NumPy Broadcasting: Array Shape Rules
Combine arrays of different shapes with explicit rules and verify results before trusting the numbers.
Data analysis with pandas, NumPy, and visualization tools
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.
Use Polars expressions and lazy execution for clear DataFrame pipelines, while understanding when to compare, combine, or stay with Pandas.
Learn how to build chatbots with Python using OpenAI, Gemini, and LangChain APIs. Step-by-step tutorial with code examples for creating AI-powered virtual assistants.