Update guide to UDFs with notes about Series.applymap deprecation and related changes

[brandon-b-miller]

This PR updates our guide to UDFs notebook in the following ways:

• Notes deprecation of cudf.Series.applymap
• reorder sections such that cudf.Series.apply and cudf.DataFrame.apply are encountered before applymap, apply_rows and apply_chunks
• Adds recommendations about use cases for each of the above APIs to distinguish them
• Minor updates all over to better describe current state

EDIT: decided to just remove the docs for applymap at this point.

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[brandon-b-miller]

rerun tests

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Codecov Report

Merging #10607 (6e1920b) into branch-22.06 (6675c75) will increase coverage by 0.02%.
The diff coverage is n/a.

@@               Coverage Diff                @@
##           branch-22.06   #10607      +/-   ##
================================================
+ Coverage         86.31%   86.34%   +0.02%     
================================================
  Files               140      140              
  Lines             22255    22280      +25     
================================================
+ Hits              19209    19237      +28     
+ Misses             3046     3043       -3     

Impacted Files	Coverage Δ
python/cudf/cudf/core/indexed_frame.py	91.77% <0.00%> (-0.87%)	⬇️
python/cudf/cudf/core/series.py	95.28% <0.00%> (-0.01%)	⬇️
python/cudf/cudf/core/column/string.py	89.10% <0.00%> (ø)
python/cudf/cudf/core/column/column.py	89.45% <0.00%> (+0.10%)	⬆️
python/cudf/cudf/core/column/lists.py	90.62% <0.00%> (+0.12%)	⬆️
python/cudf/cudf/core/column/numerical.py	96.17% <0.00%> (+0.29%)	⬆️
python/cudf/cudf/core/scalar.py	89.32% <0.00%> (+0.56%)	⬆️
python/cudf/cudf/core/frame.py	94.75% <0.00%> (+1.02%)	⬆️

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[shwina]

While it's good to do this in tests, maybe we don't want to compare the invocations of apply() with Pandas -- unless there's a difference we want to call out.

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[shwina]

Functions used with cudf.Series.apply and cudf.DataFrame.apply can be expected to handle nulls using the same rules as the rest of cuDF. In most cases this translates to nulls propagating through unary and binary operations and yielding more nulls.

I think we could simplify while also being a bit more explicit here:

The null value NA an propagates through unary and binary operations. Thus, NA + 1,abs(NA), and NA == NA all return NA. To make this concrete, let's look at the same example from above, this time using nullable data:

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[shwina]

In other parts of this notebook, we use the second person "you", whereas here we use "the user". Let's be consistent -- I have a strong preference for the second person "you".

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[shwina]

Many problems in data science and engineering are well studied and there exist known parallel algorithms for making some desired transformation to some data. Many have corresponding CUDA solutions that may not exist as column level API in cuDF. To expose the ability to use these custom kernels, cuDF supports directly using custom cuda kernels written using numba on cuDF Series objects. In short, this means that if a user has knowledge of how to write a CUDA kernel in numba, they may simply pass cuDF Series objects to that kernel as if they were numba device arrays. Let's look at a basic example of how to do this.

Two observations here:

• We should avoid categorizing users as coming from a data science or engineering background
• It takes a bit of reading before I understand what this section is about.

I think we should be more direct here; something along the lines of:

In addition to the Series.apply() method for performing custom operations, you can also pass Series objects directly into [CUDA kernels written with Numba](https://numba.pydata.org/numba-doc/latest/cuda/kernels.html).

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[shwina]

>we just need to pre-allocate an output array and leverage the forall method mentioned above

I can't remember where I first learned about avoiding the 'J' word just - but it was probably here

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[shwina]

This example is adapted from cuDF's API documentation.

Orthogonal to this PR, but the link here is broken. Ditto below with apply_grouped.

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[brandon-b-miller]

I fixed this link but actually I think the docs for apply_grouped are missing as I dont see them anywhere in our stable docs. Looking into this separately.

[brandon-b-miller]

@gpucibot merge