Python is a high-level programming language that is interpreted, interactive, portable, and object-oriented. This free and open-source general-purpose language is compatible with a wide variety of Unix variants, including Linux and macOS, and Windows.
Python is used in hacking, computer vision, data visualization, 3D machine learning, and robotics and is a popular programming language among developers worldwide.
The following table summarizes the ten most frequently used Python libraries for data science:
Plotly is collaborative analytics and graphing platform built on the web. It is one of the most potent libraries available for machine learning, data science, and AI operations. Plotly is a publication-ready and immersive data visualization tool.
Plotly makes it simple to import data into charts, allowing developers to create slide decks and dashboards quickly. It is used to create tools such as Dash and Chart Studio.
Keras is a free and open-source software library. Keras provides an interface to the TensorFlow library, allowing for rapid prototyping of deep neural networks. It was created by Francois Chollet and debuted in 2015.
Keras includes utilities for model compilation, graph visualization, and data analysis. Additionally, it provides prelabeled datasets that can be directly imported and loaded. It is user-friendly, adaptable, and well-suited for exploratory research.
NumPy, or Numerical Python, was created in 2015 by Travis Oliphant. It is a foundational library for mathematical and scientific computations.
The open-source software includes linear algebra, Fourier transform, and matrix computation functions and is primarily used for applications that require high performance and resource efficiency. NumPy aims to provide 50x faster array objects than traditional Python lists.
NumPy is the foundation for data science libraries such as SciPy, Matplotlib, Pandas, Scikit-Learn, and Statsmodels.
Seaborn is the most frequently used statistical data visualization library in Python, used to create heatmaps and other visualizations that summarize data and depict distributions. It is built on top of Matplotlib and supports both data frames and arrays.
Seaborn is used to creating simple plots such as bar graphs, line graphs, and pie charts.
Statsmodels is a Python scientific library that focuses on data science, data analysis, and statistics. It is based on NumPy and SciPy and integrates data handling with Pandas. Statsmodels enables users to perform data exploration, statistical model estimation, and statistical testing.
TensorFlow is an open-source library for deep learning applications developed by the Google Brain Team.
Initially designed for numerical compilations, it now includes a robust and flexible ecosystem of tools, libraries, and community resources that enables developers to build and deploy machine learning-based applications.
TensorFlow was initially released in 2015, but the Google Brain team recently released its latest version, TensorFlow 2.5.0, which includes additional features. It is Python 3.9 compatible.
SciPy, or Scientific Python, is a programming language used to solve complex mathematics, science, and engineering problems. It is based on the NumPy extension and enables data manipulation and visualization.
SciPy is a Python package that contains user-friendly and efficient numerical routines for linear algebra, statistics, integration, and optimization. Its applications include the processing of multidimensional images, the solution of Fourier transforms, and the solution of differential equations.
Matplotlib, created by John Hunter, is one of the most widely used libraries in the Python community. It can generate static, animated, and interactive data visualizations. Matplotlib enables a plethora of customizations and charts.
It enables developers to scatter, customize, and configure plots using histograms. The open-source library incorporates plots into applications via an object-oriented API.
Pandas were created by Wes McKinney and are used to manipulate and analyze data. It provides fast, flexible, and expressive data structures and features such as data alignment and handling of missing data.
Pandas provide fast, flexible, and expressive data structures for working with labeled and relational data. It is built on the foundation of two primary data structures: Series and Frames.
SciKit-Learn incorporates algorithms for classification, regression, and clusterings, such as DBSCAN, gradient boosting, support vector machines, and random forests.
David Cournapeau developed the library on top of SciPy, NumPy, and Matplotlib to handle everyday machine learning and data mining tasks.
SciKit-Learn is a powerful tool for analyzing predictive data.