What is next?

You have reached the end of the book – but hopefully not the end of your journey. This chapter collects suggestions for what to explore next, organized by the parts of the book.

Python

  • Regular expressions, a powerful mini-language for finding and extracting patterns in text.
  • Calling web APIs with the Requests library and working with JSON data.
  • Web scraping, e.g., with Beautiful Soup.
  • Running Python outside notebooks: writing scripts, using virtual environments, and installing packages with pip or uv.
  • Type hints, which make your code easier to read and help editors and tools catch mistakes early.
  • Testing your code with pytest.
  • Object-oriented programming: defining your own classes.
  • Practice, practice, practice: register at Codewars, an excellent site to practice programming (not just in Python).

Pandas

  • More time series functionality: rolling-window calculations (.rolling), shifting and lagging (.shift), and time zones.
  • Working with datasets larger than memory: choosing appropriate dtypes, reading files in chunks, and Dask, a Python library for parallel computing, including Dask DataFrames with the same API as Pandas.
  • Polars, a modern and very fast alternative to Pandas.
  • Interactive plotting with Plotly.
  • scikit-learn, the standard library for your first steps into machine learning.

Databases and SQL

  • Indexes and query performance: how databases find rows quickly, and how to inspect query plans with EXPLAIN.
  • Transactions: grouping several statements so that they succeed or fail as a unit.
  • Database design: entity-relationship modeling and normalization.
  • Beyond SQLite: install and try a client-server database system such as PostgreSQL or MySQL/MariaDB.
  • SQLAlchemy, the most popular Python toolkit for working with databases.
  • DuckDB, an analytical database that can run SQL directly on CSV and Parquet files and even on Pandas DataFrames – a perfect bridge between the Pandas and SQL worlds.
  • A taste of NoSQL: document databases (MongoDB) and key-value stores (Redis).
  • Practice: SQL kata on Codewars (using PostgreSQL) or the exercises at PostgreSQL Exercises.