Darya Petrashka’s Post

I was reviewing one of my old Python projects today and realized something “My past self didn’t do a great job commenting on the logic.” It actually took me a few minutes to understand what I was trying to do back then. So, I updated the comments, and wow, what a difference it made. Here’s a small reminder for all developers (including myself): 🧠 Whenever you’re writing any logic, always add a descriptive comment before starting it. You can even use simple markers like: # ----- XYZ logic started ----- # ----- logic end ----- Trust me, your future self (and your teammates) will thank you later! 🙌 A curious Data Engineer enjoys solving problems with Python and writing clean, logical, and well-documented code. ♻️ Repost if you think this could help others. #DataEngineering #Python #CodeComments

🚀 What Why and When Python Curious why Python is leading the tech world in 2025? In this video, I explain what makes Python powerful, how it compares with C, Java, and R, and where it’s used in real world projects including AI, automation, data science, and research. 🐍 Discover how Python simplifies coding, manages memory automatically, and integrates with tools like Power BI, Tableau, and Excel. 🎥 Watch the full video here 👉 https://lnkd.in/gGM2H8Xe #Python #DataScience #AI #MachineLearning #Coding #Programming #PythonInResearch #PythonVsR #TechLearning #AriseAbility

Ever wondered how the same data task looks in SQL, Python, and Excel? I came across this handy cheatsheet that lines up common operations side by side, from loading data to filtering, grouping, and joining tables. It’s a great quick reference whether you’re: an Excel user trying to pick up Python, a Python user brushing up on SQL, or just someone who likes to see how the same thing works across tools. I’ve found that learning to “translate” between these three helps a lot when switching between projects or working in mixed teams. What’s your go-to environment for data work: SQL, Python, or Excel? #DataAnalysis #Python #SQL #Excel #Learning #DataEngineering #Knowledge

One of my favorite things about working with data is finding ways to make repetitive tasks simpler and more reliable. Recently, I built a Python script that automatically downloads and consolidates compliance data from publicly available sources, such as the FDA and other regulatory websites. The script then cleans and formats the information, saving it into a structured file that can be used for tracking and analysis. What used to take several manual steps can now be done in seconds, saving time and reducing the chance of human error. For me, it was a great opportunity to combine Python automation, data cleaning, and workflow optimization, skills I’m continuously developing in my data engineering journey. 🐍 Have you automated any manual task at work recently? What was the result? #Python #Automation #DataEngineering #DataCleaning #LearningInPublic #ContinuousImprovement

As a data analyst, your time is better spent on insights, not repetitive tasks. These five Python scripts help you work faster, cleaner, and smarter. https://lnkd.in/gudM_nZe

🧑🎓 Experiment 3: Basics of DataFrame using Pandas 🐼 This experiment focuses on understanding the structure, creation, and manipulation of DataFrames — one of the most powerful tools in Python’s Pandas library for handling structured data. Throughout this practical, I explored key operations such as:  • Creating DataFrames from dictionaries  • Accessing rows, columns, and indexes  • Performing filtering, sorting, and summary statistics By the end of the lab, I gained hands-on experience in efficiently managing and analyzing datasets — an essential skill for any aspiring data scientist or analyst. 📁 Explore the repository here: 👉 https://lnkd.in/epWys7e7 #DataScience  #Python  #Pandas  #MachineLearning  #DataAnalysis  #Statistics  #JupyterNotebook Ashish Sawant Sir

 Python’s Hidden Gem: The Power of itertools Ever come across repetitive loops in Python and thought, “There must be a smarter way to do this”? That’s exactly where the "itertools" module shines. itertools - is one of those underrated Python modules that quietly powers some of the most efficient and elegant solutions — from handling infinite sequences to building complex data pipelines. Why it matters: >It helps write cleaner, faster, and memory-efficient code. >Ideal for data science, automation, and algorithm design. >You can create combinations, permutations, Cartesian products, and even infinite iterators with just a few lines of code. Example: from itertools import combinations data_values = ['A', 'B', 'C'] for letters in combinations(data_values, 2): print(letters) Output: ('A', 'B') ('A', 'C') ('B', 'C') In just one line, itertools saves you from writing loops within loops — turning complexity into simplicity. If you’ve ever wondered how to make your Python code feel more “pythonic,” start exploring itertools — it’s like having a mini toolset of algorithmic superpowers. #Python #Coding #DataScience #Automation #SoftwareEngineering #TechEducation #itertools #ProgrammingTips

🔥 Immutable but Powerful — The Secret Strength of Python Tuples! When was the last time you used a tuple in Python and truly understood why it exists? Tuples are one of those underrated data structures every data engineer should master — especially for clean, efficient, and immutable code. Here’s a quick rundown 🧠👇 ✅ Immutable & Ordered → Once created, can’t be changed. ✅ Faster than Lists → Great for read-only collections. ✅ Can store mixed types → (1, "data", 3.5) ✅ Hashable → You can use them as dictionary keys! ✅ Perfect for packing/unpacking data in pipelines. Example: t = (1, 2, 3) a, b, c = t # Unpacking print(a) # 1 If you’re preparing for interviews — remember, tuples often come up in questions about immutability, performance, or hashability in Python. 💡 Takeaway: Tuples aren’t just “immutable lists” — they’re a signal that your data is fixed and safe to share across your codebase. What’s your go-to use case for tuples in real-world data pipelines? 👇 #Python #DataEngineering #LearnPython #PythonDeveloper #CodingInterview #100DaysOfCode

After spending years in real-world Python work, one truth stands out clearly… Your code becomes cleaner, faster, and far easier to debug the moment you truly understand the behaviour of basic data structures. Not the fancy stuff. Not the advanced libraries. Just the fundamentals — lists, sets, and dictionaries. Because most real-world mistakes don’t happen in complex ML models… they happen in simple lines like append(), pop(), remove(), or forgetting how sets treat duplicates. This chart is a good reminder. Lists when you need order and flexibility. Sets when you want uniqueness and lightning-fast lookups. Dictionaries when you need structure and meaning. Master these, and suddenly your Python logic starts making sense — your scripts break less, your confidence grows, and your time-to-solution becomes unbelievably faster. Sometimes levelling up is not about learning more. It’s about understanding what you already use every day — deeply. If you’re learning data analytics and you want clarity in exactly how to think, not just what to type , I’ve created simple, practical learning kits and resources based on real project experience. check link Here https://lnkd.in/gasgBQ6k #DataAnalyst #DataScience #Python #DataJourney #PowerBi #SQL

This newly published article appears to be unrelated to data science or technical content, despite being tagged under "Data Science" on Medium. Upon review, it does not provide insights or resources relevant to programming, analytics, or machine learning. As professionals exploring curated content for growth and learning, it's important to critically evaluate sources and focus on material that advances our understanding in the field. Check the article yourself here for further context: https://lnkd.in/d8Ss6pVg Channel: Data Science on Medium #DataScience #MachineLearning #Python #DataAnalysis