elusion 8.2.0

Elusion is a modern DataFrame / Data Engineering / Data Analysis library that combines the familiarity of DataFrame operations (like those in PySpark, Pandas, and Polars) with the power of SQL query building. It provides flexible query construction without enforcing strict operation ordering, enabling developers to write intuitive and maintainable data transformations.
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use crate::prelude::*;

/// Extract a Value from a DataFrame based on column name and row index
pub async fn extract_value_from_df(df: &CustomDataFrame, column_name: &str, row_index: usize) -> ElusionResult<String>{

    let ctx = SessionContext::new();

    let batches = df.df.clone().collect().await 
        .map_err(|e| ElusionError::InvalidOperation { 
            operation: "Data Colleciton".to_string(), 
            reason: format!("Failed to collect DataFrame: {}", e), 
            suggestion: "💡 Check if DataFrame contains valid data".to_string() 
        })?;

    let schema = df.df.schema();
    let mem_table = MemTable::try_new(schema.clone().into(), vec![batches])
        .map_err(|e| ElusionError::SchemaError { 
            message: format!("Failed to create in-memory table: {}", e), 
            schema: Some(schema.to_string()), 
            suggestion: "💡 Verify schema compatibility and data types".to_string()
        })?;

    ctx.register_table("temp_extract", Arc::new(mem_table))
        .map_err(|e| ElusionError::InvalidOperation { 
            operation: "Table Registration".to_string(), 
            reason: format!("Failed to register Table: {}", e), 
            suggestion: "💡 Check if table is unique or valid".to_string() 
        })?;

    let value_df = ctx.sql(&format!("SELECT\"{}\" FROM temp_extract LIMIT 1 OFFSET {}", column_name, row_index)).await
        .map_err(|e| ElusionError::InvalidOperation { 
            operation: "SQL Execution".to_string(), 
            reason: format!("Failed to Execute SQL: {}", e), 
            suggestion: "💡 Verify column name exists in DataFrame".to_string() 
        })?;

    let batches = value_df.collect().await
        .map_err(|e| ElusionError::InvalidOperation { 
            operation: "Result Collection".to_string(), 
            reason: format!("Failed to collect Result: {}", e), 
            suggestion: "💡 Check if Query returns valid data".to_string() 
        })?;

    if batches.is_empty() || batches[0].num_rows() == 0 {
        return Err(ElusionError::Custom(format!("No data found for column '{}' at row {}", column_name, row_index)));
    }

    let col = batches[0].column(0);
    let value = match col.data_type(){
        ArrowDataType::Utf8=>{
            let array = col.as_any().downcast_ref::<StringArray>()
                .ok_or_else(|| ElusionError::Custom("Failed to downcast to StringArray".to_string()))?;

            if array.is_null(0){
                "".to_string()
            } else {
                array.value(0).to_string()
            }
        },
        _ => {
            format!("{:?}", col.as_ref())
        }
    };

    Ok(value)
}