What are the most common libraries and frameworks used in data analysis programming languages?

1 Python libraries

Python is one of the most popular and versatile programming languages for data analysis, boasting a rich ecosystem of libraries and frameworks that cover various aspects of data analysis. Pandas offers high-performance data structures and operations for manipulating tabular and time series data, while NumPy supports multidimensional arrays and matrices, as well as providing mathematical and statistical functions. SciPy provides scientific computing tools such as linear algebra, optimization, integration, and interpolation. Matplotlib enables plotting and visualization of data with various styles and formats, while Seaborn builds on Matplotlib to create attractive and informative statistical graphics. Scikit-learn offers a comprehensive collection of machine learning algorithms and tools like classification, regression, clustering, dimensionality reduction, and model evaluation. TensorFlow is a framework for building and training neural networks and deep learning models with support for distributed computing and hardware acceleration. PyTorch is another framework for building and training neural networks with a dynamic computational graph and flexible design.

2 R packages

R is a widely used programming language for data analysis, with a large and active community of developers and users who contribute to its vast repository of packages and resources. Popular packages for data analysis include Tidyverse, Data.table, Shiny, R Markdown, Caret, and RStudio. Tidyverse is a collection of packages that share a common philosophy and syntax for data manipulation, exploration, and visualization. Data.table offers a fast and concise way to work with large and complex data sets using SQL-like syntax. Shiny allows creating interactive web applications and dashboards from R code without HTML, CSS, or JavaScript knowledge. R Markdown enables creating dynamic documents, reports, and presentations that combine R code, text, and output in various formats such as HTML, PDF, and Word. Caret streamlines the process of building and evaluating predictive models with support for various machine learning techniques and metrics. Lastly, RStudio is an integrated development environment (IDE) that enhances the productivity and usability of R with features such as code editor, console, debugger, profiler, and package manager.

3 Julia packages

Julia is a relatively new programming language for data analysis, aiming to combine the expressiveness and simplicity of Python and R with the performance and speed of C and Fortran. It has an increasing number of packages and frameworks to extend its functionality and interoperability, such as DataFrames, Plots, StatsBase, MLJ, Flux, and IJulia. DataFrames offers data structures and operations for working with tabular data, similar to Pandas and data.table. Plots enables creating plots and visualizations of data with various backends and options. StatsBase provides basic statistical functions and types, such as descriptive statistics, sampling, and distributions. MLJ offers a unified interface for machine learning in Julia with support for various models, pipelines, tuning, and evaluation. Flux enables building and training neural networks and deep learning models with a minimalist design. IJulia integrates Julia with Jupyter, a web-based interactive computing platform that supports multiple languages and formats.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?