Weather Data Visualizer

An offline data visualization tool that works with pre-downloaded weather datasets (CSV format). This project processes temperature, humidity, and rainfall data over time, generating insightful visualizations like heatmaps, time-series graphs, and bar charts. Perfect for practicing data wrangling, visualization, and analytics skills.

Features

• Multiple Visualization Types

  • Time-series plots with rolling averages
  • Heatmaps showing seasonal patterns
  • Bar charts for regional comparisons
  • Distribution histograms
  • Multi-region comparison plots

• Flexible Data Filtering

  • Filter by date range
  • Filter by region(s)
  • Combine multiple filters

• Export Capabilities

  • Export charts as PNG (high resolution)
  • Export charts as PDF
  • Batch export all visualizations

• Data Analysis

  • Summary statistics
  • Region-specific analysis
  • Temporal trend analysis

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup

1. Clone the repository:

git clone <repository-url>
cd weather-data-visualizer

2. Install required dependencies:

pip install -r requirements.txt

3. Generate sample data (optional):

python generate_sample_data.py

Usage

Basic Command Structure

python main.py <input_csv> [options]

Quick Start Examples

1. Create a Time Series Plot

python main.py data/weather_data.csv -p timeseries

2. Generate All Visualizations

python main.py data/weather_data.csv -p all

3. Filter by Date Range

python main.py data/weather_data.csv -p timeseries --start-date 2023-06-01 --end-date 2023-08-31

4. Filter by Region

python main.py data/weather_data.csv -p comparison --regions North South

5. Export as PDF

python main.py data/weather_data.csv -p all -f pdf

6. Custom Output Directory

python main.py data/weather_data.csv -p heatmap -o my_charts/

7. Display Summary Statistics

python main.py data/weather_data.csv -p timeseries --summary

Command-Line Options

Option	Description	Example
input	Path to CSV file (required)	data/weather_data.csv
-p, --plot-type	Type of plot to generate	timeseries, heatmap, bar, comparison, distribution, all
--start-date	Start date for filtering	2023-01-01
--end-date	End date for filtering	2023-12-31
--regions	Filter by region(s)	North South East
-o, --output	Output directory	output/
-f, --format	Output format	png, pdf
--summary	Display summary statistics	Flag

Plot Types

1. timeseries: Time-series plots showing metrics over time with rolling averages
2. heatmap: Calendar heatmaps showing day-by-day variation
3. bar: Bar charts comparing average metrics by region
4. comparison: Line plots comparing regions side-by-side
5. distribution: Histograms showing value distributions
6. all: Generate all available plot types

CSV Data Format

Your CSV file should have the following columns:

Required Columns

• date: Date in YYYY-MM-DD format
• temperature: Temperature value (numeric)
• humidity: Humidity value (numeric)
• rainfall: Rainfall amount (numeric)

Optional Columns

• region: Region name (string) - Required for regional comparisons

Example CSV Structure

date,region,temperature,humidity,rainfall
2023-01-01,North,15.5,65.2,0.0
2023-01-01,South,25.3,72.1,5.2
2023-01-02,North,16.2,63.8,2.1
2023-01-02,South,24.8,70.5,0.0

Project Structure

weather-data-visualizer/
├── src/
│   ├── data_processor.py    # Data loading and filtering
│   ├── visualizer.py         # Visualization creation
│   └── cli.py                # Command-line interface
├── data/
│   ├── weather_data.csv      # Sample dataset with regions
│   └── simple_weather_data.csv  # Simple dataset
├── output/                   # Generated visualizations
├── examples/                 # Example usage scripts
├── main.py                   # Main entry point
├── generate_sample_data.py   # Sample data generator
├── requirements.txt          # Python dependencies
└── README.md                 # This file

Examples

Example 1: Analyzing Summer Weather Patterns

python main.py data/weather_data.csv \
  -p timeseries heatmap \
  --start-date 2023-06-01 \
  --end-date 2023-08-31 \
  --summary

Example 2: Comparing Regional Differences

python main.py data/weather_data.csv \
  -p comparison bar \
  --regions North South East West \
  -f pdf

Example 3: Full Analysis for a Specific Region

python main.py data/weather_data.csv \
  -p all \
  --regions Central \
  -o central_analysis/

Example 4: Year-over-Year Analysis

# First half of the year
python main.py data/weather_data.csv \
  -p timeseries \
  --start-date 2023-01-01 \
  --end-date 2023-06-30 \
  -o output/h1/

# Second half of the year
python main.py data/weather_data.csv \
  -p timeseries \
  --start-date 2023-07-01 \
  --end-date 2023-12-31 \
  -o output/h2/

Programmatic Usage

You can also use the modules directly in your Python scripts:

from src.data_processor import WeatherDataProcessor
from src.visualizer import WeatherVisualizer

# Load and process data
processor = WeatherDataProcessor('data/weather_data.csv')
filtered_data = processor.filter_data(
    start_date='2023-01-01',
    end_date='2023-12-31',
    regions=['North', 'South']
)

# Create visualizations
visualizer = WeatherVisualizer()
fig = visualizer.create_time_series(filtered_data)
visualizer.save_figure(fig, 'output/my_plot.png')

Sample Data

The project includes a sample data generator that creates realistic weather datasets:

python generate_sample_data.py

This generates:

• data/weather_data.csv: Full dataset with 5 regions and 365 days
• data/simple_weather_data.csv: Simple dataset without regions

Customization

Adding New Visualizations

To add new plot types, extend the WeatherVisualizer class in src/visualizer.py:

def create_custom_plot(self, data, **kwargs):
    fig, ax = plt.subplots(figsize=(12, 6))
    # Your plotting logic here
    return fig

Custom Data Processing

Extend the WeatherDataProcessor class in src/data_processor.py to add custom filtering or analysis methods.

Performance

• Handles datasets with 100,000+ records efficiently
• Optimized for plotting large time series
• Memory-efficient data processing with pandas

Use Cases

This project is ideal for:

• Portfolio Projects: Showcase data visualization and analytics skills
• Learning: Practice pandas, matplotlib, and data wrangling
• Weather Analysis: Analyze local weather patterns and trends
• Data Science Education: Teaching tool for visualization concepts
• Climate Research: Quick analysis of weather datasets

Tips

1. Large Datasets: For very large datasets, use date filtering to focus on specific periods
2. Multiple Regions: Use comparison plots to identify regional patterns
3. Export Quality: PDF format is recommended for presentations and reports
4. Batch Processing: Use the all plot type to generate all visualizations at once

Troubleshooting

Issue: "No module named 'pandas'"

Solution: Install dependencies with pip install -r requirements.txt

Issue: "Missing required columns"

Solution: Ensure your CSV has date, temperature, humidity, and rainfall columns

Issue: "No data matches the specified filters"

Solution: Check your date range and region names match the data

Issue: Plots not showing

Solution: Plots are saved to the output directory, not displayed interactively

License

This project is provided as-is for educational and portfolio purposes.

Contributing

Feel free to fork this project and adapt it to your needs. Suggestions for improvements:

• Add more visualization types
• Support for additional weather metrics
• Interactive web interface
• Real-time data streaming support

Author

Built with Python, pandas, and matplotlib for offline weather data analysis and visualization.