NumPy Tutorial for Python Data Science

🐍📰 In this tutorial, you'll learn how to start exploring a dataset with Pandas and Python. You'll learn how to access specific rows and columns to answer questions about your data. #python

🐍📰 The Pandas DataFrame: Make Working With Data Delightful In this tutorial, you'll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a Pandas DataFrame #python

"Think Your Python Code Is Slow? Stop Guessing and Start Measuring" Performance tuning without profiling is just educated guesswork. In my latest article on Towards Data Science, I show how you can use two great tools, cProfile and SnakeViz, to reveal exactly where your Python code is slowing you down—and how to fix it efficiently. Read the article for FREE using the link below. https://lnkd.in/eycxySZ5

Announcing Orbital for Python 0.3.0: Accelerated Tree-Based Models in SQL We are pleased to announce the release of Orbital for Python 0.3.0, a significant update to our library designed to streamline the deployment of machine learning models for Python and Scikit-learn users. Orbital for Python allows you to transform Scikit-learn pipelines directly into native SQL queries, enabling model inference to execute within your database and eliminating the need for separate Python environments for production scoring. For those familiar with the R ecosystem, Orbital for R provides a similar capability that allows you to predict in databases using tidymodels workflows. Version 0.3.0 optimizes tree-based models, addressing the challenge of long, complex SQL queries that can be difficult for database optimizers to parse and execute efficiently. This release specifically enhances the performance and compatibility of Decision Trees, Random Forests, and Gradient Boosted Trees. Learn more about Orbital 0.3.0 and its new capabilities: https://lnkd.in/gGZqw8sA

In my latest article on the Towards Data Science  blogging platform, “Think Your Python Code Is Slow? Stop Guessing and Start Measuring”, I walk through how to use cProfile and SnakeViz to identify real bottlenecks and optimise with confidence. If you care about the performance of your Python code, this is essential reading. Read the full article for free using the link below. https://lnkd.in/eycxySZ5

Learn how to visualize time-series data in Python. This guide from Murtaza Ali covers the concepts and provides detailed code examples for line, area, and stacked area charts.

Python for Everything 🧠 Data & Analysis Mastering Pandas with Python — A complete guide to using Pandas for data analysis, cleaning, visualization, and real-world projects.  https://lnkd.in/deUSqyXM 📊 Projects & Practice 100 Python Projects — From Beginner to Expert — Hands-on project book covering Flask, databases, data science with Pandas/Matplotlib, automation & more.  https://lnkd.in/dMdTZdnE 800 Days Python Coding Challenges with Explanation — Massive collection of practice problems with solutions.  https://lnkd.in/dxRj_3FT

🚀 Python Polars: A Lightning-Fast DataFrame Library Recently explored a tutorial on Polars, and it’s a great reminder of how modern data tools are evolving beyond traditional pandas workflows. Polars stands out for its speed, efficient memory usage, and lazy execution model—making it especially useful for large datasets, ETL pipelines, and analytics engineering use cases. Definitely worth exploring if performance and scalability are becoming bottlenecks in data workflows.

Python for Everything 🧠 Data & Analysis Mastering Pandas with Python — A complete guide to using Pandas for data analysis, cleaning, visualization, and real-world projects.  https://lnkd.in/dSPYS5m3 📊 Projects & Practice 100 Python Projects — From Beginner to Expert — Hands-on project book covering Flask, databases, data science with Pandas/Matplotlib, automation & more.  https://lnkd.in/dv7K9-Mx 800 Days Python Coding Challenges with Explanation — Massive collection of practice problems with solutions.  https://lnkd.in/dJde857P

Ever waste time optimizing a piece of Python code only to see no real speed improvement? Tom Reid's new article explains how to avoid this by profiling your code first.