Weather Data Visualizer - Mini Project

Course: Programming for Problem Solving using Python
Assignment Title: Data Analysis and Visualization with Real-World Weather Data
Type: Individual Mini Project
Name: Ayush Kumar Roll No: 2501420003 Programme: BTech CSE (DS)

📋 Project Overview

This project implements a complete weather data analysis and visualization system that covers data acquisition, cleaning, statistical analysis, visualization, and reporting. It addresses the real-world problem of climate awareness and sustainability by providing actionable insights from weather data.

🎯 Learning Objectives

By completing this project, you will:

• ✓ Load and clean real-world CSV datasets with Pandas
• ✓ Compute statistics using NumPy and group-by operations
• ✓ Create informative plots using Matplotlib
• ✓ Apply storytelling techniques to present insights
• ✓ Automate analysis and export summaries in Python

📁 Project Structure

Project-4/
├── main.py                          # Main project script
├── weather_data.csv                 # Sample weather dataset
├── README.md                        # This file
└── output/                          # Output directory (created on first run)
    ├── 01_temperature_trends.png
    ├── 02_monthly_rainfall.png
    ├── 03_humidity_vs_temperature.png
    ├── weather_analysis_combined.png
    ├── cleaned_weather_data.csv
    ├── aggregated_data_M.csv
    └── WEATHER_ANALYSIS_REPORT.md

🔧 Installation & Setup

Prerequisites

• Python 3.7 or higher
• pip (Python package manager)

Required Libraries

Install the required packages using:

pip install pandas numpy matplotlib

Or install from requirements file:

pip install -r requirements.txt

🚀 Quick Start

1. Prepare Your Data

Option A: Use the provided sample data

• The weather_data.csv file is already included with the project

Option B: Use your own data

• Download weather data from:

  • Kaggle Datasets
  • IMD (India Meteorological Department)
  • Weather.com Historical Data
  • NOAA Climate Data

• CSV file must contain columns like: Date, Temperature, Rainfall, Humidity, Pressure

• Place the CSV file in the project directory

• Update the csv_file variable in main.py if using a different filename

2. Run the Analysis

python main.py

The script will:

1. Load and inspect the weather data
2. Clean and process the data
3. Compute statistical metrics
4. Generate visualizations
5. Perform grouping and aggregation
6. Export results and generate a report

📊 Project Tasks

Task 1: Data Acquisition and Loading

• ✓ Load CSV file into Pandas DataFrame
• ✓ Inspect data structure with head(), info(), describe()
• ✓ Display missing values summary

Task 2: Data Cleaning and Processing

• ✓ Handle missing values (mean imputation for numeric, drop for dates)
• ✓ Convert date columns to datetime format
• ✓ Filter and validate relevant columns

Task 3: Statistical Analysis with NumPy

• ✓ Compute daily, monthly, and yearly statistics
• ✓ Calculate mean, median, std dev, min, max, percentiles
• ✓ Generate comprehensive statistical summaries

Task 4: Visualization with Matplotlib

• ✓ Line Chart: Daily temperature trends
• ✓ Bar Chart: Monthly rainfall totals
• ✓ Scatter Plot: Humidity vs. Temperature correlation
• ✓ Combined Figure: Multiple plots in single view

Task 5: Grouping and Aggregation

• ✓ Group data by month/season using groupby()
• ✓ Use resample() for time-series aggregation
• ✓ Calculate aggregate statistics (mean, sum, min, max)

Task 6: Export and Reporting

• ✓ Export cleaned data to CSV
• ✓ Save all plots as PNG images
• ✓ Generate comprehensive Markdown report

📈 Output Files

After running the script, the following files will be created in the output/ directory:

Visualizations

• 01_temperature_trends.png - Daily temperature line chart
• 02_monthly_rainfall.png - Monthly rainfall bar chart
• 03_humidity_vs_temperature.png - Humidity-temperature scatter plot
• weather_analysis_combined.png - Combined multi-panel visualization

Data Files

• cleaned_weather_data.csv - Processed dataset ready for further analysis
• aggregated_data_M.csv - Monthly aggregated statistics

Report

• WEATHER_ANALYSIS_REPORT.md - Comprehensive analysis report with:
  • Executive summary
  • Dataset overview
  • Statistical findings
  • Key insights
  • Sustainability recommendations
  • Methodology
  • Conclusions

💻 Code Architecture

Main Class: WeatherDataVisualizer

class WeatherDataVisualizer:
    """Main class for weather data analysis and visualization"""
    
    def load_and_inspect_data()          # Task 1
    def clean_and_process_data()         # Task 2
    def compute_statistics()              # Task 3
    def create_visualizations()           # Task 4
    def group_and_aggregate()             # Task 5
    def export_cleaned_data()             # Task 6
    def generate_report()                 # Task 6

Key Methods

Method	Purpose	Task
load_and_inspect_data()	Load CSV and display structure	1
clean_and_process_data()	Handle missing values, convert dates	2
compute_statistics()	Calculate NumPy-based statistics	3
create_visualizations()	Generate Matplotlib plots	4
group_and_aggregate()	Time-series grouping and aggregation	5
export_cleaned_data()	Save processed data to CSV	6
generate_report()	Create Markdown analysis report	6

📊 Sample Data Structure

The provided weather_data.csv contains:

Date	Temperature	Rainfall	Humidity	Pressure
2024-01-01	15.2	0.0	62	1013.5
2024-01-02	16.1	0.5	65	1012.8
...	...	...	...	...

Columns:

• Date: Date in YYYY-MM-DD format
• Temperature: Daily mean temperature in °C
• Rainfall: Daily precipitation in mm
• Humidity: Relative humidity as percentage (0-100)
• Pressure: Atmospheric pressure in hPa

📈 Sample Output

Console Output

======================================================================
WEATHER DATA VISUALIZER - MINI PROJECT
======================================================================

======================================================================
TASK 1: DATA ACQUISITION AND LOADING
======================================================================

✓ Successfully loaded weather_data.csv

Dataset Shape: 365 rows, 5 columns

--- First 5 Rows ---
        Date  Temperature  Rainfall  Humidity  Pressure
0 2024-01-01         15.2       0.0        62     1013.5
...

--- Statistical Summary ---
       Temperature    Rainfall    Humidity    Pressure
count    365.000000  365.000000  365.000000  365.000000
mean      25.234658    2.104110   52.972603 1008.921918
...

[Continues with Tasks 2-6...]

✓ ALL TASKS COMPLETED SUCCESSFULLY!

Output files saved to './output/' directory:
  • weather_analysis_combined.png
  • 01_temperature_trends.png
  • 02_monthly_rainfall.png
  • 03_humidity_vs_temperature.png
  • cleaned_weather_data.csv
  • aggregated_data_M.csv
  • WEATHER_ANALYSIS_REPORT.md

🔍 Customization Guide

Change Column Names

Edit the method calls in main():

visualizer.clean_and_process_data(
    date_column='YourDateColumn',
    temp_column='YourTempColumn',
    rainfall_column='YourRainfallColumn',
    humidity_column='YourHumidityColumn'
)

Adjust Visualization Style

Modify the style in WeatherDataVisualizer.__init__():

plt.style.use('seaborn-v0_8-whitegrid')  # Other options: 'ggplot', 'bmh', 'dark_background'
plt.rcParams['figure.figsize'] = (16, 10)  # Change figure size

Change Aggregation Frequency

In group_and_aggregate():

visualizer.group_and_aggregate(group_by='D')  # 'D'=daily, 'M'=monthly, 'Y'=yearly

Modify Report Content

Edit the generate_report() method to add custom sections or analysis.

🎨 Visualization Examples

1. Temperature Trends

• Shows daily temperature variations
• Highlights seasonal patterns and anomalies
• Includes filled area for visual emphasis

2. Monthly Rainfall

• Bar chart of accumulated rainfall per month
• Identifies wet and dry seasons
• Useful for water management planning

3. Humidity-Temperature Correlation

• Scatter plot with trend line
• Shows inverse correlation in many climates
• Includes polynomial regression line

4. Combined Analysis

• 2×2 grid combining all major visualizations
• Comprehensive overview in single image
• Professional presentation format

📝 Sample Report Sections

The generated report includes:

1. Executive Summary - Overview of analysis
2. Dataset Overview - Data dimensions and date range
3. Statistical Analysis - Detailed metrics for each variable
4. Visualizations - Description of charts
5. Key Findings - Important insights
6. Data Quality Assessment - Cleaning actions performed
7. Sustainability Recommendations - Actionable insights
8. Methodology - Tools and techniques used
9. Conclusion - Summary and future directions

🐛 Troubleshooting

Issue: "ModuleNotFoundError: No module named 'pandas'"

Solution: Install required packages

pip install pandas numpy matplotlib

Issue: "File not found" error

Solution: Ensure CSV file is in the same directory as main.py or provide full path

Issue: Plots not showing

Solution: The script saves plots to files; check the output/ directory

Issue: Date conversion error

Solution: Ensure date column format is standard (YYYY-MM-DD, MM/DD/YYYY, etc.)

Issue: Missing column error

Solution: Update column names in main() to match your CSV file

📚 Libraries Used

• Pandas - Data manipulation and analysis
• NumPy - Numerical computations and statistics
• Matplotlib - Data visualization and plotting
• Pathlib - Cross-platform file path handling
• Datetime - Date/time handling

🌍 Real-World Applications

This project demonstrates skills applicable to:

• Environmental monitoring systems
• Climate research and reporting
• Energy management optimization
• Agricultural planning
• Urban sustainability initiatives
• Weather forecasting support
• Insurance risk assessment

📖 References & Resources

• Pandas Documentation
• NumPy User Guide
• Matplotlib Tutorial
• Kaggle Weather Datasets
• IMD Open Data

✅ Assessment Criteria

This project addresses the following evaluation criteria:

Criteria	Coverage
Data Loading	✓ CSV loading with Pandas
Data Cleaning	✓ Missing value handling, type conversion
Statistical Analysis	✓ NumPy-based computations
Visualization	✓ Multiple chart types
Aggregation	✓ Groupby and resample operations
Export/Reporting	✓ CSV export and Markdown report
Code Quality	✓ Well-documented, modular design
Functionality	✓ All tasks automated

📄 License & Attribution

This project is provided for educational purposes as part of the Programming for Problem Solving using Python course.

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Last Updated: December 2025 Version: 1.0
Status: Complete and Ready for Submission