NumPy Boolean Masking for Efficient Data Extraction

Day 50 of Python Learning | NumPy isinf() Today I learned how to detect infinite values in NumPy arrays using np.isinf() 🔹 np.inf represents infinity in NumPy 🔹 np.isinf() returns a boolean array 🔹 Helps identify invalid or overflow values in data Example use case: Checking datasets for infinite values before analysis or modeling #51dayofPython #Python #Fullstackdeveloper

🚀 Python Practice – Match Strings & Loops Today I practiced handling strings and loops in Python, focusing on writing cleaner and more readable logic. 📌 Concepts covered: • 🔁 Using for loops to iterate over strings and lists • 🔍 Matching strings with conditions • 🧠 Using match-case for structured string handling • ✨ Reducing long if-elif blocks for better clarity Understanding how loops and string matching work together helps in building real-world logic like menus, commands, and validations. Small concepts, strong foundations 💪 #Python #LearningPython #Loops #StringMatching #ProgrammingBasics #DataScienceJourney #Consistency

Day 3 of my Python learning and posting journey 🐍 Today I learned about typecasting and subtypes in Python, and how Python converts data from one type to another. I also understood the difference between implicit and explicit typecasting. Implicit typecasting: Python automatically converts data types when needed. Explicit typecasting: We manually convert one data type into another using functions like int(), float(), etc.I ran a small practice program to understand this better — sharing the screenshot below 👇 #Python #LearningJourney #Day3 #ProgrammingBasics #Typecasting #Consistency

I Automated my Excel Reports in Python #programming #python #coding Here is how to Merge multiple Excel files into a single master report instantly using Python. This script leverages the glob library to find matching files and pandas to concatenate them into one dataframe.

Built a SVD image compressor from scratch to better understand the math behind it Tech stack: Python, NumPy, Matplotlib, PIL Check out the code: https://lnkd.in/ef_f_wZu #Python #MachineLearning #LinearAlgebra #DataScience #Programming

Merged PR to Pydantic : Support Root models with Literal root types as discriminator fields Working on major Python libraries teaches you about architecture and design patterns. This enables developers to use Literal types for discriminating between API response types - crucial for type-safe systems. https://lnkd.in/dsY8AqKz #OpenSource #Python #Pydantic

🚀Discovered why Python lists kill performance (200ms vs 2ms for 1M elements!) while NumPy delivers 100x speedup + 8x less memory. Explained with a chef analogy: cache misses = running to the store, GIL = one chef rule. Read full blog on medium: https://lnkd.in/dWDGWtuw #NumPy #Python #DataScience #Performance #GenAi #DataScience

Working with data in Python comes down to two essential libraries – Pandas and NumPy. Put together a quick guide on how they work and when to use each for data manipulation and analysis. #Python #Pandas #NumPy #DataAnalytics #CDAC #DataScience

Messy data is messy… but Python makes it clean! Removed duplicates, handled missing values, and fixed data types with Pandas. #DataAnalysis #Python #DataCleaning #LearningPython Here’s a screenshot of the result: