How to use Logfire with Python notebooks for observability

In our earlier posts, we discussed the challenges of importing files with Pandas and how the File Module handles this task with ease. Once your data is in, the next step is to explore, clean, and preprocess it. That’s exactly where the 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝘂𝗹𝗲 comes in. The Data Module isn’t just a code generator, it’s also equipped with a built-in 𝗰𝗼𝗻𝘀𝗼𝗹𝗲 where you can run Python code directly. Think of it as a lightweight IDE, built to speed up your data exploration and preprocessing workflow. In this demo, we show how the Data Module 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁𝘀 𝗮𝗻𝗱 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝘇𝗲𝘀 𝗮𝗹𝗹 𝘆𝗼𝘂𝗿 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗮𝘀 𝘆𝗼𝘂 𝗿𝘂𝗻 𝗰𝗼𝗱𝗲. In future demos, we’ll dive into how it uses 𝗰𝗼𝗱𝗲 𝗽𝗿𝗲𝘀𝗲𝘁𝘀 to accelerate data cleaning and preprocessing. We are also working on integrating an 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 to the Data Module, so stay tuned! In case you missed our earlier post - https://lnkd.in/dHYwBgmB #DataScience #DataAnalytics #FileImportAutomation #Pandas #DataExploration #DataPreprocessing #Modulytix

The video showcases a tool that generates realistic tick data with adjustable volatility, paving the way for live testing with real option chain data from CQF via its Python API. It simulates institutional flow with toxic flow event triggers, enabling a controlled burst of aggressive buy/sell orders. The source code's accessibility through Quantly allows for easy modification and real-time chart and metric analysis. The takeaway here involves the power of customizable simulation tools in understanding and reacting to market dynamics, offering an educational platform for visualizing order books, trade volumes, and signal analytics. #marketanalysis #tickdata #optiontrading #volatility #quantly #tradingtools

"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

We're excited to release a major update to the TUFLOW python library, PyTUFLOW, adding new methods for interacting with TUFLOW's input files, offering exciting opportunities for model workflow efficiencies. For those at the Flood Modeller conference, I'll be sharing some examples in my presentation tomorrow. https://lnkd.in/e3-c6De7

# fundamentals data types : # int # float # bool # complex # int # decimal base 10 ( 0......9 ) # binary base 2 ( 0 and 1 ) # octal base 8 ( 0......7 ) # hex decimal base 16 ( 0....9 and a=10 ,b=11, c=12, d=13, e=14, f=15 ) # float # 4.25 # 3.42 # also python represents scientific notations # examples 5e2

Day 36 of #100DaysOfCode challenged me to "Implement Trie (Prefix Tree)." This data structure is essential for efficiently storing and retrieving strings, commonly used in autocomplete and spellcheckers. The implementation required designing a TrieNode to hold a map of children (next characters) and a boolean flag (is_end) to mark the end of a word. The three core methods use simple traversal: insert: Traverse the word, creating new nodes for missing characters, and marking the final node as a word end. search: Traverse until the end of the word and check the final node's is_end flag. startsWith: Traverse the prefix; if any character path is missing, the prefix doesn't exist. All three operations are incredibly fast, achieving an optimal O(L) time complexity (where L is the length of the word/prefix). #Python #DSA #Algorithms #Trie #DataStructures #100DaysOfCode #ProblemSolving

👉Title: Master `itertools.combinations` for Pairwise Analysis ✍️Description: Use `itertools.combinations` to generate all possible pairs from a list, perfect for comparing items in data analysis without writing nested loops! 💻Code Snippet: from itertools import combinations items = ['A', 'B', 'C'] pairs = list(combinations(items, 2)) print(pairs) # Outputs: [('A', 'B'), ('A', 'C'), ('B', 'C')] 🗯️ 💻 Code smarter with #Pynfinity ❤️ 🔖Website : https://lnkd.in/gH9Vn5zX #Python #PythonTips #DataAnalysis #santoshtvk #Itertools

⏱️ Python Snippet: Process Timeout Manager Ever had a command hang forever and freeze your workflow? ⚠️ This snippet runs shell commands with a safe timeout — killing them if they take too long. ✅ Timeout handling to prevent stuck processes ✅ Clean output: stdout, stderr, return code ✅ Perfect for automation, CI/CD, and monitoring scripts 💡 Example Output: ✅ Output: PING google.com (142.250.181.78): 56 data bytes 64 bytes from 142.250.181.78: icmp_seq=0 ttl=118 time=14.5 ms 64 bytes from 142.250.181.78: icmp_seq=1 ttl=118 time=13.9 ms A must-have for production scripts and automation pipelines 🚀 #Python #SystemUtilities #BestPractices #DataDrivenInsights

Preparing the final screens for the Digital PAC Meter. All the screens are in SVG format, so they stay sharp and scalable within the platform. Just a small insight of the detail that goes into bringing the digital meter to life: ▪ Finalising the SVG screens ▪ Updating the Python script to incorporate them ▪ Testing functionality Once the template is built, it’s scalable and repeatable — swap out the device name and you’ve got a fully functioning digital meter ready to go. Almost at the finish line now…

I built a lightweight Network Traffic Analyzer in Python to better understand how traffic flows through a computer. 🔹 Captures live packets with Scapy 🔹 Tracks bandwidth usage in real time 🔹 Highlights top protocols 🔹 Lists active connections with psutil 🔹 Visualizes results using Matplotlib This project was a great way to combine networking knowledge with Python scripting and data visualization. Next, I’m planning to extend it with alerts for unusual traffic patterns. 📌 Code here: [https://lnkd.in/eYa6-HqH] #Python #Networking #Projects #LearningByDoing