Optimizing Memory for Large Data Files with Python

🚀 Day 17/20 — Python for Data Engineering Building a Simple Data Pipeline So far, we’ve learned: reading data transforming data working with APIs Now it’s time to connect everything together. 👉 That’s called a data pipeline 🔹 What is a Data Pipeline? A pipeline is a sequence of steps: 👉 Ingest → Process → Store 🔹 Simple Example import pandas as pd import requests # Step 1: Fetch data response = requests.get("https://lnkd.in/gTtgvXhZ") data = response.json() # Step 2: Convert to DataFrame df = pd.DataFrame(data) # Step 3: Transform df["salary"] = df["salary"] * 1.1 # Step 4: Store df.to_csv("output.csv", index=False) 🔹 Pipeline Flow 👉 API → Python → Transform → Output 🔹 Why This Matters Automates data flow Reduces manual work Scalable processing Foundation of data engineering 🔹 Real-World Use ETL pipelines Data ingestion systems Batch processing jobs 💡 Quick Summary A pipeline connects all steps into one flow. 💡 Something to remember Individual steps are code… Connected steps become a system. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 5/20 — Python for Data Engineering Error Handling (try / except) When working with real-world data, things don’t always go as expected. 👉 Files may be missing 👉 Data may be corrupted 👉 APIs may fail If your code crashes every time something goes wrong, that’s not data engineering. 🔹 What is Error Handling? Error handling allows your program to: 👉 handle unexpected situations 👉 continue running without crashing 🔹 Basic Syntax try: # code that might fail except: # code to handle error 🔹 Example try: df = pd.read_csv("data.csv") print(df.head()) except: print("File not found") 👉 If the file is missing, your program won’t crash 🔹 Handling Specific Errors (Better Practice) try: value = int("abc") except ValueError: print("Invalid number") 👉 More precise and professional 🔹 Why This Matters in Data Engineering Prevent pipeline failures Handle bad data gracefully Improve reliability Build production-ready systems 💡 Quick Summary Error handling makes your code: safer more stable production-ready 💡 Something to remember Good engineers don’t just write code that works… They write code that doesn’t break. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

One shift that helped me understand data engineering better: 👉 Stop thinking in scripts. 👉 Start thinking in pipelines. Instead of writing a single piece of code that does everything, I try to think in stages: Extract → Transform → Load For example: Extract Pull raw data from a source (CSV, API, database) Transform Clean and reshape it using Python, Pandas, or SQL Load Store it somewhere reliable (data warehouse or lake) This structure makes things easier to: ✔ debug ✔ scale ✔ maintain It also mirrors how real-world data platforms are built. Still practicing this mindset shift. #DataEngineering #ETL #Python #SQL

🚀 Day 8/20 — Python for Data Engineering Data Transformation Basics After reading data, the next step is not storing it… 👉 It’s transforming it into usable form Raw data is often: messy inconsistent not analysis-ready That’s where data transformation comes in. 🔹 What is Data Transformation? Changing data into a cleaner, structured, and useful format. 🔹 Common Transformations 📌 Selecting Columns df = df[["name", "salary"]] 👉 Keep only required data 📌 Filtering Rows df = df[df["salary"] > 50000] 👉 Focus on relevant records 📌 Creating New Columns df["bonus"] = df["salary"] * 0.1 👉 Add derived data 📌 Renaming Columns df.rename(columns={"salary": "income"}, inplace=True) 👉 Improve readability 🔹 Why This Matters Converts raw → usable data Prepares data for analysis Makes pipelines meaningful 🔹 Real-World Flow 👉 Raw Data → Clean → Transform → Store 💡 Quick Summary Transformation is where data becomes valuable. 💡 Something to remember Raw data is useless… Until you transform it into something meaningful. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 3/20 — Python for Data Engineering Functions: Writing Reusable & Clean Code As we start working with more data, writing the same logic again and again becomes messy. 👉 That’s where functions come in. 🔹 What is a Function? A function is a reusable block of code that performs a specific task. 🔹 Why Functions Matter in Data Engineering In real-world scenarios, we: clean data transform data apply repeated logic 👉 Instead of rewriting code, we reuse functions 🔹 Simple Example def clean_name(name): return name.strip().title() print(clean_name(" alice ")) 👉 Output: Alice 🔹 With Multiple Inputs def calculate_total(price, tax): return price + (price * tax) print(calculate_total(100, 0.1)) 👉 Output: 110.0 🔹 Where You’ll Use Functions Data cleaning Data transformation Reusable pipeline steps Automation scripts 💡 Quick Summary Functions help you: write cleaner code avoid repetition build reusable logic 💡 Something to remember Good code is not just working code. It’s reusable and maintainable. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 4/20 — Python for Data Engineering Reading & Writing Files (CSV / JSON) In data engineering, data rarely comes clean. 👉 It usually comes from: files logs exports APIs So the ability to read and write data is fundamental. 🔹 Why File Handling Matters We often: ingest raw data process it store cleaned output 👉 Python helps us do all of this easily. 🔹 Reading a CSV File import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 👉 Loads structured data into a DataFrame 🔹 Reading a JSON File import json with open("data.json") as f: data = json.load(f) print(data) 👉 Useful for API responses and semi-structured data 🔹 Writing Data to a File df.to_csv("output.csv", index=False) 👉 Save processed data for further use 🔹 Where You’ll Use This Data ingestion pipelines Data transformation workflows Exporting results Logging and backups 💡 Quick Summary Python allows you to: read data from multiple formats process it write it back efficiently 💡 Something to remember Data engineering starts with reading data… and ends with writing it in a better form. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🐍 ##learn with soumava | Series 02: Python Fundamentals for Data Engineers Transitioning from traditional ETL to AI-driven engineering requires more than just writing code—it requires choosing the right Data Structures for performance and integrity. i’ve realized that the "Basics" are actually the most powerful tools in our kit. Today, I’m sharing my personal notebook on the building blocks of Python. What’s inside this guide? ✅ Variables & Dynamic Typing: How Python infers types (and how to verify them). ✅ Lists: Why being "Mutable" and "Dynamic" makes them an ETL engineer's best friend. ✅ NumPy Arrays: The secret to high-speed mathematical operations over large datasets. ✅ Tuples: How to use "Immutability" to protect your database credentials and constants. Key Takeaway from the Guide: 🔹 Use a List for flexibility and changing data. 🔹 Use a Tuple for security and read-only data. 🔹 Use an Array for raw performance and math. Swipe through my Colab notes below to see the code snippets and real-world ETL use cases! 👇 #LearnWithSoumava #PythonProgramming #DataEngineering #NumPy #ETL #TechCommunity #DataAnalytics

🚀 Day 20/20 — Python for Data Engineering Writing Production-Ready Python You’ve learned: data handling transformations pipelines automation big data (PySpark) Now comes the real difference: 👉 Writing code that works vs 👉 Writing code that lasts 🔹 What is Production-Ready Code? Code that is: reliable readable scalable maintainable 🔹 Key Practices 📌 1. Clean & Readable Code # Bad x = df[df["salary"] > 50000] # Good high_salary_df = df[df["salary"] > 50000] 📌 2. Error Handling try: df = pd.read_csv("data.csv") except Exception as e: print("Error:", e) 📌 3. Logging import logging logging.info("Pipeline started") 📌 4. Modular Code def load_data(): return pd.read_csv("data.csv") 📌 5. Avoid Hardcoding file_path = "data.csv" df = pd.read_csv(file_path) 🔹 Why This Matters Easier debugging Better collaboration Scalable systems Production reliability 🔹 Real-World Flow 👉 Write Code → Test → Deploy → Monitor 💡 Quick Summary Production-ready code = clean + reliable + scalable 💡 Something to remember Code that works is good… Code that lasts is professional. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

🚀 Day 2/20 — Python for Data Engineering Understanding Data Types (Lists, Tuples, Sets, Dictionaries) After understanding why Python is important, the next step is knowing how Python stores and works with data. 🔹 Why Data Types Matter? In data engineering, we constantly deal with: structured data collections of records key-value mappings 👉 Choosing the right data type makes processing easier and efficient. 🔹 Common Data Types: 📌 Lists numbers = [3, 7, 1, 9] names = ["Alice", "Bob"] 👉 Ordered and changeable 👉 Useful for processing sequences 📌 Tuples point = (3, 4) values = ("Alice", 95) 👉 Ordered but immutable 👉 Useful for fixed data 📌 Sets unique_numbers = {3, 7, 1, 9} 👉 Unordered, no duplicates 👉 Useful for removing duplicates 📌 Dictionaries employee = {"name": "Alice", "salary": 50000} 👉 Key-value pairs 👉 Useful for lookup and mapping 🔹 Where You’ll Use Them Lists → processing rows of data Tuples → fixed records Sets → removing duplicates Dictionaries → mapping & transformations 💡 Quick Summary Different data types serve different purposes. Choosing the right one helps you write better and cleaner code. 💡 Something to remember Data types are not just syntax. They define how efficiently you handle data. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

Here are 5 Python libraries I use every week that I never learned about in grad school. Not pandas. Not scikit-learn. The ones nobody tells you about until you're debugging something at 11 PM. 1. pydantic — I used to validate data with if-else chains. Now I define data models that catch bad records before they hit my pipeline. One config change saved me hours of debugging clinical data feeds. 2. missingno — One visualization that shows every missing value pattern in your dataset. In healthcare data, the pattern of what's missing matters more than the percentage. This library makes it obvious. 3. pandera — Schema validation for dataframes. Define what your columns should look like and it yells at you before bad data propagates downstream. Essential when your data comes from multiple sources. 4. rich — Better logging and console output. Sounds trivial. But when you're running a pipeline on a remote server and need to quickly understand what went wrong, pretty output saves real time. 5. janitor (pyjanitor) — Clean column names, remove empty rows, handle Excel messiness. The boring data cleaning that eats 30% of every project. What's a library that changed how you work? The more niche, the better. #Python #DataScience #MachineLearning