for high-performance computations.

It has a rich ecosystem, including libraries for computer vision and natural language processing. That makes it ideal for developing and prototyping deep learning models.

Key Features

  • Dynamic Computational Graph: Build and modify the computational graph during runtime. It allows for flexible experimentation with deep learning models.
  • Automatic Differentiation: Similar to TensorFlow, it facilitates gradient calculation for training deep learning models.
  • GPU Acceleration: Leverage GPUs for faster training and inference of deep learning models. This improves computational performance significantly.
  • Debugging and Profiling Tools: These include tools to debug model training issues and profile code for performance optimization.

Ideal for: Researchers, data scientists, and developers focusing on deep learning and AI research.

8. PySpark


PySpark is the Python API for Apache Spark, a distributed computing system for big data processing and analytics. It enables efficient large-scale data processing capabilities in Python. PySpark supports SQL, streaming data, machine learning, and graph processing.

PySpark integrates with the Hadoop ecosystem, making it suitable for handling vast datasets across clustered environments. Its scalability and speed make it essential for big data applications and real-time data analysis.

Key Features

  • Distributed Processing: Process massive datasets by leveraging the power of distributed clusters.
  • Fault Tolerance: Provides mechanisms for handling failures. That ensures data processing continues even if nodes in the cluster fail.
  • Spark SQL: Offers functionalities for working with structured data using SQL-like syntax.
  • Spark Streaming: Enables real-time processing of data streams for real-time analytics on streaming data.
  • Integration with Spark MLlib: It provides access to machine learning algorithms in PySpark’s MLlib library for distributed machine learning tasks.

Ideal for: Data engineers and analysts working with big data that require distributed data processing capabilities.

9. Seaborn


Seaborn is a statistical data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. With minimal code, Seaborn supports complex visualizations like violin plots, heatmaps, and time series plots. 

It integrates with pandas DataFrames, simplifying data manipulation and visualization. With built-in themes and color palettes, Seaborn enhances the aesthetic appeal of plots. That makes it a popular choice for exploratory data analysis and presentation.

Key Features

  • Statistical Relationships: Visualize relationships between variables using joint and pair plots. They help you to identify correlations or trends.
  • Statistical Visualizations: It provides an interface for creating statistical visualizations like box plots, violin plots, and heatmaps.
  • Aesthetics and Clarity: Focuses on creating visually appealing and informative visualizations for data exploration.
  • Color Palettes: This site offers a collection of color palettes designed for effective data visualization. It ensures a clear distinction between data categories.
  • Faceting: Create multiple plots within a single figure based on different data categories. This enables efficient comparison between different categories of data.

Ideal for: Data analysts and scientists who need to quickly create statistically informative and aesthetically pleasing visualizations.

10. SciPy


SciPy is a scientific and technical computing library built on top of NumPy. It provides optimization, integration, interpolation modules, and advanced mathematical functions. SciPy’s capabilities extend to signal processing, linear algebra, and statistical analysis.

It is widely used in industry for scientific research and engineering applications. SciPy enhances NumPy with additional functionality, making it a critical tool for performing comprehensive scientific computations and analyses.

Key Features

  • Optimization Algorithms: Offers a variety of optimization algorithms for solving complex optimization problems.
  • Integration and Differentiation: Provides functionalities for numerical integration and differentiation.
  • Linear Algebra Functions: Includes advanced linear algebra functions beyond NumPy’s core functionalities.
  • Special Functions: Provides access to various mathematical functions for various scientific and engineering applications.
  • Interpolation: Offers functionalities for interpolating data points to create smoother curves or estimate missing values.

Ideal for: Scientists and engineers needing advanced mathematical tools and scientific computations beyond basic operations.Our Python development experts widely use these libraries for their robustness, efficiency, and ease of use. You can choose between them based on your project needs, features, expertise, etc.

FAQs About Best Python Libraries

How do I install a Python library?
Python libraries can be installed using package managers like pip. For example, you can install a library by running pip install library_name in your command line or terminal. Some libraries might require additional setup or dependencies.
Are there any security considerations when using Python libraries?
Yes, security is important. Here are some tips:
  • Use the official Python Package Index (PyPI) or reputable repositories to avoid malware or vulnerabilities.
  • Stay updated with the latest versions to benefit from security patches and bug fixes.
  • Understand and assess the security implications of any external libraries the chosen library relies on.
  • If using libraries from unofficial sources, thoroughly review their code to identify potential security risks.
This practice must be followed to ensure security before using a Python library.
How do I choose the right libraries for my project?
The best libraries depend on your project's specific needs. Consider tasks you need to accomplish and research libraries that address those areas. Many libraries offer extensive documentation and tutorials to help you get started.


Python offers a range of libraries, both built-in and third-party, for the developers across a wide range of domains. They offer modularity, reusability, and rich features to create robust, efficient web apps with a more streamlined process and better productivity.

While libraries like NumPy and Pandas let you wrangle data on the server-side, Requests simplifies communication with external services. There’s a library out there for nearly every web development task imaginable. Choose the best one depending on your required features, preferences, and expertise.

If you need help with these libraries or develop custom ones for your project, hire our Python developers. We will ensure the best results.

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Jigar Shah is the Founder of WPWeb Infotech - a leading Web Development Company in India, USA. Being the founder of the company, he takes care of business development activities and handles the execution of the projects. He is Enthusiastic about producing quality content on challenging technical subjects.

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