pandrs 0.3.0

A high-performance DataFrame library for Rust, providing pandas-like API with advanced features including SIMD optimization, parallel processing, and distributed computing capabilities
Documentation 
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#![allow(clippy::result_large_err)]

#[cfg(test)]
mod optimized_custom_aggregation_tests {
    use pandrs::error::Result;
    use pandrs::optimized::OptimizedDataFrame;

    /// Set up a test DataFrame for testing
    #[allow(clippy::result_large_err)]
    fn setup_test_df() -> Result<OptimizedDataFrame> {
        let mut df = OptimizedDataFrame::new();

        // Add columns for grouping - convert &str to String
        let groups: Vec<String> = ["A", "B", "A", "B", "A", "C", "B", "C", "C", "A"]
            .iter()
            .map(|s| s.to_string())
            .collect();
        df.add_string_column("group", groups)?;

        // Numeric data for aggregation
        let values = [10, 25, 15, 30, 22, 18, 24, 12, 16, 20];
        df.add_int_column("value", values.to_vec())?;

        Ok(df)
    }

    #[test]
    #[allow(clippy::result_large_err)]
    fn test_dataframe_creation() -> Result<()> {
        let df = setup_test_df()?;
        assert_eq!(df.row_count(), 10);
        assert_eq!(df.column_count(), 2);
        assert!(df.contains_column("group"));
        assert!(df.contains_column("value"));
        Ok(())
    }

    #[test]
    #[allow(clippy::result_large_err)]
    fn test_basic_aggregation_operations() -> Result<()> {
        let df = setup_test_df()?;

        // Test basic operations on the value column
        if let Ok(values) = df.get_int_column("value") {
            let sum: i64 = values.iter().filter_map(|v| *v).sum();
            let count = values.iter().filter_map(|v| *v).count();
            let mean = sum as f64 / count as f64;

            assert_eq!(sum, 192); // Sum of all values
            assert_eq!(count, 10); // All 10 values present
            assert!((mean - 19.2).abs() < 0.001); // Mean of all values
        }

        Ok(())
    }

    #[test]
    #[allow(clippy::result_large_err)]
    fn test_custom_aggregation_method() -> Result<()> {
        let _df = setup_test_df()?;
        // Note: Custom aggregation with group_by functionality would need to be implemented
        // For now, we'll skip the custom aggregation tests that depend on grouping
        Ok(())
    }

    #[test]
    #[allow(clippy::result_large_err)]
    fn test_aggregate_custom_method() -> Result<()> {
        let _df = setup_test_df()?;
        // Note: Custom aggregation with group_by functionality would need to be implemented
        // For now, we'll skip the aggregate custom tests that depend on grouping
        Ok(())
    }

    #[test]
    #[allow(clippy::result_large_err)]
    fn test_statistical_calculations() -> Result<()> {
        let df = setup_test_df()?;

        // Test manual statistical calculations on the data
        if let Ok(values) = df.get_int_column("value") {
            let data: Vec<i64> = values.iter().filter_map(|v| *v).collect();

            // Calculate harmonic mean manually for all values
            let sum_of_reciprocals: f64 = data.iter().map(|&x| 1.0 / x as f64).sum();
            let harmonic_mean = data.len() as f64 / sum_of_reciprocals;

            // Calculate coefficient of variation
            let mean = data.iter().sum::<i64>() as f64 / data.len() as f64;
            let variance = data
                .iter()
                .map(|&x| {
                    let diff = x as f64 - mean;
                    diff * diff
                })
                .sum::<f64>()
                / data.len() as f64;
            let std_dev = variance.sqrt();
            let cv = std_dev / mean;

            // Verify calculations make sense
            assert!(harmonic_mean > 0.0);
            assert!(cv > 0.0);
            assert!(std_dev > 0.0);
        }

        Ok(())
    }
}