Python is an open-source language that has gained tremendous popularity over the years. The language is used by developers and businesses worldwide to build web applications, artificial intelligence algorithms, data analytics and visualization tools, and much more. Python is widely used in data science and analysis because of its versatility and ease of use.
This article will explore the top 10 Python data visualization libraries to consider when hiring Python developers in 2023.
What is a Python Library?
A Python library is a collection of pre-written code modules that developers can use to perform a specific task. Libraries provide a set of functions and methods that can be called within the code to perform certain tasks. Python has a vast library of packages that can be used to perform various tasks, ranging from web development to data analysis.
Further, explore the top Python data visualization libraries to look out for in 2023.
Top Python Libraries in 2023
Let’s take a look at the top 10 Python libraries that will dominate the Python ecosystem.
TensorFlow is an open-source library for numerical computation. It is widely used in machine learning, data analysis, and other artificial intelligence applications. TensorFlow can be used for a wide range of tasks such as natural language processing, image recognition, and deep learning. It has a user-friendly interface that makes it easy to work with, and it is highly scalable.
- TensorFlow offers a user-friendly interface
- It has a strong ecosystem with a range of tools and libraries
- TensorFlow is highly scalable
NumPy is a powerful library for numerical computations. It provides high-level mathematical functions and tools that help in creating arrays and matrices. NumPy is used for scientific computing, data analysis, and machine learning. It is one of the most popular libraries in Python and is used extensively in scientific computing.
- NumPy provides high-level mathematical functions and tools
- It is widely used in scientific computing and data analysis
- NumPy is a powerful library for machine learning
Pandas is an open-source library for data manipulation and analysis. It provides a range of data structures for efficient data analysis. Pandas is used for data analysis, data visualization, and data cleaning. It is a powerful data analysis tool widely used in data science.
- Pandas is a powerful tool for data analysis and visualization
- It provides a range of data structures for efficient data analysis
- Pandas is widely used in data science and data cleaning
Matplotlib is a library for data visualization. It provides a range of tools for creating charts, graphs, and other visualizations. Matplotlib is used for scientific visualization, data analysis, and machine learning. It is a powerful tool for visualizing data and is widely used in data science.
- Matplotlib is a powerful tool for data visualization
- It provides a range of tools for creating charts, graphs, and other visualizations
- Matplotlib is widely used in scientific visualization and data analysis
Scikit-Learn is a library for machine learning. It provides a range of tools for creating machine learning models. Scikit-Learn is used for data analysis, data visualization, and machine learning. It is a powerful tool for machine learning and is widely used in data science.
- Scikit-Learn is a powerful tool for machine learning
- It provides a range of tools for creating machine learning models
- Scikit-Learn is widely used in data science and data analysis
Pygame is a library for creating games and multimedia applications. It provides a range of tools for creating games, animations, and other multimedia applications. Pygame is used for game development, animation, and other multimedia applications.
- Pygame is a powerful tool for creating games and multimedia applications
- It provides a range of tools for creating games, animations, and other multimedia applications
- Pygame is widely used in game development and animation
Keras is a Python library that is used for building and training deep learning models. It supports building various types of models such as convolutional neural networks and recurrent neural networks.
- Keras provides a high-level API for building deep learning models using various building blocks such as layers, activations, and optimizers.
- It provides tools for training and optimizing deep learning models using various optimization techniques.
- It runs smoothly on both CPU and GPU.
It is a Python computational framework machine learning library for computing multidimensional arrays. It works similarly to TensorFlow, but it not as efficient as TensorFlow due to its inability to fit into production environments.
- Ability to use NumPy arrays in Theano-compiled functions completely.
- Perform data-intensive computations much faster than on a CPU.
- Evaluate expressions faster than ever before, thereby increasing efficiency by a lot.
Pandas is a Python machine-learning package offering high-level data structures and analytical tools. The ability to translate complex operations with data using one or two commands is one of this library’s best features. With time-series functionality, Pandas includes various built-in methods for grouping, merging, and filtering data.
- It has eloquent syntax
- Built on top of NumPy
- Helps take care of a lot of the tedious and time-consuming tasks related to data
- Also used in academic fields
OpenCV is a Python library with many functions, making it a great tool for real-time computer vision programs. This library is highly efficient in processing various visual inputs, including images and video data. In addition to recognizing faces and handwriting, OpenCV can also identify objects.
- Performs tasks like objection tracking, face detection, landmark detection, and more
- Provides developers with access to more than 2,500 classic, state-of-the-art algorithms
- Used extensively even by tech giants like Google, IBM, Toyota, and more
- Also used in image/video analysis
Python libraries are available in hundreds of thousands, but the list above includes a few of the best. To keep up with Python’s booming popularity and growth, these libraries often get upgraded and enhanced.
Further, you can hire python developers who have knowledge in these popular python libraries.