Your SPSS to Python Migration – Senior Engineers, Fixed Scope
Yes, migrating from IBM SPSS Statistics to Python (Pandas & Scikit-Learn) is a well-understood project. Our senior platform engineers translate your .sps scripts, macros, and custom procedures into clean
The lowest-cost way off IBM SPSS Statistics: our migration tooling automates the repetitive work —you pay senior engineers for judgment, not keystrokes.
How we support IBM SPSS Statistics → Python (Pandas & Scikit-Learn) after migration
- ✓Senior platform engineers run your fixed-scope migration.
- ✓Your IBM SPSS Statistics vendor support stays active as you migrate.
- ✓Third-party support covers most issues if your vendor renewal lapses.
- ✓Save 40-70% on legacy support while you transition.
- ✓24/7 US-based support for your migration and beyond.
- ✓No forced big-bang cutover – migrate at your pace.
Complex Syntax
Legacy SPSS macros and proprietary matrix syntax seem impossible to translate.
Data Structure Lock-In
Complex multi-table .sav data with integrated metadata labels makes migration daunting.
Vendor Renewal Pressure
Fear of losing vendor support mid-migration forces you into an expensive renewal.
IBM SPSS Statistics → Python (Pandas & Scikit-Learn) migration — your questions answered
What happens to my SPSS macros and proprietary matrix syntax?+
We convert your .sps scripts, macros, and custom procedures line by line into clean, production-grade Python code (Pandas, Scikit-Learn). Complex custom macro logic and proprietary matrix syntax are handled by our senior engineers.
Can I keep my IBM SPSS Statistics support during migration?+
Yes. Your vendor support remains active as long as your contract is current. If your renewal lapses before migration completes, our independent third-party support covers most operational issues on the legacy platform—no pressure to pay a full renewal just to buy time.
How do you handle complex multi-table .sav files with metadata?+
We preserve all variable labels, value labels, and metadata from .sav files in your Python DataFrames. Multi-table structures and integrated metadata are replicated using Pandas best practices.
What is the typical timeline and budget?+
Typical projects run 3–6 months with 3–5 senior consultants. The budget is $75,000–$450,000 depending on script volume and complexity. Every project is fixed-scope, so you know the cost upfront.
Beyond Migration: Managed Python Environment & Orchestration
Your migration doesn't end when the code is converted. We manage your new Python environment—containerization, automated pipelines with Airflow or Prefect, and ongoing maintenance. Let us keep your data science infrastructure running at peak performance.