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
.sqlfiles - 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