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How to make Animated pie charts in python

Effective information understanding and presentation may be achieved via the use of data visualization. Pie charts are a common choice for visually attractive data distribution representation. We’ll look at how to use Python to make animated pie charts in this blog article. For the purpose of creating dynamic and interactive visualizations, we will utilize the sjvisualizer package. This article will walk you through installing the necessary libraries and putting the necessary code into use to make animated pie charts in python.

Applications of Animated pie charts in python

Here are the top 4 applications of animated pie charts in python.

  • Presenting data distribution in a visually engaging manner
  • Showing the evolution of data over time
  • Comparing proportions between different categories
  • Adding interactivity to data visualizations

Implementations of Animated pie charts in python

Install the sjvisualizer library: Open your terminal or command prompt and execute the following command

But before that you have to download this whl file from here. And place in your folder where your Jupyter notebook file is present.

https://github.com/SjoerdTilmans/sjvisualizer/blob/main/dist/sjvisualizer-0.0.7-py2.py3-none-any.whl

You can download the dataset from google drive

https://drive.google.com/drive/folders/1BafDv-Dy-13Z0frzYwrYvFEhZqifGq8z?usp=sharing

!pip install "sjvisualizer-0.0.7-py2.py3-none-any.whl"

Import the required modules:

from sjvisualizer import Canvas
from sjvisualizer import DataHandler
from sjvisualizer import PieRace
import time
import json

Define the main function:

def main(fps=60, duration=0.35):
    number_of_frames = duration * 60 * fps
    df = DataHandler.DataHandler(excel_file="Countries that smoke the most.xlsx",
                                number_of_frames=number_of_frames).df
    canvas = Canvas.canvas()
    bar_chart = PieRace.pie_plot(canvas=canvas.canvas, df=df)
    canvas.add_sub_plot(bar_chart)

    # add static text
    canvas.add_title("Countries with the highest smoking rates", color=(0,132,255))
    canvas.add_sub_title("Nation with High Smoking Rates", color=(0,132,255))

    canvas.add_time(df=df, time_indicator="month")
    canvas.play(fps=fps)

Here is an explanation of the above codes

  • The code defines a function named main with two optional parameters: fps (frames per second) and duration.
  • The variable number_of_frames is calculated by multiplying the duration (in seconds) by 60 (seconds per minute) and the specified fps.
  • The code initializes a DataHandler object by passing the name of an Excel file (“Countries that smoke the most.xlsx”) and the number_of_frames as arguments. The resulting df object represents the data from the Excel file.
  • A Canvas object is created using the canvas function.
  • A pie chart race is created using the pie_plot function from the PieRace module, passing the canvas object and the df object as arguments. The resulting bar_chart object represents the pie chart race.
  • The bar_chart is added as a subplot to the canvas.
  • A title and sub-title s added to the canvas
  • A time indicator is added to the canvas using the add_time function, passing the df object and the time indicator as arguments. The time indicator can be set to “month” in this case.
  • The canvas is played with the specified fps, resulting in the animation of the pie chart race.

Execute the main function:

if __name__ == "__main__":
    main()

Conclusion

In this blog article, we learnt how to use the sjvisualizer module to generate animated pie charts in Python. The techniques listed make it simple to see how data is distributed and how it changes over time. Pie charts offer a simple method for comparing percentages and efficiently communicating information. Try out various datasets, then adapt the graphic components to your need. You can produce gorgeous animated pie charts for a variety of applications using Python’s power and data visualisation modules.

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