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...