DOC: Add scaling to large datasets section
Closes https://github.com/pandas-dev/pandas/issues/28315
Thanks for addressing comments. Generally looks good and I think a welcome addition to docs And thank you for the review :)

FYI, this adds ~30s to the doc build on my machine (haven't checked the slowdown on CI, but presumably its longer). Is that unacceptable? I can make the examples smaller if needed. FYI, rendered version:

![FireShot Capture 007 - Scaling to large datasets — pandas 0 25 0+418 ga7fb97f44a dirty documen_ - ](https://user-images.githubusercontent.com/1312546/65462754-0f615b80-de1c-11e9-88c1-a939e6fb8aab.png)
 Any other thoughts here? > Not for this PR, but if we have an additional level of navigation, we could put this together with the current enhancingperf.rst in a "performance" section?

I slightly prefer smaller doc pages that cross reference eachother.

I've removed the section comparing the perf of read_csv and read_parquet. That muddies the purpose of this document, since it's talking about speed, while the rest of the document focuses primarily on memory usage.

I've updated the "subset of columns" example to use the `%memit` magic from memory_profiler. flake8-rst didn't like jupyter magics, so I've had to remove it. > > Not for this PR, but if we have an additional level of navigation, we could put this together with the current enhancingperf.rst in a "performance" section?
>
> I slightly prefer smaller doc pages that cross reference eachother.

Sorry, I was not very clear :) I didn't mean to suggest to put them in a single file. Because I also prefer smaller doc pages, I want to split some of the existing ones, and create an extra level of hierarchy in the user guide. And in that light, this one could fit together with the existing enhancingperf (which could be splitted for the cython stuff and the querying) in a general "Performance" section (that has multiple sub pages). But that's for the future, not now, so nothing to care about for this PR.

> I've removed the section comparing the perf of read_csv and read_parquet. That muddies the purpose of this document, since it's talking about speed, while the rest of the document focuses primarily on memory usage.

I actually think that section could still be useful. read_csv vs read_parquet is not only for speed, also memory wise I would think that read_parquet is quite a bit better? (I heard about read_csv needing quite some more memory than the original file or final dataframe) So maybe if using `%memit` instead of `%timeit` would illustrate that? lgtm, needs a rebase though. All green here. Thanks. Not that it needed to be in this PR (could be a follow-up), but an opinion on the usefulness of what I remarked in https://github.com/pandas-dev/pandas/pull/28577#issuecomment-535467605 (the second paragraph on reading files)