log10transform on series

operations_on_series2.rs

// Return a series
fn numb_to_log(in_df: &mut DataFrame) -> PolarResult<Series>{
    //  unwrap to have Series, then .f64().uwrap() to retrieve a chunked array
    let to_log10_column = in_df.drop_in_place("sepal.length").unwrap().rename("log10.sepal.length")
                                                             .f64().unwrap() // create chunked array
                                                             .cast::<Float64Type>() // here we have apply
                                                             .unwrap() // unwrap because we have Result<>
                                                             .apply(|s| s.log10());
    
    let series10 = to_log10_column.into_series(); // reconvert into a series
    
    // return the column
    println!("{}", series10);
    Ok(series10)
}

pub fn deal_with_apply<P: AsRef<Path>>(path: P) -> () {
    /* Apply is one of the key functions in pandas*/
    let mut df = read_csv(&path).unwrap();
    // apply a closure 
    println!("Add 1 to first column");
    df.apply_at_idx(0, |s| s+1);
    println!("{}", df.head(Some(5)));
    // compute the log transform of a column and learn to play with series and chunked arrays
    let log10_series = numb_to_log(&mut df);
    // insert the log10-column 
    println!(" log 10 of sepal length");
    df.with_column(log10_series.unwrap());
    println!("{}", df.head(Some(5)));
    // let's see how to perform the log10 transform directly from teh dataframe
    df.apply_at_idx(0, |s| s.f64().unwrap().apply(|t| t.log10()));
}