Python Log Inspector Tool with Regex and Reporting

I just built a simple but practical project: financial data automation using Python. The problem: Many operations still rely on manual spreadsheets, which are prone to errors and rework. The solution: I developed a script that reads a financial CSV file, validates the data, processes it, and automatically generates a structured report. What the project does: - Validates required columns - Handles data errors - Calculates key financial metrics (total, average, sales, expenses) - Groups data by category - Generates a final report in .txt format Tech stack: - Python - Pandas Result: A simple workflow that transforms raw data into organized information with no manual intervention. 🐱 GitHub project: https://lnkd.in/dJ53JNVA Next steps: - Export results to CSV - Add dynamic file input - Deploy in the cloud This is just the beginning, the focus now is to keep building solutions that solve real-world problems.

#Learnings File operations are really helpful especially for csv and json files. We perform various operations read, write in normal file manager too, but Python allows multiples diverse logics along with various data structures that make it an effective way to perform such operations. Here is a glimpse of a similar code shared below based on file operations: https://lnkd.in/gjFbX85v

🐍🕸 Python Web Scraping Tutorials — Web scraping is about downloading structured data from the web, selecting some of that data, and passing along what you selected to another process https://lnkd.in/g_K889x

Software news: teiphy v.0.1.24 is now available at https://lnkd.in/gjXnatFh! I've made some Dependabot-informed dependency updates (which unfortunately required me to drop Python 3.9 support); conversion methods now include a progress bar; and BEAST 2.7 XML outputs are more streamlined to reduce unnecessary computation. As always, you can check out the source code directly on GitHub, or you can install the latest version easily with pip via pip install teiphy

Built a PDF Text Validator using Python. It checks whether a specific text is present across multiple PDF files in a folder and generates a validation report. How it works: → Select a folder containing PDF files → Enter the required text to search for → Click Run Validation → Each PDF is scanned and marked as Pass or Fail → A CSV report is generated with file name, status, and remarks Features: → Tkinter GUI with folder browser and text input field → Case-sensitive and case-insensitive search options → Subfolder scanning support → Color-coded results table (green for Pass, red for Fail) → Summary showing total files checked, passed count, and failed count → One-click CSV export Built entirely with Python standard library. No external packages required. #Python #Automation #PDF #Tkinter #Scripting #ProcessAutomation

Made a Data Visualization Dashboard only using Python! DataViz is a lightweight, terminal-based data visualization tool designed for quick and efficient analysis of structured data. It enables users to generate graphs directly from datasets while also supporting manual data entry, making it flexible. The tool provides seamless CSV import and export functionality, allowing users to easily load existing data and save processed results for further use. With its minimal setup and fast execution, DataViz offers a simple yet powerful way to visualize and manage data directly from the command line. I have uploaded the project on my git https://lnkd.in/d8VhW4VE Steps to access: 1. Go to repo 2. Click and access the latest release page 3. Download the .exe file(dataviz.exe) and All set! Make sure to give a star if you liked the project !

🚀 Mini Project Showcase: Python File Organizer As part of my Data Analyst learning journey, I worked on a small Python project while revising my SQL concepts. 📂 Project: File Organizer using Python : This script automatically organizes files into folders like Images, Documents, Videos, etc., based on their file types. 🔧 What I used : Python (os, shutil modules) Logical structuring of file types Automation concepts 📊 Why this matters for Data Analytics : While learning SQL helps in querying data, Python helps in automating repetitive tasks and handling real-world data files. 💡 Key Learnings: File handling in Python Automation basics Writing cleaner and reusable code 🔗 GitHub Repository : https://lnkd.in/dGcnCmXT This is a small step, but I’m consistently building my skills in both Python and SQL to become job-ready as a Data Analyst. #Python #SQL #DataAnalytics #BeginnerProjects #LearningJourney

Excited to share proxyml, an open source Python SDK I've been building for privacy-preserving model explainability. Most XAI tools require sending your data somewhere; proxyml doesn't. Your training data never leaves your environment. pip install proxyml to try it out, and star the repo if you want to follow along as the server-side API comes online. https://lnkd.in/gACWJNxd

🧠 Python Concept: TypedDict (Structured Dictionaries) Make dictionaries safer 😎 ❌ Normal Dictionary user = { "name": "Alice", "age": 25 } 👉 No structure 👉 Easy to make mistakes ✅ With TypedDict from typing import TypedDict class User(TypedDict): name: str age: int user: User = { "name": "Alice", "age": 25 } 🧒 Simple Explanation 👉 TypedDict = dictionary with rules 📋 ➡️ Defines expected keys ➡️ Defines data types ➡️ Helps catch errors early 💡 Why This Matters ✔ Better type safety ✔ Cleaner code ✔ Great for large projects ✔ Helps with IDE + static checking ⚡ Bonus Example class User(TypedDict, total=False): name: str age: int 👉 Fields become optional 😎 🧠 Real-World Use ✨ API request/response models ✨ Config files ✨ Data validation layers 🐍 Don’t use random dictionaries 🐍 Define structure #Python #AdvancedPython #CleanCode #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

In the previous article of this “Moving from Python to esProc SPL” series, I covered how to set up esProc SPL and load your first datasets. It’s now time to learn what makes esProc SPL a tool for data analysis: its syntax and data structures. In this article, you’ll learn about SPL’s core syntax elements, its primary data structure (the table), and how to perform common data operations. By the end, you’ll understand how to write effective SPL code and how it compares to equivalent operations in Python. Read below👇 https://lnkd.in/dnjpuPAR