Data Visualisation Library : Pygal

A Step by Step Tutorial To Create amazing Visualisations

The part of data we need to work with, analyze, and explore; and that quantity is only going to grow higher and higher as the technology around us improves. For an instance, imagine observing thousands of rows of data in a spreadsheet, trying to find hidden patterns, and trail down changes in the number that could be helpful in our post-analysis interpretation. Click here to learn Data Science Course

That is where visualisation comes into picture..

Visualization helps us to understand data better and explore more valuable patterns in it. The different types of charts and graphs convey data findings faster and more efficiently.

This Data Visualisation Library is useful for creating several types of dynamic and interactive graphs on web applications directly. This library can be used with well-known python web frameworks like Flask, Django. This library is suggested for small web applications that require simple and intuitive graphs.

I will talk about a Python library that can help us create noticeable, amazing, interactive visualizations. The library is Pygal.

Pygal is used to plot a wide range of simple charts and even complex graphs. It is a method where we can find trends in our data in a much simpler way by observing them. It is an open-source library that creates graphs with very few lines of code. It includes graphs like a bar, line, histogram, pie, XY, radar, box, pyramid, funnel, treemap, gauge, etc. These graphs can be embedded directly in the web pages either by using an embed tag or included directly in HTML. We can yield its output in different formats including SVG, Etree, PNG, etc. Data Science Course in Hyderabad

We need a few lines of code to create a pretty-looking interactive chart. Here, each chart is packaged as a method and there are many built-in styles and configurations to create effective graphs.

Installation:

pip install Pygal

Conda Install: Visualization helps us to understand data better and explore more valuable patterns in it. The different types of charts and graphs convey data findings faster and more efficiently.

This Data Visualisation Library is useful for creating several types of dynamic and interactive graphs on web applications directly. This library can be used with well-known python web frameworks like Flask, Django. This library is suggested for small web applications that require simple and intuitive graphs.

We need a few lines of code to create a pretty-looking interactive chart. Here, each chart is packaged as a method and there are many built-in styles and configurations to create effective graphs. Click here to learn Data Science Course in Chennai

To install using conda, open the terminal and run the following command:

conda install -c conda-forge pygal

Different Types of Graphs: -

Bar Graph: -

We will plot the Fibonacci of numbers.SVG is a vector-based graphics in the XML format that can restore the output in any format. Here we can save the file in SVG format.

Solid Gauge :

Gauges are the popular charts that help for dashboards, they anticipate a number in a range at a glance. It can use colored bands, hands, and combinations to display multiple values and their relation to a numeric scale.

Treemaps:

Treemaps are the best choice for visualizing categorical data. Data is represented in nested forms usually in rectangles shape. The size of the map represents the value of data i.e. higher the value greater is the size of the treemap. we can build a treemap with Seaborn and Squarify.

Gauge Tree:

This gives visualizations like a tachometer output. This is often used when we have to compare two outputs, for target metrics, project progress, or deadlines. A gauge chart is a union of a pie chart and a doughnut chart. It helps in visualizing data at a specific value.

Different Output Formats: -

1. Render Directly in Browser: -

2. SVG Format: -

SVG is an XML-based markup language used for 2 - dimensional vector-based graphics. SVG images are principally found on the web, and while they have comparable uses to JPEG, PNG, and WebP image types, their DNA is especially different. we can style an SVG using CSS. SVG-format vector images can be rendered at any size without loss of standard and can be easily confined by upgrading the text within them. Data Science Training in Bangalore

3. To get output in png format:

• pip install lxml # for handling Html and XML files
• pip install cairosvg # to export SVG files to pdf, png, etc.
• pip install tinycss #​​ error handling for CSS 2.1 & CSS3 modules

We need these dependencies for png format output (image version of graphs)

4.To get output in Flask APP - render_response ()

5.To get output in Django APP - render_django_response () – Django App

6.. render_tree() - To return the SVG root Etree node

Embedding in a Web Page: -

1. To get the Output using Embed Tag: -

We should mention the type as svg + xml and give the source as the svg file.

2. To get the Output directly in HTML: -

Mention the pygal render () result in the HTML code

Conclusion :

There were a lot of charts and shades…

The Pygal library provides so many more options, more graph types, and more options to embed the result graph’s SVG on different websites. Many like to work with Pygal a lot as it allows the user to unchain their artistry and create attractive graphics that are inter mutual, clear, and picturesque.