Ten practical guides
Python for Practical Data and Automation Work
These pages follow one practical workflow: collect operational data, clean and summarize it, visualize outcomes, and ship repeatable scripts with tests.
Python remains a widely used language for data processing, automation, analytics, and backend workflows in many job roles.
Basics and control flow
Learn Python variables, if/elif logic, and loop patterns by building an order status script that classifies records and flags missing data.
Functions and modules
Use Python functions and modules to split long scripts into reusable units, pass clear inputs, and create a safe main entry point for imports.
Data structures
Choose lists, dictionaries, sets, and tuples based on lookup and uniqueness needs, then model records so analysis code stays fast and readable.
File handling
Read and write text, CSV, and JSON files with context managers, encoding control, and schema checks that catch bad rows before reporting.
pandas cleaning
Clean pandas datasets by fixing dtypes, normalizing category labels, handling missing values, and deduplicating records with a defined business key.
pandas groupby
Build pandas groupby summaries for weekly KPIs by channel and region, using named aggregations and reconciliation checks to verify totals.
Data visualization
Create Matplotlib line and bar charts from pandas summaries, with sorted axes, clear labels, and target lines that explain KPI gaps at a glance.
API data fetching
Fetch API data with Requests using timeouts and status checks, validate JSON keys, and reshape payloads into stable tables for downstream joins.
Automation scripts
Design a daily Python automation script that validates inputs, runs repeatable transforms, and writes deterministic outputs that are safe to rerun.
Testing and debugging
Test Python data code with pytest using fixtures and regression cases, then debug failures with focused runs and assertions that expose root causes.