Data Story and Visualization - Air Quality Data in India

Abstract

Monitoring pollution is crucial for the well-being of our planet. This repository compiles pollutant measurements across 26 cities in India from 2015 to 2020. Explore daily readings of parameters like PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, Xylene, AQI, and AQI Bucket.

Dataset Source : Kaggle

Tech Stack:

• Python: Data preprocessing and cleaning.
• Pandas: Handling and manipulating the dataset.
• HTML, JS, CSS: Creating an interactive and visually appealing webpage.
• D3.js: Dynamic and interactive data visualizations.
• GitHub Pages: Hosting the visualization page for easy sharing and access.

Data Description

Date: Daily readings (2015-2020). Parameters: PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, Xylene, AQI, AQI Bucket.

Keywords

Air quality index, D3JS, Indian air quality data, Exploratory data analysis, Line Plot, Stacked Bar chart, Bar plot.

Data Cleaning

Cleaned using Python and Pandas. Handled duplicated data and missing values. Created two DataFrames (df and df1) from city_day.csv and city_hour.csv. Saved cleaned data to "City_data1.csv."

Steps to Run the Code

• Preprocess the Data: Use Python for data preprocessing.
• HTML Page Setup: Create an HTML page with titles and spacing for visualizations.
• Visualization Implementation: Implement visualization code using HTML, JS, CSS, and D3.
• Embed Visualizations: Embed D3 visualizations into specific HTML.
• Bind Visualizations: Use an iframe in "map.html" for a seamless viewing experience.