Polars outperforms Pandas in large datasets

A very great comparison between Polars and Pandas! 🐻❄️🐼 Polars’ Lazy Evaluation and Streaming capabilities allow you to process 100GB+ files in chunks without crashing your kernel. While Pandas is great for quick EDA, Polars is the gold standard for high-performance Batch and Stream pipelines. The learning curve is minimal, but the performance gain is massive. Personally, I’m using Polars to read XML files over 10GB, and then using Pandas for data cleaning and manipulation techniques. This pipeline reduces processing time by 10x, preventing the script from crashing.

🚀 Stop crushing your RAM with huge CSVs! Before (messy): import pandas as pd def load_huge_file(filepath): # Reads everything into memory at once! Crash waiting to happen. df = pd.read_csv(filepath) return df After (clean): import pandas as pd def process_huge_file_in_chunks(filepath, chunksize=10000): chunk_iterator = pd.read_csv(filepath, chunksize=chunksize) for chunk in chunk_iterator: yield chunk # Yielding keeps memory usage low Why this matters for data engineers: Iterating with generators avoids OOM errors when processing multi-GB raw files, making pipelines robust and scalable. What's your favorite memory-saving trick in Pandas? #DataEngineering #Python #Pandas #ETL #BigData

Stop using Pandas for your production pipelines! Most of the data teams switched to Polars for core processing, especially for large datasets and pipelines. You should start using it too! Here is why you should use Polars 🐻❄️: 🔺2-10x faster than Pandas 🔺2-5x less RAM usage 🔺Lazy API (allows the query optimizer to reorder operations for maximum efficiency) When should you stay on Pandas 🐼? ▪️Standard tools compatibility: Many libs (like scikit-learn, PyTorch, ...) are still integrated with Pandas; if you use Polars, you will have to convert the dataframe to Pandas in the lib usage step ▪️Small datasets (less than ~100MB): Using Polars with those small datasets can be slower (overhead by Polars' multi-threading ⚡Quick Summary:  For production, large datasets, or high performance is required ➡️ Use Polars For research, educational work, or quick exploration ➡️ Use Pandas #DataEngineering #Python #Polars #ETL

Day 2/15 — Creating Your First NumPy Arrays Yesterday you saw why NumPy is faster than Python lists. Today you actually start using it. NumPy arrays are the core structure used for numerical computation, data science, and machine learning. Unlike Python lists, NumPy arrays are designed to handle large amounts of data efficiently. Today you learned: • How to create arrays using np.array() • Converting Python lists into NumPy arrays • Checking array type using type() • Understanding dimensions using .ndim • Creating arrays from basic user input These fundamentals are important because every dataset you work with in machine learning will eventually be converted into NumPy arrays. Once your data is in array form, you can perform fast mathematical operations on entire datasets at once. Mini Challenge: Create a NumPy array from this list and print its dimension: [10, 20, 30, 40] Then print: type(array) array.ndim Share your output in the comments. I’m sharing 15 days of NumPy fundamentals — building the core math foundation for Data Science and Machine Learning. Next up: Specialized array initializers like zeros, ones, arange, and linspace. Working with arrays and inspecting values becomes easier in PyCharm by JetBrains, especially with variable explorers and debugging tools. Follow for the full NumPy learning series. Like • Save • Share with someone learning Data Science. #NumPy #Python #DataScience #MachineLearning #LearnPython #Coding #Programming #Developers #JetBrains #PyCharm

Built and deployed an end-to-end ML pipeline — Student Exam Score Predictor. Not just a notebook. A full production-style system: Data ingestion → transformation → hyperparameter tuning → model selection → Flask API → deployed Best model: Lasso (R² 0.88) — selected over CatBoost and Gradient Boosting after tuned comparison. Stack: Scikit-learn, XGBoost, CatBoost, Flask, Python Live demo: https://lnkd.in/d2MsqRjK GitHub: https://lnkd.in/diQZjtcj PS: Albeit a simple project, this one helped learn how to maintain a solid file structure and documentation which will help me with my next project #MachineLearning #Python #Flask #EndToEndML

The best way to learn ML? Stop using libraries. I challenged myself to build linear regression using only NumPy and pandas. No sklearn. No model.fit(). No shortcuts. The result: 3 days of debugging, 4 major bugs, and one working model. I documented everything in a new Medium article: The math behind gradient descent (explained simply) Why feature scaling saved my model from exploding The dummy variable trap I almost fell into How I fixed R² = -6660 (yes, negative six thousand) If you're learning data science, this will save you hours of frustration. Read the full story: [https://lnkd.in/gvEu6-fM] Code on GitHub: [https://lnkd.in/gQUsAfzD] #DataScience #MachineLearning #Python #100DaysOfCode

--- 🚀 Day 11 — Understanding NumPy Arrays (Core Operations) #M4aceLearningChallenge Today, I went deeper into NumPy arrays, which are the backbone of numerical computing in Python. Unlike regular Python lists, NumPy arrays are faster, more efficient, and support powerful mathematical operations. 🔹 Key Concepts I Learned: 1. Creating NumPy Arrays import numpy as np arr = np.array([1, 2, 3, 4]) print(arr) 2. Array Attributes print(arr.shape) # Shape of the array print(arr.ndim) # Number of dimensions print(arr.dtype) # Data type 3. Indexing and Slicing print(arr[0]) # First element print(arr[1:3]) # Slice from index 1 to 2 4. Mathematical Operations arr2 = np.array([5, 6, 7, 8]) print(arr + arr2) # Element-wise addition print(arr * arr2) # Element-wise multiplication 5. Broadcasting NumPy allows operations between arrays of different shapes: print(arr + 10) # Adds 10 to each element 💡 Key Takeaway: NumPy arrays make data processing much faster and cleaner, especially when working with large datasets or preparing data for machine learning models. Every step I take with NumPy makes me more confident handling real-world data. ---

Discover the power of data science with Python and learn how to analyze and interpret complex data with our comprehensive guide, covering data analysis, machine learning, and visualization https://lnkd.in/gvUixiG3 #DataScienceWithPython Read the full article https://lnkd.in/gvUixiG3

Discover the power of data science with Python and learn how to analyze and interpret complex data with our comprehensive guide, covering data analysis, machine learning, and visualization https://lnkd.in/gvUixiG3 #DataScienceWithPython Read the full article https://lnkd.in/gvUixiG3

Day 51 of my #100DaysOfCode challenge 🚀 Today I worked on a Python program to perform Matrix Transpose using NumPy. This is a fundamental concept in linear algebra and widely used in Data Science & Machine Learning. What the program does: • Creates a 2D matrix using NumPy • Transposes the matrix (rows ↔ columns) • Uses built-in .T for efficient computation • Displays original and transposed matrix Original Matrix: [1, 2, 3] [4, 5, 6] [7, 8, 9] Transposed Matrix: [1, 4, 7] [2, 5, 8] [3, 6, 9] How the logic works: • Create matrix using NumPy array • Use: 👉 matrix.T • This automatically swaps: • Rows → Columns • Columns → Rows • No manual loops required ✅ Why this is important: – Core concept in Linear Algebra – Used in Machine Learning algorithms – Essential for matrix operations & transformations – Makes code faster and cleaner with NumPy – Time Complexity: O(n × m) – Space Complexity: O(1) (view-based operation) Key learnings from Day 51: – Introduction to NumPy – Matrix transpose concept – Efficient built-in operations – Writing optimized Python code #100DaysOfCode #Day51 #Python #NumPy #DataScience #MachineLearning #Matrix #LinearAlgebra #CodingPractice #ProblemSolving #DeveloperJourney #BuildInPublic #BTech #CSE #AIandML #VITBhopal #TechJourney