How to add Column names in a Polars DataFrame while using CsvReader - rust

Question

I can read a csv file which does not have column headers in the file. With the following code using polars in rust:
use polars::prelude::*;
fn read_wine_data() -> Result<DataFrame> {
let file = "datastore/wine.data";
CsvReader::from_path(file)?
.has_header(false)
.finish()
}
fn main() {
let df = read_wine_data();
match df {
Ok(content) => println!("{:?}", content.head(Some(10))),
Err(error) => panic!("Problem reading file: {:?}", error)
}
}
But now I want to add column names into the dataframe while reading or after reading, how can I add the columns names. Here is a column name vector:
let COLUMN_NAMES = vec![
"Class label", "Alcohol",
"Malic acid", "Ash",
"Alcalinity of ash", "Magnesium",
"Total phenols", "Flavanoids",
"Nonflavanoid phenols",
"Proanthocyanins",
"Color intensity", "Hue",
"OD280/OD315 of diluted wines",
"Proline"
];
How can I add these names to the dataframe. The data can be downloaded with the following code:
wget https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

Answer 1

This seemed to work, by creating a schema object and passing it in with the with_schema method on the CsvReader:
use polars::prelude::*;
use polars::datatypes::DataType;
fn read_wine_data() -> Result<DataFrame> {
let file = "datastore/wine.data";
let mut schema: Schema = Schema::new();
schema.with_column("wine".to_string(), DataType::Float32);
CsvReader::from_path(file)?
.has_header(false)
.with_schema(&schema)
.finish()
}
fn main() {
let df = read_wine_data();
match df {
Ok(content) => println!("{:?}", content.head(Some(10))),
Err(error) => panic!("Problem reading file: {:?}", error)
}
}
Granted I don't know what the column names should be, but this is the output I got when adding the one column:
shape: (10, 1)
┌──────┐
│ wine │
│ --- │
│ f32 │
╞══════╡
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ ... │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
├╌╌╌╌╌╌┤
│ 1.0 │
└──────┘

Answer 2

Here is the full solution working for me:
fn read_csv_into_df(path: PathBuf) -> Result<DataFrame> {
let schema = Schema::from(vec![
Field::new("class_label", Int64),
Field::new("alcohol", Float64),
Field::new("malic_acid", Float64),
Field::new("ash", Float64),
Field::new("alcalinity_of_ash", Float64),
Field::new("magnesium", Float64),
Field::new("total_phenols", Float64),
Field::new("flavanoids", Float64),
Field::new("nonflavanoid_phenols", Float64),
Field::new("color_intensity", Float64),
Field::new("hue", Float64),
Field::new("od280/od315_of_diluted_wines", Float64),
Field::new("proline", Float64),
]);
CsvReader::from_path(path)?.has_header(false).with_schema(&schema).finish()
}
I had Use Field and types for each field to create a schema then use the schema in CsvReader to read the data.