Christophe Blefari’s Post

#Python is generally available in #Excel #copilot in Excel knows Python A whole new world of possibilities opens up. #pythoninexcel

Day 11: Advanced Data Types - Unlocking Python’s Collections Module 🔑 "Python’s collections module: Supercharging your data structures with elegance and efficiency!" Today, let’s explore Advanced Data Types from the collections module—tools that make data handling more flexible and efficient. 1️⃣ Deque (Double-Ended Queue): Efficiently add or remove elements from both ends. Perfect for implementing queues or stacks. 2️⃣ Counter: Count elements in a collection effortlessly. It’s like having an enhanced dictionary specifically for counting. 3️⃣ OrderedDict: Maintains the insertion order (important before Python 3.7’s native dict ordering). 4️⃣ DefaultDict: Automatically provides default values for missing keys, saving you from tedious error handling. 5️⃣ NamedTuple: A tuple with named fields for better readability and accessibility (bye-bye, cryptic indexing!). 💡 Fun Fact: These data types aren’t just for convenience—they also come with optimized performance for specific use cases. Tomorrow, we’ll break down Lambda Functions and Functional Programming—time to simplify your code with powerful one-liners! #dailylearning #Python #DataStructures #AdvancedPython #Collections #CodingTips Masai Prepleaf by Masai

🐍 Python in Excel (via Azure) + Copilot “Python code runs in its own hypervisor isolated container using Azure Container Instances and secure, source-built packages from Anaconda through a secure software supply chain.” #Excel #Python #Copilot

HUGE news today in the Excel world. 🚀🐍🤖 Not only is Copilot in Excel available for general release, the new Copilot in Excel with Python features for advanced analysis have been announced. Expect Excel + Python + Copilot to be the new trinity for data analysts. Excited to be a part of these big movements forward. If you want to get up to speed check out some of the resources linked to at the bottom of this page, including my own LinkedIn Learning course on Copilot for Excel! https://lnkd.in/grPQ4ZTG

🚀 Leveling Up My Data Analysis Skills with Pandas! 🐼📊 Just completed an intermediate-level, hands-on course on the pandas library with instructor Jonathan Fernandes. This amazing journey focused on using pandas for effective data analysis and structuring. I worked with a large, real-world dataset (Olympic medal winners from 1896 to 2008!) and sharpened my skills in: 📑 DataFrames for managing data efficiently 📈 Basic plotting to visualize trends 📝 Boolean indexing for filtering data 🔍 String handling for better data manipulation 🔄 Indexing and grouping data for deeper insights 📊 Reshaping data and creating custom colormaps Excited to apply these new skills to solve real-world challenges! #DataAnalysis #Python #Pandas #DataScience #ContinuousLearning #OlympicsData

🚀 Exciting News for Data Enthusiasts! 🚀 I’m thrilled to share that Copilot in Excel with Python is now available in public preview! This game-changing feature combines the power of Python’s advanced analytics with the familiarity of Excel, making data analysis and visualization more powerful and accessible than ever before. 📊 Seamless Integration: Use Python libraries to clean, explore, and analyze data directly within Excel. 📈 Advanced Analytics: Perform complex data analysis like forecasting, risk analysis, create custom plots, and run machine learning models all within Excel ! This integration is set to revolutionize how we work with data in Excel. Let’s embrace this new era of productivity and innovation! 🌟 Lean more: https://lnkd.in/er-UsrrE

Data Quality 🧼 is a broad topic and people often underestimate it when building big or small solutions. You should try to scan one of your dataset using a tool like #Soda 🥤 it's always insightful! ( nice simple example 👉 https://lnkd.in/dJej3qsn , thanks Baturay Ofluoglu ❤️) Luckily the Python User Group 🐍 🇧🇪 decided to organize a Data Quality focused event! 😂 Would you like to learn how to orchestrate Data Quality scans with Soda & Prefect? Would you like to learn how to use Great Expectations? Would you like to finally understand Data Contracts? Then you can't miss the next event! RSVP using this link 👇 see you there 👀 https://lnkd.in/dD5Z34he

📊 Day-75 of my Python for Data Engineers learning journey: Pandas practice! 🐼 ♻ Combining Data in Pandas 🚀 Pandas' Series and DataFrame objects offer powerful data exploration and analysis tools. One of their strengths lies in their ability to combine separate datasets seamlessly. Here's how: 🔗 merge(): 📌 Combine data on common columns or indices. 🔑 join(): 📌 Join data on a key column or an index. ➕ concat(): 📌 Combine DataFrames across rows or columns. ➡️ append(): 📌 Append rows of another DataFrame to the end of the given DataFrame. Using these versatile data combination methods, you can effectively unify and better understand your data as you analyze it with Pandas! So, let's keep exploring and learning! Don't forget to stay curious! #Python #DataEngineering #Pandas #DataManipulation #LearningJourney

Hey folks, Its day-1 of my promised dive into the basics to strengthen my foundation in data analytics and tools I use. Revised basics and solved some problems to brush up my basics. Found new things which I had ignored or forgot about. It was refreshing to revisit basics. Tomorrow I plan to complete my revisit to python and move forward to next tool of my toolkit, probably MS Excel. See you tomorrow! "FOR THE DATA!!"

🐼🚀 Learning and Growing with Pandas... and Pandas! 🐼🚀 I’ve been spending time with Pandas lately—no, not the cute, bamboo-eating kind (though I wish 😅). I’m talking about Pandas, the powerful Python library for data manipulation! 📊 Today, I tackled the mysterious art of selecting rows with .loc[], and let me tell you, it felt like discovering a secret code! 🔍 Specifically, extracting rows 10 to 20 from my dataset with df.loc[10:20]—what a game-changer! Here’s what I found while “panda-ing” around with data: 🖱️ .loc[]: Accessing rows and columns by labels has never been this easy! 🎯 Slicing Magic: Handling data slices feels like cutting through bamboo—smooth and efficient. 🐼 Versatility: Whether it’s tiny datasets or jungles of data, Pandas keeps things light and agile! Data manipulation is like feeding your brain healthy bamboo—it helps you grow stronger insights! If you’re also “bamboo-zling” through Pandas or any data analysis tools, let’s connect and geek out! 🧠 #DataScience #Python #Pandas #DataManipulation #LearningJourney #PandasAndPandas #GrowthMindset #TechJourney