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Npm Data Science Libraries

Most Popular Npm Data Science Libraries

NameSizeLicenseAgeLast Published
immutable142.59 kBMIT10 Years28 Aug 2023
get-stream6.74 kBMIT8 Years17 Aug 2023
formidable38.07 kBMIT13 Years25 Aug 2023
mongoose614.29 kBMIT13 Years15 Sep 2023
pretty-bytes3.54 kBMIT9 Years4 Feb 2023
chart.js1.16 MBMIT9 Years24 Aug 2023
yocto-queue2.85 kBMIT3 Years12 Aug 2021
istanbul-lib-coverage7.88 kBBSD-3-Clause8 Years18 Oct 2021
configstore3.33 kBBSD-2-Clause11 Years5 Apr 2021
data-uri-to-buffer3.42 kBMIT10 Years5 May 2023
env-paths3.18 kBMIT7 Years27 Aug 2021
papaparse44.81 kBMIT9 Years23 Mar 2023
mdn-data87.52 kBCC0-1.06 Years29 Aug 2023
internal-slot6.53 kBMIT4 Years9 Feb 2023
functional-red-black-tree9.86 kBMIT10 Years29 Sep 2014

When are Data Science Software Dependencies Useful?

In the realm of JavaScript and NPM package management, data science dependencies offer a wealth of functionality and can be extremely useful in the following scenarios:

  • Data Processing: When you need to transform, filter, aggregate, and otherwise process large datasets, these packages can dramatically simplify tasks that would otherwise be cumbersome to write from scratch.

  • Statistical Analysis: These packages often contain pre-built functions for statistical operations such as regression, correlation, and variance.

  • Machine Learning: There are numerous packages available designed to help with the development and training of machine-learning models.

  • Data Visualization: Many packages also provide functionality for data visualization, simplifying the process of generating graphs, charts, heat maps, etc.

Functionalities of Data Science Packages

Here are some common functionalities found in many data science libraries:

  • Data Wrangling: They provide functions to clean and transform raw data into an actionable format. This includes handling missing data, filtering rows, grouping, and reshaping the data.

  • Mathematical and Statistical Functions: Many packages offer a wide variety of mathematical and statistical functions for data analysis. This can include averaging, standard deviation, max/min values, etc.

  • Data Visualization: They often provide built-in scripts to produce graphs and charts. These can range from simple pie and line charts to complex 3D visualizations.

  • Machine Learning: Many packages offer pre-built functions for training machine-learning models, encompassing a wide variety of algorithms from linear regression to neural networks.

Gotchas/Pitfalls to Look Out For

Despite their significant power and flexibility, data science packages for JavaScript come with a set of potential pitfalls which are important to keep in mind to ensure effective use:

  • Dependency security: One of the major concerns with third-party packages, whether for data science or general purposes, is security. All dependencies should be reviewed for potential security vulnerabilities.

  • Package Updates: It's vital to keep dependencies up-to-date. However, updates can sometimes break existing functionality, so apply updates with caution.

  • Heavy Computation: Data science packages can sometimes perform heavy computations which may slow down your JavaScript application - especially if it's running on a client's web browser.

  • Data Privacy: If the packages require external APIs for some operations, data privacy could become an issue.

  • Licence Compliance: Be cautious about the licenses of the packages you use. Some could restrict commercial usage or require publishing your source code.


Understanding the functionalities and potential downfalls of NPM data science packages can aid developers in effectively choosing and using the correct ones for their projects. While they can add robust functionality and help to simplify complex data processing and analysis tasks, one must also take into consideration the associated risks and costs. Understanding the boundaries and effective usage of these dependencies is a step in the right direction for smooth and effective project execution.