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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.
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
I've used Highcharts with both Angular Js Reactive applications (render as ReactJs) and also a bit of D3. Personally I found Highcharts to be the easiest to use but, with still quite a good level of customisability if you need it. graphs and charts then give D3 a try.
ML Model Training and Benchmarking
We choose python for ML and data analysis. Because:
- Simple syntax and easy to use
- ML Library and framework support
The python libraries and frameworks we choose for ML are:
TensorFlow
- High performance (GPU support/ highly parallel)
- Easy to debug
- visualization support
Numpy
- Easy matrix manipulation
- datatype with high compatibility
Pandas
- High efficiency when handling large data
- Dataset manipulation and customization
Matplotlib
- Simple and easy to use
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.
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.
Pros of Highcharts
- Low learning curve and powerful34
- Multiple chart types such as pie, bar, line and others17
- Responsive charts13
- Handles everything you throw at it9
- Extremely easy-to-parse documentation8
- Built-in export chart as-is to image file5
- Easy to customize color scheme and palettes5
- Export on server side, can be used in email1
Pros of Pandas
- Easy data frame management21
- Extensive file format compatibility2
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Cons of Highcharts
- Expensive9
Cons of Pandas
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What is Highcharts?
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