Complete Python Testing Guide by Jan Giacomelli is out 🚀 Learn how to write better tests with your AI agents to deliver better software faster. Including agent files - CLAUDE .MD and .cursorrules https://lnkd.in/ecD3e9_8
Python Testing Guide by Jan Giacomelli
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Evaluating Expressions in Python using eval() While practicing on HackerRank, I explored the power of Python’s built-in eval() function. *Problem Statement: Read a mathematical expression from input and evaluate its result. Solution: # Enter your code here. Read input from STDIN. Print output to STDOUT expression = input() print(eval(expression)) * How it works: input() takes the expression as a string (e.g., "3 + 5 * 2") eval() evaluates the string as a Python expression The result is printed directly Example: Input: 3 + 5 * 2 Output: 13 *Important Note: eval() is powerful but should be used carefully, as it can execute arbitrary code. It’s safe in controlled environments like coding platforms, but not recommended for untrusted input in real-world applications. -> Always exciting to learn how Python simplifies complex tasks with minimal code! #Python #HackerRank #CodingPractice #Programming #LearnToCode #100DaysOfCode
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Tim Hopper suggests that one obstacle in the way of uv adoption is that AI agents decide on `pip install` when Python projects look like the many pre-uv projects on GitHub. Likewise, I also started my project with conda instead of uv because I was in the habit of doing data science projects. I'm glad I made the switch since I didn't end up using any data science libraries. It has sped up the GitLab CI/CD pipeline for my project because uv sets up the environment faster than conda. Check out Tim's blog post for more details on the forces hindering uv adoption. Will you be using uv for your next project? Have you already started?
Despite the rapid growth and widespread affinity for the uv package manager in Python, a recent analysis indicates the majority of new projects on GitHub aren't using it. It seems like there are a varity of reasons for this: 1. LLMs/coding agents default to pip 2. pip is the recommendation of Python docs and the official Python Packaging User Guide 3. Developer inertia compounded by this being the example seen in many popular repos Check out the blog post in the thread for more thoughts...
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Built a small Python script that auto-organizes files in a folder by type like images, videos, documents, code files and more. Nothing fancy, just a useful little tool I made for myself. Open-source if anyone wants it. GitHub: https://lnkd.in/gt8cEsYf #Python #Automation #OpenSource
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I kept seeing coding agents drift into pip install, raw python, and manual dependency edits in Python projects. It works until it doesn’t, and the inconsistency becomes annoying fast, especially when the agent also uses small background Python scripts you did not explicitly ask for. This post is a short reflection on that pattern and the small uv-first setup I now use to keep Claude Code and Codex more consistent across runs. I wrote up the reasoning, the repo-level instruction approach, and the example setup here: https://lnkd.in/dAvyjEwr
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🐍 Python Term of the Day: Unicode (Python Glossary) Unicode is a universal character encoding standard that assigns a unique number (code point) to every character in every language, plus symbols, emojis, and control characters. https://lnkd.in/gMsCtXD3
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Introduction to NumPy NumPy is a Python library used for working with arrays. NumPy is a library consisting of multidimensional array objects and a collection of routines for processing the arrays In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object which works faster than traditional Python lists. NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.
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How to Add a Row to a DataFrame in Python Master the surprising truth about DataFrame row additions - from basic loc[] tricks to advanced concat() techniques. Python experts hate this approach. https://lnkd.in/dmyXyETX
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