charton 0.4.1

A high-level, layered charting system for Rust, designed for Polars-first data workflows and multi-backend rendering.
Documentation 
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use crate::error::ChartonError;
use polars::prelude::*;
use time::OffsetDateTime;

// unemployment dataset: A sub set of country's unemployment rate from past 31 years from https://www.kaggle.com/datasets/pantanjali/unemployment-dataset
pub fn get_data() -> Result<DataFrame, ChartonError> {
    let country = [
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
        "Belarus",
        "Ireland",
        "Moldova",
        "Malta",
        "Puerto Rico",
        "Senegal",
        "Slovenia",
    ];
    let year = [
        2011, 2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2013,
        2013, 2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015,
        2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017,
        2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019,
        2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021,
        2021, 2021,
    ];
    let unemployment = [
        6.17, 15.35, 6.68, 6.38, 15.7, 10.36, 8.17, 6.05, 15.45, 5.58, 6.2, 14.5, 9.44, 8.84, 6.01,
        13.73, 5.1, 6.11, 14.3, 8.58, 10.1, 5.99, 11.86, 3.73, 5.72, 13.9, 7.65, 9.67, 5.84, 9.91,
        4.7, 5.38, 12.0, 6.76, 8.96, 5.84, 8.37, 4.02, 4.69, 11.8, 4.46, 8.0, 5.65, 6.71, 4.1, 4.0,
        10.8, 3.69, 6.56, 4.76, 5.74, 4.11, 3.66, 9.2, 3.28, 5.11, 4.16, 4.95, 5.1, 3.62, 8.3,
        2.86, 4.45, 4.77, 5.62, 3.82, 4.26, 8.89, 3.62, 4.97, 4.74, 6.63, 3.96, 3.5, 8.27, 3.72,
        4.42,
    ];

    // Convert i32 years into nanosecond timestamps (i64)
    let year_nanos: Vec<i64> = year
        .iter()
        .map(|&y| {
            // Use the 'time' crate to construct a date and retrieve the nanosecond timestamp.
            // We assume each year starts at midnight on January 1st (00:00:00).
            OffsetDateTime::from_unix_timestamp(0)
                .unwrap()
                .replace_year(y)
                .unwrap()
                .replace_month(time::Month::January)
                .unwrap()
                .replace_day(1)
                .unwrap()
                .unix_timestamp_nanos() as i64
        })
        .collect();

    // Construct the DataFrame using the df! macro
    let mut df = df![
        "Country" => country,
        "Year" => year_nanos,
        "Unemployment rate (%)" => unemployment
    ]?;

    // Cast the "Year" column from Int64 to Datetime(Nanoseconds).
    // This allows 'interpret_semantic_type' to recognize it as a Temporal type.
    df.with_column(
        df.column("Year")?
            .cast(&DataType::Datetime(TimeUnit::Nanoseconds, None))?
            .with_name("Year".into()),
    )?;

    Ok(df)
}