How Much Python Do You
REALLY Need as a Data
Engineer?
Not sure where to stop or start?
Here’s a no-fluff roadmap showing exactly what to
learn, what’s optional, and how to replace SQL
with Python using Pandas.
Bookmark this & share with every aspiring data
engineer.
Core Python Concepts You
MUST Know
These topics are your foundation for handling data in Python, just
like SQL:
Basics & Syntax
Variables, Data Types, Casting, Strings, Booleans
Control Flow
If...Else, For Loops, While Loops, Match (newer Python)
Data Structures
Lists, Tuples, Sets, Dictionaries, Arrays (via NumPy)
Functions & Error Handling
def, lambda, try/except, scope
Intermediate Topics That
Matter
Modules & Imports
Dates & Math
JSON Handling (for nested/structured data)
Regex – Like SQL’s LIKE for text search
String Formatting
VirtualEnv – Optional, but great for managing packages
File Handling: Your CSVs =
SQL Tables
Reading/Writing Files
open(), readlines(), write(), with
os.remove() for file deletion
CSV, JSON, Parquet
Use pandas.read_csv(), json.load(), pyarrow
Read in chunks, stream large files
SQL vs Pandas: Common
Translations
SQL Pandas
SELECT df[['col']]
WHERE df[df['col'] == val]
ORDER BY df.sort_values('col')
INSERT df.append() or pd.concat()
UPDATE df.loc[...] = ...
DELETE df.drop()
SQL Pandas
GROUP BY df.groupby().agg()
JOIN pd.merge()
IN, BETWEEN .isin([...]), between()
COUNT, SUM df['col'].count(), sum()
LIKE df['col'].str.contains()
Learning Pandas means writing SQL-style logic in code.
Python Libraries to Master
for Data Work
Must-Have Libraries for Data Engineers:
NumPy – Fast array operations, math functions
Pandas – SQL on steroids 🧪
pyarrow – For Parquet file handling
requests / httpx – For API ingestion
boto3, gcsfs, azure-storage – Cloud storage access
Bonus: Learn Jupyter/Colab for running data experiments.
Practice Platforms (Free &
Paid)
Where to learn Python specifically for data engineers:
Python Focus
W3Schools – Quick syntax
HackerRank – Code practice
PythonTutor – Visual code debugger
Real Python – Deep dives
SQL-like Pandas Practice
Kaggle Learn – Most practical
DataCamp – Hands-on courses
Analytics Vidhya – Community articles
Mode SQL + Python - Compare SQL vs Python
Project-Based Learning
Ideas
Apply What You Learn via Projects
✅ Load + Filter + Group CSVs
✅ Mimic SQL joins and aggregations in Pandas
✅ Analyze datasets: sales, movies, transactions
✅ Practice ETL logic:
→ Load → Clean → Transform → Save
Tools: Google Colab, Kaggle Datasets, Jupyter Notebook
Final Summary + CTA
Final Focus Areas:
Area Learn?
Python Basics ✅ Must
Control Flow ✅ Must
Data Structures ✅ Must
File Handling ✅ Must
Pandas / NumPy ✅ Must
OOP, Web, Django ❌ Optional
Comment “Starting Python Now” if you're just beginning
Share with your Python-learning group
Save this post for your learning roadmap
Follow [@Abhishek Agrawal] for more Data Engineering roadmaps
Education Ellipse | Practical Data Engineering, Simplified
Follow for more
content like this
Abhishek Agarwal
Data Engineer
