24. How to Load SQL Query Results into a pandas DataFrame
Chala mitrano, CSV ne shiklo — pan real Blinkit / Amazon Now tables often MySQL, Postgres ki SQLite madhe rahtat. Aaj aapan SQL query cha result seedha pandas DataFrame madhe aanuya. Model nahi. Read-only habit, read_sql, parameters. Ghabru naka; copy-paste examples aahet.
What you will learn in this chapter
- Why analysts pull SQL into pandas (not only CSV)
- A read-only connection mindset (select, do not update live ops tables)
pd.read_sql/read_sql_queryin plain English- Beginner patterns: SQLite, MySQL, PostgreSQL (+ SQLAlchemy URL)
- Filter in SQL vs filter in pandas — when to do which
- Common mistakes: loading huge tables; string-format SQL (use parameters)
- A small Blinkit-style lab you can copy in Jupyter
Fictional data
Blinkit / Amazon Now figures in this chapter are fictional sample data for learning only. They do not show real company numbers. People names are for practice; e-mails use example.com. Database host names and passwords below are classroom placeholders — never paste real production secrets into a notebook you share.
Concepts in this chapter
- 24.1Why Pull SQL into pandas
- 24.2Connection Basics: Read-Only Mindset
- 24.3pd.read_sql and read_sql_query
- 24.4SQLAlchemy and DB-API: Beginner Patterns
- 24.5Filter in SQL vs Filter in pandas
- 24.6Common Mistakes: Huge Tables and String-Format SQL
- 24.7Practice, Tip and Close
The chapter recap is at the end of the last concept page.