Module: aprender::data

Public module of the aprender-core crate.

Source

crates/aprender-core/src/data.rs or directory.

Example

use aprender::data::{DataFrame, ColumnStats};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::data provides a minimal columnar DataFrame for ML pipelines — named Vector<f32> columns, schema-aware selection, row / matrix extraction, and a describe summary that returns ColumnStats per column. The companion submodules cover PII redaction (data::pii), schema evolution (data::evolve), and quality filtering (data::quality_filter) — the basics needed to clean and validate datasets before training.

Key types

TypeDescription
DataFrameNamed-column container of Vector<f32>s. Methods: column, select, row, to_matrix, iter_columns, add_column, drop_column, describe.
ColumnStatsSummary statistics per column (mean, std, min, max, etc.).
data::piiPII detection / redaction utilities.
data::evolveSchema evolution between dataset versions.
data::quality_filterQuality-based row filtering (NaN, Inf, range checks).

Usage patterns

Pattern 1: Build a DataFrame and convert to Matrix

use aprender::data::DataFrame;
use aprender::primitives::Vector;

let df = DataFrame::new(vec![
    ("age".to_string(), Vector::from_slice(&[25.0, 30.0, 35.0, 40.0])),
    ("income".to_string(), Vector::from_slice(&[40_000.0, 55_000.0, 65_000.0, 80_000.0])),
    ("score".to_string(), Vector::from_slice(&[0.7, 0.8, 0.85, 0.9])),
]).expect("valid DataFrame");

assert_eq!(df.shape(), (4, 3));
let names = df.column_names();
println!("columns: {:?}", names);

// Pull a `Matrix<f32>` ready for an Estimator.
let x = df.to_matrix();
assert_eq!(x.shape(), (4, 3));

Pattern 2: Select columns and summarise

use aprender::data::DataFrame;
use aprender::primitives::Vector;

let df = DataFrame::new(vec![
    ("a".to_string(), Vector::from_slice(&[1.0, 2.0, 3.0, 4.0])),
    ("b".to_string(), Vector::from_slice(&[5.0, 6.0, 7.0, 8.0])),
    ("c".to_string(), Vector::from_slice(&[9.0, 10.0, 11.0, 12.0])),
]).expect("valid DataFrame");

let subset = df.select(&["a", "c"]).expect("select existing cols");
assert_eq!(subset.n_cols(), 2);

for stats in df.describe() {
    println!("column stats: {:?}", stats);
}

See also

  • primitives — Matrix and Vector are the underlying storage
  • preprocessing — apply StandardScaler / encoders to a DataFrame
  • loading — read CSV / Parquet / JSON into a DataFrame
  • mining — pattern mining on transactional / categorical data

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.