Two from regular commenter here, Keith Lewis:
This is a library for creating xll add-ins for Excel from 97 through 2013. It makes every feature of the latest Excel SDK available to you including the big grid, wide character strings, and asynchronous functions. It is the easiest way to integrate your C and C++, or even Fortran, code into Excel to achieve the highest possible performance. You can also generate native documentation using the same tool Microsoft uses for their help files.
If you need a small, fast, portable, and self contained way to extend Excel’s functionality, this is the library for you. Just hand someone the xll and chm help file that you create and they are ready to go. No need to figure out what version of .Net they run, no Primary Interop Assemblies to worry about, no managed code that forces you to marshal data back and forth from Excel. There are also no automagic code generators, no proprietary markup languages to learn, and no wizards that hide things behind your back. Everything is just pure, modern, and readable C++.
Check out xllblog for the latest goodies in the pipeline.
And two maths and plotting links from Alfred Vachris:
Welcome to the GeomAlgorithms.com website. The full list of Algorithm Titles is shown below, and active links indicate the algorithms that have been posted and are now accessible.
The purpose of this site is to provide practical geometric algorithms for the software developer. That is, algorithms that are:
- Relevant – they solve significant geometric problems for real world applications
- Correct – they give accurate solutions for the problems
- Robust – they tolerate small numerical errors and avoid overflow within constraints
- Efficient – they are fast in practice for typical applications, both small and large
- Conservative – they use few resources, such as storage space
- Maintainable – they are straightforward to implement and troubleshoot
- Elegant – one can understand why they work, which gives confidence in their use.
Those who use the ggplot2 package in R and do everything else in Python will appreciate this Python port of the package from yhat.
Excel makes some great looking plots, but I wouldn’t be the first to say that creating charts in Excel involves a lot of manual work. Data is messy, and exploring it requires considerable effort to clean it up, transform it, and rearrange it from one format to another. R and Python make these tasks easier, allowing you to visually inspect data in several ways quickly and without tons of effort.
The preeminent graphics packages for R and Python are ggplot2 and matplotlib respectively. Both are feature-rich, well maintained, and highly capable. Now, I’ve always been a ggplot2 guy for graphics, but I’m a Python guy for everything else. As a result, I’m constantly toggling between the two languages which can become rather tedious.
Once you get the Python library installed (and its dependencies), you’ll be able to use the same layered graphics approach as the R package, with a similar syntax.
Also another couple of links from Alfred that have featured here before, but well worth another look:
Several files for numerical or financial Math, free for download and with no warranty.
This website is meant for scientists and engineers who want to use the ubiquity, convenience and power of Excel, including its flexibility to go beyond the functionality already provided by Microsoft. It contains many freely downloadable, open-access add-in functions and macros for Excel associated with my book, Advanced Excel for scientific data analysis, 3rd ed., Atlantic Academic 2012, as well as additional information that arrived too late tobe incorporated in the printed version or that required color.