setup

To check your Python environment for required data science libraries, run an environment audit that reports installed versus missing packages. This verifies library presence for pandas, scikit-learn, scipy, statsmodels, and numpy, and suggests install commands without modifying your setup.

Data science and machine learning environments require Python version 3.9 or higher. Verifying your Python version ensures compatibility with required libraries like pandas, scikit-learn, scipy, statsmodels, and numpy before you start coding or debugging.

You cannot automatically install missing Python libraries during an environment health check. The process preserves your existing setup by suggesting install commands for missing required libraries like pandas and numpy without executing any installations.

The best way to prevent import errors and version conflicts in a new data science project is performing a quick environment readiness check. Auditing installed libraries and the Python version confirms compatibility and stability before you begin coding.

To verify optional and required DS/ML library versions before starting a project, run an environment setup check. This identifies installed libraries, checks Python 3.9+ compatibility, and reports missing dependencies to ensure project readiness.

Your data science environment fails when importing scipy or statsmodels because these required libraries are missing or your Python version is below 3.9. An environment audit identifies missing packages and provides suggested install commands to resolve the issue.