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As you can see, @transform does not perform column selection, and the :grade_100 column is created as a new column and added to the right of our DataFrame.
macros also support begin ... end statements. For example, suppose that you are creating two columns in a @transform macro:
| name | grade | grade_100 | grade_5 |
|---|---|---|---|
| Sally | 1.0 | 10.0 | 0.5 |
| Bob | 5.0 | 50.0 | 2.5 |
| Alice | 8.5 | 85.0 | 4.25 |
| Hank | 4.0 | 40.0 | 2.0 |
| Bob | 9.5 | 95.0 | 4.75 |
| Sally | 9.5 | 95.0 | 4.75 |
| Hank | 6.0 | 60.0 | 3.0 |
We can also use other columns in our transformations, which makes DataFramesMeta.jl more appealing than DataFrames.jl due to the easier syntax.
First, let’s revisit the leftjoined from Chapter 4:
| name | grade_2020 | grade_2021 |
|---|---|---|
| Sally | 1.0 | 9.5 |
| Hank | 4.0 | 6.0 |
| Bob | 5.0 | missing |
| Alice | 8.5 | missing |
Additionally, we’ll replace the missing values with 5 (Section , also note the ! in in-place variant @rtransform!):
| name | grade_2020 | grade_2021 | mean_grades |
|---|---|---|---|
| Sally | 1.0 | 9.5 | 5.25 |
| Hank | 4.0 | 6.0 | 5.0 |
| Bob | 5.0 | 5 | 5.0 |
| Alice | 8.5 | 5 | 6.75 |
This is how you would perform it in :
As you can see, the case for easier syntax is not hard to argue for DataFramesMeta.jl.
