Mastering NumPy: NumPy Foundation and Array Operations

🚀 Day-56 of #100DaysOfCode 📊 NumPy Practice – Finding Unique Values & Frequency Today I practiced identifying unique elements and counting their occurrences using NumPy. 🔹 Concepts Practiced: ✔ np.unique() ✔ Frequency counting ✔ Handling duplicate values ✔ Efficient array analysis 🔹 Key Learning: Using return_counts=True makes frequency analysis simple and efficient without loops — very useful in data preprocessing. Slowly stepping into data analysis concepts using NumPy 💡🔥 #Python #NumPy #DataAnalysis #ArrayOperations #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

🚀 Day-54 of #100DaysOfCode 📊 NumPy Practice – Filtering Even Numbers Today I practiced generating random arrays and filtering values using NumPy. 🔹 Concepts Practiced: ✔ np.random.randint() ✔ Boolean indexing ✔ Modulo operation ✔ Vectorized filtering 🔹 Key Learning: NumPy allows powerful filtering operations without using loops, making code cleaner and computationally efficient. Step by step moving deeper into NumPy & Data Analysis fundamentals 💡🔥 #Python #NumPy #DataScience #ArrayFiltering #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

🚀 Day-53 of #100DaysOfCode 📊 NumPy Practice – Conditional Array Modification Today I practiced conditional filtering using NumPy. 🔹 Concepts Practiced: ✔ Boolean indexing ✔ Conditional replacement ✔ Vectorized operations ✔ Efficient array manipulation 🔹 Key Learning: Using boolean indexing (a[a < 0] = 0) allows fast and clean data transformation without loops — one of NumPy’s biggest advantages. Slowly building strong fundamentals in NumPy & Data Handling 💡🔥 #Python #NumPy #DataScience #ArrayManipulation #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

📊 New Video: Pandas Advanced – Part 5 Advanced Indexing & Query Thinking is one of the most misunderstood areas in Pandas — and also one of the most important in real-world analysis. In this video, I cover: • .loc vs .iloc with clear examples • Label-based vs position-based indexing • How to think like an analyst when querying data • Common mistakes that silently break results 🎥 Watch here: https://lnkd.in/gTaT9s5p 📂 GitHub (code & notebooks): https://lnkd.in/gNFk2iPa Sharing this for anyone learning Pandas beyond the basics. #pyaihub #DataAnalysis #Python #PandasAdvanced

⚠️ Pandas trap: groupby() silently drops NaN keys by default, groupby() excludes rows where grouping columns contain NaN (dropna=True). This means: • Your training population may shrink • Group sizes may be biased • Downstream thresholds may fail Always define explicitly 💪 : Which rows you learn from. Whether NaN groups should be included (dropna=False). Your data quality assumptions before aggregation 🙅♀️ Silent defaults create silent bias. #Python #Pandas #DataScience #DataEngineering #DataQuality

Pandas GroupBy is powerful — but only when you understand how it actually works. In Pandas Advanced – Part 6, I break down: GroupBy internals (split → apply → combine) When to use apply, agg, and transform How analysts think while writing Pandas code Why some GroupBy code feels slow in real projects 🎥 Full video: https://lnkd.in/gyw2KAyC 📂 Code & learning notes: https://lnkd.in/gdzNcMaT #pyaihub #Pandas #DataAnalysis #Python #LearningInPublic

We are deeply rooted in open source: for our product Skore, it was natural to have an open source layer. Thanks Adrien Morel!

I’ve been strengthening my Python foundations by diving into NumPy with guidance from Intellipaat’s YouTube channel. This session covered the essentials: importing and installing NumPy, understanding dimensions and shapes, creating arrays (including random arrays), and working with NumPy data types. I also explored type casting and learned how to handle type casting errors — a critical skill for ensuring clean, reliable computations. I’ve documented my work and uploaded the notebook to GitHub: https://lnkd.in/djEdyFp8. Excited to keep building momentum and applying these skills to larger projects. Feedback and insights are always welcome. #NumPy #Python #DataScience #MachineLearning #Intellipaat #LearningJourney #DataScience #ComputerScience

🚀 Today’s Learning Update – Doubly Linked List (Data Structures) Today, I deepened my understanding of Doubly Linked List in Data Structures and Algorithms. 🔹 Learned how each node contains: data prev pointer next pointer 🔹 Implemented the following operations in Python: ✔️ Insertion at beginning ✔️ Insertion at end ✔️ Insertion at middle ✔️ Deletion of head, end and middle node ✔️ Forward and backward traversal Consistency in DSA practice is helping me strengthen my problem-solving skills step by step. 💪 #DSA #DataStructures #LinkedList #Python #CodingJourney #LearningEveryday #SoftwareDevelopment

𝗽𝗮𝗻𝗱𝗮𝘀 𝟯.𝟬: 𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝗦𝗲𝘁𝘁𝗶𝗻𝗴𝗪𝗶𝘁𝗵𝗖𝗼𝗽𝘆𝗪𝗮𝗿𝗻𝗶𝗻𝗴 New Feature: new default string dtype 🤖Problem When you filter a DataFrame and modify the result, you expect the original to stay unchanged. But sometimes pandas modified your original data anyway, triggering the SettingWithCopyWarning. 🌝Solution pandas 3.0 fixes this. Filtering now always creates a separate copy, so modifying the result never affects your original data. Upgrade to pandas 3.0 with “pip install -U pandas”. #data #dataanalysis #Pandas3 #datascience #tech #python