Announcing cleanframe: A Python library for data cleaning and validation

🐍 Power up your Python in Excel experience. https://msft.it/6044sahEQ Editable initialization is here, letting you set up Python your way. Maybe you prefer working with NumPy arrays instead of pandas DataFrames, or want to preload custom functions or libraries every time you open your workbook. Either way, you're in control. Save changes, reset defaults, and make Excel fit your workflow like never before. Get all the details in our latest blog by Ndeyanta Jallow, Product Manager on the Excel team. https://msft.it/6044sahEQ #MicrosoftExcel #PythoninExcel #ExcelforWindows

Pandas revolutionized Python analytics : pip install and done. But we're now using DataFrames for everything they weren't built for. What if you could get DataFrame simplicity + real database power? In this video, Mehdi Ouazza is covering 6 pragmatic reasons why you'd pip install #duckdb instead of yet another DataFrame library. 📺 https://lnkd.in/gJYwz63m

I'm a huge fan of DuckDB, but I can't say the same for their latest war on dataframes. Dataframe APIs are powerful, expressive, and a great choice for Python (and other) developers. This and another recent blog post on the same topic make the same mistake: they conflate the engine and the interface. SQL and dataframe libraries both provide APIs that operate on the underlying compute engine. Take Apache Spark, for example: Spark SQL is fundamentally equivalent to the Spark DataFrame API, whether you use Python, Scala, Java, or R.  To learn more about the difference between the engine and the interface, including some informative examples of why you might actually prefer to use a dataframe API, check out Gil Forsyth's great talk: https://lnkd.in/gwpD_rhx And to learn how you can use Python dataframes with DuckDB, read the DuckDB docs: https://lnkd.in/gzYWmuyz 🙂

LinkedIn — Day 14/30 (EN) Day 14/30 — Full recap on NumPy/Pandas/Matplotlib + Developer Environment set up 🔧 Today ML/Data: Performed a ground-up review of NumPy, Pandas, and Matplotlib. Fixed small mistakes, cleaned plots/data ops, and documented the repeat loop with concise notes. Python: Set up the developer environment: venv + pip deps, VS Code config (format/lint), a simple requirements.txt, initial git setup, and repo hygiene. Takeaway: A clean dev environment (venv, linter/formatter, clear structure) speeds up debugging and keeps the project portable. #90DaysOfCode #Python #Pandas #NumPy #Matplotlib #MachineLearning #DataScience #DeveloperEnvironment #GitHub #LearningInPublic

#30daysofcode Challenge #Day12 Lists Hi everyone! Today we dove into the fundamentals of Lists in Python. Lists are the cornerstone of data handling, allowing us to store collections of data in an ordered, changeable sequence. It's crucial for any task that involves managing multiple items! ✨Problem Focus: Here's the core logic for the foundational day: Initial Structure: Creating the list using [] and using the list() constructor. Data Population: Understanding that lists can hold mixed data types (integers, strings, even other lists).   Access & Indexing: Retrieving specific elements using positive and negative indexing. Basic Slicing: Extracting simple subsets of the list (e.g., the first three items). ✅ What I Learned Today: 💜 List Definition: Confidently creating lists and initializing them with data. 💜 Indexing Rules: Mastering how zero-based indexing works, along with using negative indices for access from the end. 💜 Immutability vs. Mutability: Solidifying the understanding that lists are mutable (changeable), a key feature separating them from tuples. 💜 Length and Emptiness: Using the len() function to check the size of the list. #Python #CodingChallenge #LearningPython #Fullstack #NxtWave #reactjs

Our support for py 3.14 is in beta 😉 We now provide experimental (beta-level) support for Python 3.14, enabling you to integrate the latest Python features directly into your data loading pipelines. This week was all about Python 3.14 and the sweet taste of π... though the cherry on top was watching the data ecosystem's recipe get rewritten. Also in dlt 1.15, we've added schema export in DBML format for visualizing pipeline designs, enhanced our REST API client with has_more flag support and JSON body pagination, and introduced fine-grained control over Delta Lake streamed execution for performance tuning. 🔗 Read the full release notes here: https://lnkd.in/dRvku2GK #Python314 #dlt #DataEngineering #OpenSource #DataPipelines

Here's how to override the default card preview of a Pandas DataFrame in Python in Excel. https://lnkd.in/du9_yWg7 The preview card shown is just one way of doing it - you can define it in any way you want - whatever is useful to your workflow, or your client's workflow. You create a function that defines the card layout, then register it with the excel.repr module. Place all of that in the editable Initialization pane in Python in Excel and your custom preview card will be used for every DataFrame in your workbook. It's all explained in the post. I hope it's useful! #data #analytics #python #excel

Did you know that pandas DataFrames are built on top of NumPy arrays? That’s part of why they feel so intuitive to work with. The underlying arrays give pandas the efficiency of numerical operations, while the DataFrame structure makes it easy to organize and explore data in a way that just makes sense. It’s a simple design, but it really makes working with data feel smooth. It’s also a nice example of how Python libraries are often built on top of other libraries, each layer adds functionality, showing how thoughtful design can make complex tasks feel straightforward.

🚀 𝟭𝟬 𝗣𝘆𝘁𝗵𝗼𝗻 𝗢𝗻𝗲-𝗟𝗶𝗻𝗲𝗿𝘀 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Python is full of hidden gems - and sometimes, one line of code can save you hours of work. That’s why I’ve compiled 𝟭𝟬 𝘀𝘂𝗽𝗲𝗿-𝘂𝘀𝗲𝗳𝘂𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗼𝗻𝗲-𝗹𝗶𝗻𝗲𝗿𝘀 for Data Engineering into a handy PDF. 💡 𝗖𝗼𝘃𝗲𝗿𝘀 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗮𝘀𝗸𝘀 𝗹𝗶𝗸𝗲: 🔹 Extract JSON metadata into DataFrame 🔹 Find top 5% slow queries 🔹 Rolling averages on logs 🔹 Detect schema changes automatically 🔹 Flag anomalies with sliding windows And more... 📂 This guide is short, simple, and practical - perfect for interview prep or day-to-day coding. 👉 Download the PDF attached & keep it as a quick reference. 💬 Which one-liner do you use the most in your projects? Drop it in the comments! #Python #DataEngineering #BigData #CodingTips #SQL #SoftwareEngineering #Learning #careergrowth

"Building Data from the Sky - My Weather CLI Project"☁️ Thrilled to share my latest hands-on project a Python Weather CLI Tool powered by the OpenWeatherMap API This tool fetches live weather updates from multiple cities, stores them efficiently in SQLite, and handles API rate limits smartly using retry logic🔁 Through this project, I explored API integration, JSON parsing, and data automation, gaining a deeper understanding of how real-world systems work behind the scenes💡 🔧Tech Stack: Python | Requests | SQLite |OpenWeatherMap API 🔗GitHub: 👇🏻 https://lnkd.in/gGvrUmFd #Python #ApIIntegration #SQLite #APIs #WeatherData #OpenWeatherMap #TechLearning #DataEngineering