Highcharts vs Pandas

Overview

Highcharts

Stacks1.3K

Followers1.1K

Votes92

Pandas

Stacks1.8K

Followers1.3K

Votes23

Highcharts vs Pandas: What are the differences?

What is Highcharts? A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application. Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

What is Pandas? High-performance, easy-to-use data structures and data analysis tools for the Python programming language. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Highcharts and Pandas are primarily classified as "Charting Libraries" and "Data Science" tools respectively.

Some of the features offered by Highcharts are:

It works in all modern mobile and desktop browsers including the iPhone/iPad and Internet Explorer from version 6
Free for non-commercial
One of the key features of Highcharts is that under any of the licenses, free or not, you are allowed to download the source code and make your own edits

On the other hand, Pandas provides the following key features:

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

"Low learning curve and powerful" is the primary reason why developers consider Highcharts over the competitors, whereas "Easy data frame management" was stated as the key factor in picking Pandas.

Highcharts and Pandas are both open source tools. It seems that Pandas with 20.2K GitHub stars and 8K forks on GitHub has more adoption than Highcharts with 8.79K GitHub stars and 2.32K GitHub forks.

Webedia, WebbyLab, and Zumba are some of the popular companies that use Highcharts, whereas Pandas is used by Instacart, Twilio SendGrid, and Sighten. Highcharts has a broader approval, being mentioned in 212 company stacks & 40 developers stacks; compared to Pandas, which is listed in 73 company stacks and 49 developer stacks.

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Advice on Highcharts, Pandas

Shaik

Feb 18, 2020

Needs advice

I have used highcharts and it is pretty awesome for my previous project. now as I am about to start my new project I want to use other charting libraries such as recharts, chart js, Nivo, d3 js.... my upcoming project might use react js as front end and laravel as a backend technology. the project would be of hotel management type. please suggest me the best charts to use

246k views246k

Comments

Vinay

Oct 10, 2020

Decided

We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.

5.82k views5.82k

Comments

cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

72.8k views72.8k

Comments

Detailed Comparison

Highcharts	Pandas
Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
It works in all modern mobile and desktop browsers including the iPhone/iPad and Internet Explorer from version 6;Free for non-commercial;One of the key features of Highcharts is that under any of the licenses, free or not, you are allowed to download the source code and make your own edits;Pure Javascript - Highcharts is solely based on native browser technologies and doesn't require client side plugins like Flash or Java.	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
Stacks 1.3K	Stacks 1.8K
Followers 1.1K	Followers 1.3K
Votes 92	Votes 23
Pros & Cons
Pros 34 Low learning curve and powerful 17 Multiple chart types such as pie, bar, line and others 13 Responsive charts 9 Handles everything you throw at it 8 Extremely easy-to-parse documentation Cons 9 Expensive	Pros 21 Easy data frame management 2 Extensive file format compatibility
Integrations
No integrations available	Python

What are some alternatives to Highcharts, Pandas?

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.