ETL pipelines, data processing, web scraping, and analytics built by a freelance Python data analyst with 200+ projects and 5,000+ hours on Upwork.
When spreadsheets hit their limits, Python takes over. As a Python data analyst freelancer with over 200 completed projects on Upwork, I build production-grade scripts and pipelines that handle millions of rows, connect to any API, and automate processes that would be impossible in Excel alone.
My Python solutions span the full data lifecycle: extracting data from web pages and APIs, transforming and cleaning it with Pandas and NumPy, loading it into databases or dashboards, and even building machine learning models for prediction and classification. Every script I deliver is modular, well-documented, and ready for your team to maintain.
With 5,000+ hours logged and a 100% job success score, I have the track record to back up the technical expertise. Whether you need a one-time data migration or an ongoing automated pipeline, I deliver reliable Python solutions that scale with your data.
From quick scripts to enterprise-grade pipelines, every solution is built to handle real-world data at scale.
Automated Extract-Transform-Load pipelines that pull data from databases, APIs, and files, clean and reshape it, and deliver it to your target system on schedule.
Pandas-powered scripts that handle messy, inconsistent data — deduplication, standardization, outlier detection, and formatting — turning raw exports into analysis-ready datasets.
Robust web scrapers and API integrations built with BeautifulSoup, Selenium, and Requests. Collect structured data from any website or platform automatically.
Predictive models, classification systems, and data-driven forecasting built with scikit-learn and TensorFlow. From customer churn prediction to demand forecasting.
Python is the better choice when you are working with large datasets (100K+ rows), need to connect to APIs or databases, require web scraping, or want to build machine learning models. VBA is best for automations that live entirely inside Excel.
Python with Pandas can handle millions of rows efficiently on a standard machine. For truly massive datasets, I use chunked processing, Dask, or database-backed approaches that scale to billions of records without running out of memory.
Deployment depends on your needs. Scripts can run locally, on a cloud server (AWS, GCP), as scheduled tasks via cron or Task Scheduler, or as serverless functions. I handle setup and provide clear documentation for your team.
My core stack includes Pandas, NumPy, openpyxl, BeautifulSoup, Selenium, Requests, scikit-learn, Matplotlib, and Flask. I also work with SQLAlchemy for databases and various API-specific SDKs depending on the project.