Most #dataengineers over-engineer their pipelines. Here's a 5-line #Python trick that saved my team 3 hours every week: Why this works: → Parquet is 10x faster to query than CSV → dropna + dedup in one chain = no intermediate memory bloat → reset_index keeps your downstream joins clean Bookmark this. You'll use it Monday morning. What's your go-to data cleaning shortcut? Drop it below 👇 #DataEngineering #Python #DataPipelines #ETL #Programming
Optimize Data Pipelines with Python Trick
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Really excited to share my week1 python learning blog. Topic: variables and data Types In this article ,I have covered up by exploring the fundamentals of python including variables, examples and , data types as well with the common bugs with the solutions. please read here: https://lnkd.in/gryZb6-t #python #programing #Learningjourney#GitHub #Coding#Developers
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🐍 Day 13 of My 30-Day Python Learning Challenge Today I improved my understanding of File Handling using a better approach (with open). 📌 Problem: Read a file and count how many lines it contains. 📌 Code: with open("sample.txt", "r") as file: lines = file.readlines() print(len(lines)) 📌 Output: Total number of lines in the file 💡 Why use “with open”? • Automatically closes the file • Safer and cleaner • Avoids memory issues 📊 Quick Question What will be the output? with open("sample.txt", "w") as file: file.write("Hello") with open("sample.txt", "r") as file: print(file.read()) A) Hello B) Error C) Empty D) None Answer tomorrow 👇 #Python #FileHandling #CleanCode #LearningInPublic #SoftwareDeveloper
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🚀 Python Learning Journey – Day 26 Today, I learned about PDBC (Python Database Connectivity) and how Python interacts with databases. Here’s what I explored: ✅ What PDBC is and why it is used ✅ Connecting Python with a database ✅ Executing SQL queries using Python ✅ Performing operations like create, insert, update, delete ✅ Using cursor and connection objects This helped me understand how Python works with real-world data stored in databases. Step by step, moving towards building data-driven applications 💪 #Python #LearningJourney #Day26 #PDBC #Database #SQL #Coding #KeepLearning
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🐍 New on wcblog.in: Python Basics — Variables, Data Types, Loops & Functions Explained If you're starting out with Python (or need a solid refresher), I just published a practical, engineer-focused guide covering everything you need to write real Python code from day one: ✅ Variables & data types (int, str, list, dict, set...) ✅ String manipulation & f-strings ✅ Loops — for, while & list comprehensions ✅ Functions, *args, **kwargs ✅ Error handling with try/except ✅ A mini pipeline project to tie it all together Python is the backbone of data engineering, ML, and automation — and it all starts with these fundamentals. 👉 Read the full guide: https://lnkd.in/g92XrVSU #Python #DataEngineering #PythonBasics #LearnPython #Programming #DataEngineer #TechBlog
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Python String Methods: file names, user input, APIs, data cleaning, logs. If you work with Python, these 10 string methods aren’t optional — they’re daily tools. You’ll use them for: - cleaning extra spaces. - checking file extensions. - splitting and joining data. - finding and counting characters. These methods help you write cleaner, shorter, and more readable code. If you ever forget the syntax, this one image is enough to refresh your memory. Save it — future you will thank you. #Python #LearnPython #PythonTips #Programming #Coding #SoftwareEngineering #PythonDeveloper
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Merging spreadsheets, cleaning exports, and splitting reports are necessary-but-boring tasks. These Python scripts handle the repetitive parts so you can focus on the actual work. https://lnkd.in/eJtC6Wae
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Stateful UDFs just changed how Python scales. With @daft.cls, you can turn any Python class into a distributed operator that initialises once per worker and reuses state across every row. That means models, API clients, and database connections no longer get rebuilt on every call. The mental model stays simple: write normal Python classes, add a decorator, and Daft handles execution, scheduling, and parallelism. Find out more: https://lnkd.in/e79SePbN #PythonScaling #DaftCls #DistributedComputing #PythonClasses
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🐍 Day 22 of My 30-Day Python Learning Challenge Today I improved my Log File Analyzer by allowing user input (file name) instead of hardcoding. 📌 Code: filename = input("Enter file name: ") with open(filename, "r") as file: content = file.read().lower() print(content[:100]) # preview first 100 characters 📌 Why this matters? • Makes the program flexible • Works with any file • Closer to real-world usage 📊 Quick Question What happens if the user enters a wrong file name? A) Program crashes B) Empty output C) None D) Skips execution Answer tomorrow 👇 #Python #MiniProject #UserInput #LearningInPublic #SoftwareDeveloper
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🔁 Python Revision – Sets Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Sets (creation and properties) ✔️ Unique elements and unordered nature ✔️ Set methods (add, remove, discard, etc.) ✔️ Set operations (union, intersection, difference) Practiced using sets to handle unique data and perform efficient operations like finding common or different elements between datasets. Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Understanding sets is helping me work better with data and avoid duplicates 📊 Next: dictionaries and real-world data handling 🚀 #Python #Revision #Sets #Programming #DataAnalytics #LearningJourney #Coding
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📅 Day 75 – Higher Order Functions using filter() 🚀🐍 Today I practiced filter() function in Python to select required data from lists 💡 🔹 Filtered even numbers from a list ⚖️🔢 🔹 Filtered odd numbers from a list 🔀🔢 🔹 Selected numbers greater than 10 ⬆️🔢 🔹 Applied condition-based filtering on numeric data ✔️ 🔹 Filtered words that start with vowels (a, e, i, o, u) 🔤 🔹 Learned string-based filtering using conditions 📏 💡 Understood how filter() helps in extracting only required elements from a dataset ⚡ 💡 Improved logic building with lambda functions + conditions 🧠 🔥 Feeling more confident in functional programming and data filtering in Python! #Day75 #Python #FilterFunction #HigherOrderFunctions #CodingJourney #LearnPython #WomenInTech #FutureEngineer 🚀✨
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