Julia comes with excellent facilities for reading and storing data out of the box. Given its focus on data science and scientific computing, support for tabular-file formats (CSV, TSV) is first class.
Let's extract some data from our initial dataset and use it to practice persistence and retrieval from various backends.
We can reference a section of a DataFrame by defining its bounds through the corresponding columns and rows. For example, we can define a new DataFrame composed only of the PetalLength and PetalWidth columns and the first three rows:
julia> iris[1:3, [:PetalLength, :PetalWidth]]
3×2 DataFrames.DataFrame
│ Row │ PetalLength │ PetalWidth │
├─────┼─────────────┼────────────┤
│ 1 │ 1.4 │ 0.2 │
│ 2 │ 1.4 │ 0.2 │
│ 3 │ 1.3 │ 0.2 │
The generic indexing notation is dataframe[rows, cols], where rows can be a number, a range, or an Array of boolean values where true indicates that the row should...