Python API Challenge

This project demonstrates API access, data cleaning, analysis, and visualization using Python. The challenge was divided into two parts: analyzing weather data across global cities and visualizing vacation data based on weather preferences.

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Project Overview

Part 1: WeatherPy

Collected and analyzed weather data from 500+ cities using OpenWeatherMap API.

Analysis Included:

• Temperature (Max)
• Humidity
• Cloudiness
• Wind Speed
• Geolocation (Latitude vs Weather Metrics)

Deliverables:

• API call automation to retrieve weather data
• Cleaned and transformed data into structured DataFrames
• Visualized relationships between latitude and weather conditions
• Exported data and visualizations for reporting

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Part 2: VacationPy

Leveraged weather data to identify ideal vacation destinations based on:

• Preferred temperature ranges
• Low humidity
• Clear weather conditions

Deliverables:

• Used Google Places API to locate hotels near ideal cities
• Visualized results on heatmaps and marker layers using Jupyter Notebooks and Matplotlib Basemap
• Created dynamic visualizations for client-ready deliverables

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Tools & Technologies Used

• Python 3.x
• Jupyter Notebooks
• Pandas
• Matplotlib
• OpenWeatherMap API
• Google Places API
• Requests Library
• CSV File Handling
• API Key Management
• Data Visualization

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File Structure

WeatherPy/
├── WeatherPy.ipynb - API access, weather data analysis, visualizations
├── VacationPy.ipynb - Hotel search and vacation mapping
├── api_keys.py - API key storage (excluded from GitHub)
├── output_data/
│   ├── cities.csv - Cleaned weather data
│   ├── Fig1.png - Latitude vs Max Temp
│   ├── Fig2.png - Latitude vs Humidity
│   ├── Fig3.png - Latitude vs Cloudiness
│   └── Fig4.png - Latitude vs Wind Speed

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Skills Demonstrated

• Accessing and using public APIs
• Automating data collection
• Data cleaning and transformation with Pandas
• Visualizing geographic data trends
• Using Python to generate client-friendly reports
• Handling API keys securely
• Mapping data visually for business storytelling

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Key Takeaways

• Developed a repeatable process for data extraction from APIs.
• Learned how to clean and organize real-world messy API data.
• Used Python visualizations to support business-relevant recommendations.
• Strengthened skills in automating data analysis pipelines.