Python-bloggers

Working with Python code becomes significantly more manageable when objects are assigned clear, sensible names, and Excel is no different. This blog post covers how to create named ranges and tables in Excel using openpyxl. To begin this demonstration, we will import several modules from openpyxl, along with a sample ...

Several packages facilitate working with Python and Excel, but openpyxl stands out as an excellent choice due to its comprehensive features for reading and writing workbooks. In this blog post, we will explore how to get started with creating Excel workbooks from scratch and modifying existing ones using openpyxl. For ...

In a world where compute time is billed by the second, make every one of them count. There are zero valid reasons to utilize a quarter of your CPU and memory, but achieving complete resource utilization isn’t always a straightforward task. That is if you don’t know about ...

One of the key insights Excel users should appreciate about Copilot is its vital role in keeping Excel current in the AI era. It achieves this by innovatively integrating multiple intelligence sources, expanding Excel’s functions beyond traditional spreadsheets. Today’s era is characterized by growing system interoperability. In particular, ...

Parallelizing R dataframe computation is a guaranteed way to shave minutes or even hours from your data processing pipeline compute time. Sure, it adds more complexity to the code, but it can drastically reduce your computing bills, especially if you’re doing everything in the cloud. R doParallel package provides ...

Data visualization and analytics tools are crucial for businesses and researchers alike. Power BI, Spotfire, and R Shiny have emerged as significant players in the market. This article aims to compare these data visualization tools for businesses across various parameters, helping you make informed decisions based on your specific dashboard ...

For those interested in learning Python for Excel, a key area to explore is time series analysis. In particular, Python’s Pandas package is specifically designed to handle time series data effectively, often much better than Excel. In this post, we’re going to look at a common task with ...

In the previous article, we learned about the first OOP system in R called S3. In this one, we are going to dive into the S4 OOP system. The S4 system is a more formal OOP system developed by Bell Labs and introduced into the S language in the late 1990...

Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1