Exploring Data Visualization in bqplot
Here is a short FAQ about this repository
1) What is this repository about?
In this repository, we are going to build different sets of visualizations using bqplot library in Python. The jupyter notebook (.ipynb) provides the code and this readme contains all the relevant information about this tutorial.
2) What is bqplot?
The bqplot library is a 2-D visualization solution for the Jupyter notebook, based on the Grammar of Graphics components. It has been created by fully utilizing d3.js and ipywidgets. The bqplot package aims to bring d3.js functionality to Python while keeping the ipywidgets widget facility. It achieves this by utilizing widgets, which are all of the available plot components. The library was designed with interactive widgets in mind, so we may update widget variables to follow plot changes.
3) Which plots have been used?
For this tutorial, we have built Scatter Plot, Pie Chart, Box Plot, Bar Chart, Histogram, Line Chart, CandleStick Chart, Correlation Heatmap and Choropleth Maps.
4) Which datasets has been used?
The 'milespergallon' or 'mpg' dataset, NIFTY-50 stock market data and world happiness dataset from Kaggle has been used for creating the plots. https://www.kaggle.com/datasets/uciml/autompg-dataset, https://www.kaggle.com/datasets/rohanrao/nifty50-stock-market-data, https://www.kaggle.com/datasets/ajaypalsinghlo/world-happiness-report-2021
Details of this tutorial and introduction to bqplot have been discussed in my article published on Analytics Vidhya
https://www.analyticsvidhya.com/blog/2022/04/interactive-data-visualization-using-bqplot/