SQLAlchemy Transactions: Session, Commit, and Rollback
Control the Session lifecycle so related database operations commit or roll back as one unit.
Data analysis with pandas, NumPy, and visualization tools
Control the Session lifecycle so related database operations commit or roll back as one unit.
Master the split-apply-combine pattern to summarize tabular data by category in pandas.
Combine arrays of different shapes with explicit rules and verify results before trusting the numbers.
Combine tables without silently multiplying rows and audit unmatched records.
Model bidirectional links with clear types and separate database foreign keys from ORM behavior.
Summarize numeric datasets with appropriate measures without hiding meaningful outliers.
Configure asynchronous engines and sessions with explicit transactions and no shared state between concurrent tasks.
Give command-line applications their own environments instead of mixing dependencies with projects or system Python.
Turn a DataFrame into an interactive Streamlit dashboard with filters, metrics, charts, and deliberate caching.
Validate DataFrame columns, types, and business rules with Pandera while building and testing a small sales pipeline.
Combine preprocessing and a model in a scikit-learn Pipeline, validate it correctly, and keep test information out of training.
Query CSV, Parquet, and in-memory DataFrames with SQL through DuckDB and Python, without operating a database server.