Adventures in Machine Learning

Mastering Python Visualization with Pygal: A Comprehensive Guide

Pygal: An Introduction to Python Visualization

Python is a high-level programming language that has gained immense popularity in the recent past across various domains. One of the reasons for its popularity is its flexibility and easy-to-understand syntax.

Python libraries serve as a backbone in creating visualizations for various purposes. One such library is Pygal, a Python module for creating beautiful graphs and charts.

Installation Process

The initial step is to install Pygal, which can be done using pip, a package installer for Python. To install, all you need is to run the following command in the command prompt:

pip install pygal

Overview of Pygal

Pygal is a flexible Python library that can be used to create various types of graphs and charts such as Line Chart, Bar Chart, Pie Chart, Radar Chart, etc. This module enables the user to visualize data in an interactive and interesting way.

The data can be in any format: CSV, Excel, or even MySQL databases. Some of the unique features of Pygal are:

  • It supports a variety of file formats, such as SVG, PDF, PNG, and JPEG.
  • Pygal charts are responsive, which means the charts automatically adjust themselves depending on the display size they occupy.
  • Pygal charts are customizable with options to adjust colors, size labels, and markers.

1) Creating Line Chart in Pygal

Creating an empty Line Chart

Import Pygal and create an empty Line Chart, as shown below:

import pygal
line_chart = pygal.Line()

Setting Title and X Labels/Values

Add a title and X labels with corresponding values for the Line Chart to be easily understood. Here’s an example:

line_chart.title = 'Programming languages popularity'
line_chart.x_labels = map(str, range(2000, 2022))

The above code assigns the title of the chart to “Programming languages popularity” and X Labels to the years between 2000 and 2022.

Adding Line Chart for Each Programming Language

Now, add a Line Chart for each programming language: Python, Kotlin, C++, and Java to visualize their popularity using Pygal. The line label indicates which programming language chart it represents, and the line plot values are the popularity of the corresponding language each year.

line_chart.add('Python', [22, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135])
line_chart.add('Kotlin', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 20, 25, 30, 35])
line_chart.add('C++', [65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 0])
line_chart.add('Java', [75, 65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1])

Saving and Rendering the Plot in SVG Format

Finally, save and render the plot in SVG format using the render_to_file method as shown below:

line_chart.render_to_file('Programming languages popularity.svg')

2) Creating Horizontal Line Chart in Pygal

Creating a Horizontal Line Chart using HorizontalLine method

To create a Horizontal Line Chart, we use the `HorizontalLine()` method in Pygal. Here is an example code snippet:

import pygal
horizontal_line_chart = pygal.HorizontalLine()

Setting Title and X Labels/Values

As with all Pygal charts, we need to give our Horizontal Line Chart a title, x labels, and corresponding x values so that it can be easily understood. We can accomplish this with the following code:

horizontal_line_chart.title = 'Programming languages popularity'
horizontal_line_chart.x_labels = map(str, range(2000, 2022))

The above code assigns the title of the chart to “Programming languages popularity” and sets the X Labels to the years between 2000 and 2022.

Adding Horizontal Line Chart for Each Programming Language

After setting the chart’s title and X Labels, we can add a chart for each programming language we want to display. We can do so by using the `add()` method and passing in the name of the programming language and its popularity values as arguments.

Here is an example:

horizontal_line_chart.add('Python', 135)
horizontal_line_chart.add('Java', 132)
horizontal_line_chart.add('C++', 50)
horizontal_line_chart.add('Kotlin', 35)

In the above code, we are adding a chart for Python, Java, C++, and Kotlin, with their respective popularity values.

Saving and Rendering the Plot in SVG Format

Finally, we want to save and render the plot in SVG format using the `render_to_file()` method as shown below:

horizontal_line_chart.render_to_file('Programming languages popularity.svg')

3) Creating Stacked Line Chart in Pygal

Creating a Stacked Line Chart using StackedLine method

To create a Stacked Line Chart, we use the `StackedLine()` method in Pygal. Here is an example code snippet:

import pygal
stacked_line_chart = pygal.StackedLine(fill=True)

Setting Title and X Labels/Values

As with all Pygal charts, we need to give our Stacked Line Chart a title, x labels, and corresponding x values so that it can be easily understood. We can accomplish this with the following code:

stacked_line_chart.title = 'Programming languages popularity'
stacked_line_chart.x_labels = map(str, range(2000, 2022))

Adding Stacked Line Chart for Each Programming Language

After setting the chart’s title and X Labels, we can add a chart for each programming language we want to display, and each chart is stacked on top of each other. This is done by using the `add()` method with a `stack_from` parameter so that the chart is properly stacked.

Here is an example:

stacked_line_chart.add('Python', [22, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135], stack_from=0)
stacked_line_chart.add('Java', [75, 65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1], stack_from=22)
stacked_line_chart.add('C++', [65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 0], stack_from=75)
stacked_line_chart.add('Kotlin', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 20, 25, 30, 35], stack_from=65)

In the above code, we are adding a chart for Python, Java, C++, and Kotlin, with their respective popularity values, and each chart is stacked on top of the prior chart using the `stack_from` parameter.

Saving and Rendering the Plot in SVG Format

Finally, we want to save and render the plot in SVG format using the `render_to_file()` method as shown below:

stacked_line_chart.render_to_file('Programming languages popularity.svg')

Conclusion

In this article, we explored Pygal, a Python module used for creating charts and graphs. We started with an overview of Pygal, discussing its installation process and features.

Pygal is an easy-to-use library that offers a wide range of chart types, including Line Charts, Bar Charts, Pie Charts, Radar Charts, and more. Pygal is also highly customizable with options to adjust colors, size labels, and markers.

Additionally, Pygal charts are responsive, which means they automatically adjust themselves depending on the display size they occupy. Next, we explored two specific chart types in greater depth: Horizontal Line Charts and Stacked Line Charts.

Horizontal Line Charts are useful when working with data that is better viewed horizontally instead of vertically. Stacked Line Charts, on the other hand, are great for viewing how multiple data sets come together to contribute towards a total sum.

Each chart type offers unique visualizations and insights into data, and Pygal makes it easy to create both. In conclusion, Pygal is a valuable tool for data visualization in Python.

It provides users with a range of chart types and customization options to create visually appealing and informative charts and graphs. With Pygal, anyone can explore complex data sets and present their findings in a clear and concise way.

We hope this tutorial has been helpful in guiding readers through the Pygal library and its various features. As with any learning process, practice is key, so we encourage readers to continue experimenting with Pygal and exploring all of its capabilities.

We appreciate the readers’ time and dedication in learning more about Pygal and hope the library will be a useful tool in their future data visualization endeavors. In this article, we covered Pygal, a Python module that enables users to create beautiful visualizations for their data.

We learned about Pygal’s installation process, overview of features, and explored Line Charts, Bar Charts, Pie Charts, Radar Charts, Horizontal Line Charts, and Stacked Line Charts in detail. Pygal proved to be a versatile and powerful tool in data visualization, providing a range of chart types and customization options.

Readers can now leverage Pygal’s features to create stunning visualizations for their data. With Pygal, the task of presenting complex data sets becomes easier and more accessible to all.

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