SQL and Python Together (P05)¶

This document explains how SQL and Python are combined in professional data analytics workflows.

Both languages are essential. They serve different but complementary roles.

Why We Use Both SQL and Python¶

SQL is best for:

• filtering
• joining
• grouping
• aggregating
• shaping relational data

Python is best for:

• orchestration
• automation
• file handling
• configuration
• analysis and visualization

Professional analytics workflows use both.

Separation of Responsibilities¶

A common best practice:

• SQL handles data logic
• Python handles program logic

This separation improves:

• readability
• reuse
• testing
• collaboration

SQL in .sql Files¶

In professional projects:

• SQL is often stored in .sql files
• queries are versioned and reviewed
• logic is reused across scripts

Benefits:

• easier to read than embedded strings
• easier to test
• easier to maintain

Parameterized Queries¶

When SQL needs inputs (dates, limits, IDs):

• use parameterized queries
• do not insert values directly into SQL text

Why:

• safer
• reusable
• clearer intent

Vocabulary to know:

• parameterized query
• placeholder
• bound parameter

Python as the Orchestrator¶

Python typically:

• reads SQL files
• opens database connections
• binds parameters
• executes queries
• receives results as tables or dataframes
• saves outputs

This pattern scales from:

• example projects
• to analytics teams
• to production pipelines

DataFrames and SQL¶

Modern analytics workflows often:

• run SQL
• return results into a pandas DataFrame
• continue analysis in Python

This allows analysts to:

• use the best tool at each step
• stay flexible
• work efficiently

Typical Roles¶