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Data Visualization & API Analytics Engine

This repository contains a collection of Python-based projects focused on building data pipelines, integrating with RESTful APIs, and generating interactive data visualizations.

Project Structure

  • api_call_practice/: Scripts that interact with external APIs (like the GitHub REST API and Hacker News API) to programmatically fetch, parse, and analyze real-time JSON payloads.
  • earthquake_data_project/: A data mapping project that extracts and processes complex GeoJSON seismic activity records to visualize global earthquake trends.
  • telephone_operator_data_project/: A data analytics project that cleans and transforms raw telecom operator CSV datasets into readable formats for analysis.

Key Features & Technical Highlights

  • REST API Integration: Engineered scripts using the Python requests library to execute HTTP calls, handle rate limits, and dynamically parse nested dictionaries from live JSON data.
  • Data Processing & Transformation: Built efficient data pipelines to extract and format unstructured/raw data from various file types (.csv, .geojson, .json).
  • Interactive Data Visualization: Leveraged Matplotlib and Plotly to transform raw datasets into high-impact visual insights, focusing on clear data mapping and user experience improvements.
  • Clean Code Practices: Focused on writing efficient, maintainable, and well-documented Python scripts with proper debugging and error handling.

Technologies Used

  • Language: Python 3
  • Libraries: requests, matplotlib, plotly, json, csv
  • Data Formats: JSON, GeoJSON, CSV

How to Run

  1. Clone this repository to your local machine.
  2. Ensure you have Python installed, along with the required visualization libraries (pip install matplotlib plotly requests).
  3. Navigate to the specific project folder and run the Python scripts via your terminal (e.g., python python_repos.py).

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