Underscore vs Pandas vs Plottable.js | What are the differences? | StackShare

Pandas vs Plottable.js vs Underscore

Overview

Underscore

Stacks1.8K

Followers589

Votes290

GitHub Stars27.4K

Forks5.5K

Pandas

Stacks1.8K

Followers1.3K

Votes23

Plottable.js

Stacks4

Followers33

Votes0

GitHub Stars3.0K

Forks227

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Advice on Underscore, Pandas, Plottable.js

Abigail

Dec 6, 2019

Decided

Fast Healthcare Interoperability Resources (FHIR) provides standard data objects in JSON format for the healthcare industry. Since JSON objects are hierarchical and tree-like, we had a need to defensively 'pluck' fields from our JSON objects and do lots of mapping. We tried jQuery and Underscore and a few other technologies like FHIRPath; but Lodash has been the most well supported, works in the most contexts, has the cleanest syntax, etc. We particularly like the ES6 version of Lodash, where we can import the method names directly, without resorting to * or _ syntax. We got hooked on the 'get' function to defensively pluck fields from objects without crashing our user interface, and have found countless uses for the other lodash functions throughout our apps. Lodash is great for developing and optimizing algorithms.

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Detailed Comparison

Underscore	Pandas	Plottable.js
A JavaScript library that provides a whole mess of useful functional programming helpers without extending any built-in objects.	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.	Built on top of D3, Plottable gives you a set of flexible, premade components that you can combine and rearrange to build charts.
-	Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.	-
Statistics
GitHub Stars 27.4K	GitHub Stars -	GitHub Stars 3.0K
GitHub Forks 5.5K	GitHub Forks -	GitHub Forks 227
Stacks 1.8K	Stacks 1.8K	Stacks 4
Followers 589	Followers 1.3K	Followers 33
Votes 290	Votes 23	Votes 0
Pros & Cons
Pros 85 Utility 55 Simple 40 Functional programming 32 Fast 28 Open source	Pros 21 Easy data frame management 2 Extensive file format compatibility	No community feedback yet
Integrations
No integrations available	Python	D3.js

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