Python Data Persistence: With SQL and NoSQL Databases

Python Data Persistence: From Simple Files to Modern Databases 🐍

Every useful program eventually needs to save something — a user preference, a transaction record, a dataset, a configuration file. Python Data Persistence: With SQL and NoSQL Databases is a hands-on guide to the many ways Python can read, write, and manage data that outlives a single run of a program.

Malhar Lathkar starts from the ground up, assuming no prior programming experience. The early chapters offer a quick Python tutorial, then move into functions, modules, object-oriented programming, and file I/O. From there, the book expands into serialization, relational databases, ORMs, spreadsheets, and NoSQL stores. It is a connected tour of Python’s data-handling ecosystem rather than a scattered reference.

What the Book Covers 💾

• Python foundations: data types, control flow, functions, modules, packages, and OOP concepts such as inheritance, properties, and magic methods.
• File I/O: opening, reading, writing, binary files, simultaneous read/write, and file/directory management with the os module.
• Object serialization: pickle, shelve, dbm, CSV, JSON, XML, and plistlib.
• Relational databases: RDBMS concepts, SQLite, SQL statements, transactions, and Python’s DB-API with sqlite3, pymysql, and pyodbc.
• ORMs: SQLAlchemy ORM and Core, including sessions, queries, relationships, and updates.
• Excel integration: openpyxl and pandas for creating workbooks, formulas, charts, and reading ranges into DataFrames.
• NoSQL databases: MongoDB with PyMongo and Apache Cassandra with its Python driver, covering documents, collections, keyspaces, and CQL.

From SQL Tables to Document Stores 🗄️

The middle chapters build a strong bridge between Python and SQL. You see how flat files fall short, how relational tables solve those problems, and how Python speaks to databases through DB-API modules. The book then introduces SQLAlchemy, showing how object-relational mapping can align database tables with Python classes. This progression helps you understand not just the syntax but the design choices behind each approach.

The final chapters turn to NoSQL. MongoDB and Cassandra represent two different data models — document store and column store — and the book explains their native tools alongside Python APIs. If you have wondered when a relational database is the right fit and when a NoSQL store makes more sense, these chapters provide practical context.

Working with Excel and Pandas 📊

Spreadsheets remain one of the most common ways to store and exchange data. The book devotes a chapter to openpyxl and pandas, covering workbook creation, reading cell ranges, formulas, charts, images, and moving data between worksheets and DataFrames. For analysts and data scientists, this section connects Python’s data-persistence skills to everyday office workflows.

Who This Book Is For

The preface states that no prior programming knowledge is assumed, making the book accessible to beginners. At the same time, the breadth of database topics — from SQLite to Cassandra — gives intermediate Python users a structured way to fill gaps in their data-handling knowledge. Data science practitioners who need to understand where their data lives and how to retrieve it will also find relevant material, especially the sections on relational databases, SQLAlchemy, and pandas.

A Structured, Example-Driven Approach

Each chapter is organized around clear concepts and practical code. The book uses Python 3.7.2 examples and notes that Python is cross-platform, so the techniques apply across Windows, Linux, and macOS. Appendices collect useful reference material, including built-in functions, modules, magic methods, SQLite dot commands, ANSI SQL statements, PyMongo API methods, and Cassandra CQL shell commands. A companion code repository is referenced in the book for readers who want to run and adapt the samples.

If you are ready to move beyond temporary variables and in-memory lists, Python Data Persistence offers a clear, wide-ranging path to storing data in files, SQL databases, spreadsheets, and NoSQL systems. It is a solid addition to any Python learner’s shelf.