Python-bloggers

Pyomo is another flexible Open Source optimisation modelling language for Python. It can be used to define, solve, and analyse a wide range of optimisation problems, including Linear Programming (LP) and Mixed-Integer Programming (MIP), nonlinear programming (NLP), and differential equations. 📢 The book Hands-On Mathematical Optimization with Python (available free online) ...

In a previous post, we explored the plotnine package in Python in Excel, highlighting how its implementation of the Grammar of Graphics allows for an almost limitless array of possibilities in creating any plot imaginable: In this post, we’ll focus on how to craft layered (i.e., multiple elements ...

Python in Excel offers powerful tools for creating impactful visualizations, and one standout option is the plotnine package. In this post, we’ll delve into the origins and philosophy of plotnine, explore its core building blocks, and demonstrate some easy and impressive visualizations you can create—perfect for Excel users ...

Data science interviews present unique challenges that blend theoretical knowledge, practical coding skills, and business acumen. After helping hundreds of candidates prepare for roles at top tech companies, I’m sharing key strategies and tools that can streamline your preparation process. Building a Solid Foundation Before diving into specific interview ...

CVXPY is a powerful, Open Source optimization modelling library for Python. It provides an interface for defining, solving, and analysing a wide range of convex optimization problems, including Linear Programming (LP), Quadratic Programming (QP), Second-Order Cone Programming (SOCP), and Semidefinite Programming (SDP).

SciPy is a general-purpose scientific computing library for Python, with an optimize module for optimisation.

In a previous post, we explored the Seaborn library and highlighted its fundamental plot types, showcasing how it simplifies the creation of visually appealing and insightful data visualizations directly in Excel using Python: In this post, we’ll take it a step further by diving into advanced, statistically driven plots—...

Media processing needs have increased by over 300% in the last five years. Organizations are looking for faster ways to handle transcoding. The biggest problem with CPU-based transcoding is high energy costs and poor performance when processing multiple streams at once. iGPU servers are a great alternative that combines integrated graphics ...

The moment of truth has arrived! On Day 28, we iterated through all the metrics we had previously used to identify and analyze the robustness of our strategy. We found the new adjusted strategy performed better than the original and adjusted strateg...

On Day 27, we had our strategy enhancement reveal. By modifying the arithmetic behind our error correction, we chiseled another 16% points of outperformance vs. buy-and-hold and the original 12-by-12 strategy. All that remains now is to run the pred...