Practical Business Python

Indeed DataFrames.jl isn't and won't be the fastest way to do many things. It makes a lot of trade offs in performance for flexibility. The columns of the dataframe can be any indexable array, so while most examples use 64-bit floating point numbers, strings, and categorical arrays, the nice thing about DataFrames.jl is that using arbitrary precision floats, pointers to binaries, etc. are all fine inside of a DataFrame without any modification. This is compared to things like the Pandas allowed datatypes (https://pbpython.com/pandas_dtypes.html). I'm quite impressed by the DataFrames.jl developers given how they've kept it dynamic yet seem to have achieved pretty good performance. Most of it is smart use of function barriers to avoid the dynamism in the core algorithms. But from that knowledge it's very clear that systems should be able to exist that outperform it even with the same algorithms, in some cases just by tens of nanoseconds but in theory that bump is always there.

In the Julia world the one which optimizes to be fully non-dynamic is TypedTables (https://github.com/JuliaData/TypedTables.jl) where all column types are known at compile time, removing the dynamic dispatch overhead. But in Julia the minor performance gain of using TypedTables vs the major flexibility loss is the reason why you pretty much never hear about it. Probably not even worth mentioning but it's a fun tidbit.

> For what it's worth, data.table is my favourite to use and I believe it has the…

So I talk about COM, and you move the goal posts about DirectX in particular, that only implements a subset of COM, which requires writing wrappers by hand, including in .NET, hence Managed DirectX, XNA and SharpDX projects.

Apparently you missed the news regarding PWAs in Windows and its support for native UWP. Again, WinJS got dropped, because it was tied to the Windows 8 programming model, which got improved on Windows 8.1, only to be replaced by UWP on Windows 10.

Win32 GDI actually has "we wish you were all using Win2D and DirectDraw" support on Windows 10.

Here are some examples of automating Excel with Python, http://pbpython.com/windows-com.html, hardly any different from using VBA.

You can easily export any pandas DataFrame to html using the to_html() method. To generate full webpage, you'll probably want a templating engine like Jinja2.

The best demo I've seen for generating a PDF report is on Practical Business Python[1]

Edit: I forgot to mention the new pandas Style[1] feature for generating some impressive looking html tables.

[1] http://pbpython.com/pdf-reports.html

[2] http://pandas.pydata.org/pandas-docs/stable/style.html

This guys has a lot of nice material, presented in a clear and approachable way (this week I was teaching Python and I was linking him a lot).

For example:

http://pbpython.com/visualization-tools-1.html

http://pbpython.com/web-scraping-mn-budget.html