Introducing duckrun: a Python package for lakehouse workflows

Maven Analytics Data Drill #6 — Streak Leaderboard (Python Solution) This month’s Maven Data Drill was focused on time-based data wrangling and analysis. My Approach using Python (pandas): - Loaded and normalized the data to remove time components using .dt.normalize(). - Used groupby("user_id") to collect each user’s unique lesson dates into a set, ensuring duplicates were removed. - Filtered users who were active on 2025-09-28. - For each user, looped backward from the target date (2025-09-28) and counted consecutive active days until a break was found. - Combined the results into a leaderboard showing the top 10 users with the longest active streaks. You can view my full Python solution and notebook here: Google Colab Link: https://lnkd.in/dzarex2Y #Data #Analytics #Python #Pandas #DataWrangling #Leaderboard #LearningAnalytics #data #analytics #mavendatadrill

Well... gotta start somewhere... Leetcode 75 + SQL 50 Day 1: 1768. Merge Strings Alternatively (🟢 Easy - Python) given two strings word1 and word2. Merge the strings by adding letters in alternating order, starting with word1. If a string is longer than the other, append the additional letters onto the end of the merged string. Return the merged string. EXAMPLE: Input: word1 = "abc", word2 = "pqr" Output: "apbqcr" Explanation: The merged string will be merged as so: word1: a b c word2: p q r merged: a p b q c r Initial Thoughts: - boy am I rusty - I can treat the strings like arrays - start with the first letters that can be added together starting with word1 then, word2 - then deal with any remaining letters if the length of the words is not the same My Solution: - create the result string to be a string of nothing (like an array with nothing in it) - the for loop will iterate over the length of the smallest word adding the letter with index "i" from the first word, then the second word concatenating it to the result string - to deal with any remaining letters there are if/elif statements to deal with the remaining letters depending on if the first or second word is longer - depending on what word is longer, it will concatenate the rest of the words remaining characters starting at the index which is the length of the smallest word

#me: in the dockerfile file, can I use 2 images, FROM python:3.11-slim and the 8n8 official image file to generate my contener? (2/2) #gemini: 2. Multi-Stage Build (Less common, but possible) Multi-stage builds are typically used to keep the final image size small by only copying necessary artifacts. In this scenario, you'd use the python:3.11-slim image to build or install Python libraries, then copy those libraries into the final n8n image. Example Dockerfile using Multi-Stage Build: Dockerfile # --- Stage 1: Builder (uses the lightweight Python image) --- FROM python:3.11-slim AS python_builder # Install pip dependencies to a dedicated directory WORKDIR /install COPY requirements.txt . RUN pip install --prefix=/install --no-warn-script-location -r requirements.txt # --- Stage 2: Final Image (uses the n8n image) --- FROM n8nio/n8n:latest # Switch to root to perform installation copy USER root # Copy the Python installation from the builder stage # /usr/local/lib/python3.11/site-packages is the standard install path COPY --from=python_builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages # Optionally copy the Python executable, though often not needed if n8n's base image already has Python installed # If n8n's base image is minimal, you might need to install Python's core dependencies first (like in the Simple Extension method) # and then copy just the installed libraries. # Switch back to the non-root 'node' user USER node For most n8n use cases that require Python, Simple Extension (Option 1) is the more straightforward and effective method.

𝑬𝒗𝒆𝒓 𝒐𝒑𝒆𝒏𝒆𝒅 𝒂𝒏 __𝒊𝒏𝒊𝒕__.𝒑𝒚 𝒇𝒊𝒍𝒆 𝒂𝒏𝒅 𝒕𝒉𝒐𝒖𝒈𝒉𝒕… “𝑰𝒕’𝒔 𝒃𝒍𝒂𝒏𝒌!?” 😅  That little blank file actually has a big role in Python projects. __init__.py tells Python that the folder it’s in should be treated as a package This seemingly "blank" file is the magic that transforms a regular directory into a Python package. Here's what it does: 𝑷𝒂𝒄𝒌𝒂𝒈𝒆 𝑹𝒆𝒄𝒐𝒈𝒏𝒊𝒕𝒊𝒐𝒏 : Tells Python "hey, this folder is a package, not just a random directory" 𝑰𝒎𝒑𝒐𝒓𝒕 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 : Lets you define what gets exposed when someone imports your package 𝑰𝒏𝒊𝒕𝒊𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏 𝑪𝒐𝒅𝒆 : Runs setup code when the package is first imported 𝑪𝒍𝒆𝒂𝒏𝒆𝒓 𝑰𝒎𝒑𝒐𝒓𝒕𝒔 : Instead of 𝒇𝒓𝒐𝒎 𝒎𝒚𝒑𝒂𝒄𝒌𝒂𝒈𝒆.𝒎𝒐𝒅𝒖𝒍𝒆.𝒔𝒖𝒃𝒎𝒐𝒅𝒖𝒍𝒆 𝒊𝒎𝒑𝒐𝒓𝒕 𝑴𝒚𝑪𝒍𝒂𝒔𝒔 you can enable 𝒇𝒓𝒐𝒎 𝒎𝒚𝒑𝒂𝒄𝒌𝒂𝒈𝒆 𝒊𝒎𝒑𝒐𝒓𝒕 𝑴𝒚𝑪𝒍𝒂𝒔𝒔 𝑷𝒓𝒐 𝒕𝒊𝒑 : While Python 3.3+ introduced namespace packages (packages without init.py), explicitly including this file is still 𝒃𝒆𝒔𝒕 𝒑𝒓𝒂𝒄𝒕𝒊𝒄𝒆 for clarity and compatibility. So yes , it may look blank, but it’s quietly doing important work behind the scenes #Python #Programming #SoftwareDevelopment #CodingLife

Simple Moving Average Estimation in Python ‎The following code shows how to estimate a 14-day Simple Moving Average: ‎Import pandas as pd ‎Import Metatrade5 as mt5 ‎ ‎def sma14(symbol): ‎      data= mt5.copy_rate_from_pos(symbol=symbol,mt5.TIMEFRAME_M1,0,20) ‎df=pd.DataFrame(data) ‎df['sma14]=df['Close']. rolling (14).mean() ‎   return df['sma14] ‎ ‎The .rolling(interval).mean() function will generate a pandas series with the SMA(14) values associated with each date; ‎

🐍 Day 6 of #100DaysLearningChallenge Topic: Modify Any Class or Function at Runtime | Monkey Patching in Python 🐵 Today, I learned how to change or replace a function/method of a class at runtime — without touching the original source code! 💡 Why Use Monkey Patching? Testing / Mocking: Replace slow API calls with fast mock data Temporary Fixes: Bypass a bug in an external library Add Features: Extend or change behavior temporarily ⚠️ Be Careful: Can confuse other developers May cause unexpected bugs Should be used temporarily, not permanently 🔹 Example: Replace Slow Function class ExternalService: def fetch_data(self): print("Starting slow network call...") time.sleep(5) # Slow call return "Real data from server" def mock_fetch_data(self): print("Mock: Bypassing network call") return "Mock test data" api_service = ExternalService() api_service.fetch_data = mock_fetch_data # Monkey patching result = api_service.fetch_data() print(result) Output: Mock: Bypassing network call Mock test data ✅ Time saved: ~0 seconds instead of 5 seconds How It Works: Original function is slow (or external, can’t modify it) Define a faster/custom version Assign it at runtime → api_service.fetch_data = mock_fetch_data Every call to fetch_data() now runs your version! 💡 Key Takeaways: Python allows modifying class/function behavior at runtime Perfect for testing, debugging, and temporary fixes Avoid overusing in production — document clearly Thanks, Saurabh Shukla Sir, for motivating us to explore hidden Python tricks 🙌 #100DaysLearningChallenge #Python #MonkeyPatching #CodingTips #Programming #Developer #BuildInPublic #PythonTips #SoftwareEngineering

Yes, data visualization tools with Python are powerful. There is no doubt about that. However, if you are blind, they leave you behind because the data visualizations are images. There’s no descriptions or anything like that for the visualizations that you generate. With tools like the monarch it helps with charts, but only if they’re in the right format, which these charts for things like pandas are not.

Python JSON Made Easy – PDF + Code for Beginners! JSON (JavaScript Object Notation) is a lightweight format to store & share data between apps, APIs, and databases. Learn how to: • Convert Python dict → JSON & back ✅ • Save/load data from files ✅ • Use JSON in real-world applications like chatbots, web apps & AI models✅ I’m sharing a PDF with step-by-step Python JSON example + code file so you can learn & practice. 📄 Check out the PDF and start coding today! #Python #PythonJSON #LearnPython #CodingForBeginners #JSON #DataHandling #PythonTutorial #QuickPythonTips

🐍 Python Command Cheat Sheet — Your Quick Reference → Basic Commands • print() – Display output • input() – Take user input • len() – Get data length → Variables & Data Types • int, float, bool, list, dict, tuple, set, str → Control Structures • if, elif, else • for, while • break, continue, pass → Functions • def, return, lambda → Modules & Packages • import • from module import function → Exception Handling • try, except, finally, raise → File Handling • open(), read(), write(), close() → List Comprehensions • [x for x in list if condition] → Decorators & Generators • @decorator_name • yield 🎓 Learn Python step-by-step → https://lnkd.in/dNB4GthH Credit: Gina Acosta Gutiérrez (@ginacostag) #Python #Coding #CheatSheet #ProgrammingValley #DataScience #PythonDeveloper #FreeCourses

Master Data Analysis with Pandas DataFrames! 📊 If you use Python for data, the pandas DataFrame is your essential tool. It's a powerful, flexible spreadsheet in code, key to efficient data manipulation. 1. Key Steps for Using DataFrames: Load & Inspect: Use pd.read_csv() to load data, and quickly check it with df.head() and df.info(). 2. Clean: Handle missing values (df.fillna()), check types (.astype()), and drop duplicates (df.drop_duplicates()). 3. Filter & Select: Pick columns (df[['col1']]) or filter rows based on conditions (df[df['value'] > 10]). 4. Analyze: Group data and aggregate stats (df.groupby().mean()) or create new features (df['new_col'] = ...). Pandas is the standard for data wrangling in Python. What's your go-to pandas trick? 👇 #Python #DataScience #DataAnalysis #Pandas #Dataframes #TechTips