Add util function to drop rows with NaN values in the target

[angela97lin]

Closes #335 by introducing a util function that will drop any rows with NaN values in y.

Note: this is updated to only drop rows in X and y for every NaN row in y. I figured it wouldn't always make the most sense to drop rows in X for any NaN value that might appear in X since there could be so many, and that's what imputation is for. So this util function instead checks for NaN values in y and drops the corresponding rows in X :)

[codecov]

Codecov Report

Merging #487 into master will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #487      +/-   ##
==========================================
+ Coverage   98.67%   98.68%   +0.01%     
==========================================
  Files         113      114       +1     
  Lines        3985     4026      +41     
==========================================
+ Hits         3932     3973      +41     
  Misses         53       53              

Impacted Files	Coverage Δ
evalml/pipelines/components/__init__.py	100.00% <ø> (ø)
evalml/preprocessing/utils.py	100.00% <100.00%> (ø)
...lml/tests/preprocessing_tests/test_drop_na_rows.py	100.00% <100.00%> (ø)

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[dsherry]

@angela97lin this is a valuable addition, thanks for doing it! I'm sure we'll end up having automl add this to pipelines in the future if the data checks say there are NaN values.

I've added #337 to the data checks project -- see ticket for further discussion. Let's update this PR to not close #337.

[jeremyliweishih]

Looks good! Just have a concern that there will be mismatched sizes between X and y when used with a pipeline.

[dsherry]

Left a couple suggestions. Thanks for adding this, will be good!

[dsherry]

Looks great, 🚢 !