Greptile Overview

Greptile Summary

Added variance aggregation function (var()) with configurable degrees of freedom (ddof) parameter, resolving #4705. The implementation supports both sample variance (ddof=1, default) and population variance (ddof=0).

Key Changes:

• Added Expr.var(ddof) method to Python API following existing patterns
• Created corresponding daft.functions.var() function per custom rule 8b8ae412
• Implemented distributed variance calculation using algebraic decomposition: var = (E(X²) - E(X)²) * n/(n-ddof)
• Added SQL support: var, var_samp, var_pop, variance functions
• Comprehensive test coverage including edge cases (single values, nulls, grouped operations, multiple partitions)

Implementation Notes:

• Single partition: Uses direct calculation via calculate_variance() in stats utils
• Multiple partitions: Decomposes into sum(X), sum(X²), and count(X) for distributed aggregation
• Properly handles edge case where n ≤ ddof by returning null
• All variance results cast to Float64 type

Confidence Score: 4/5

• Safe to merge with one minor style issue regarding inline imports
• Implementation is mathematically sound and follows established patterns from stddev. Comprehensive test coverage validates correctness across edge cases. One style comment about inline imports (custom rule violation present throughout codebase).
• No files require special attention - all changes follow existing patterns

Important Files Changed

Sequence Diagram

sequenceDiagram
    participant User
    participant Python API
    participant DSL Layer
    participant Local Plan
    participant Core Ops
    participant Stats Utils

    User->>Python API: df.agg(col("x").var(ddof=1))
    Python API->>DSL Layer: expr.var(ddof)
    DSL Layer->>DSL Layer: Create AggExpr::Var(expr, ddof)
    
    alt Single Partition
        DSL Layer->>Core Ops: Series.var(groups=None, ddof)
        Core Ops->>Core Ops: Cast to Float64
        Core Ops->>Stats Utils: calculate_stats(array)
        Stats Utils-->>Core Ops: Stats{sum, count, mean}
        Core Ops->>Stats Utils: calculate_variance(stats, values, ddof)
        Note over Stats Utils: variance = sum_of_squares / (n - ddof)
        Stats Utils-->>Core Ops: variance value
        Core Ops-->>User: Result
    else Multiple Partitions
        DSL Layer->>Local Plan: populate_aggregation_stages
        Note over Local Plan: First Stage (per partition)
        Local Plan->>Core Ops: Sum(X), Sum(X²), Count(X)
        Core Ops-->>Local Plan: Partial results
        Note over Local Plan: Second Stage (combine partitions)
        Local Plan->>Local Plan: Sum(sums), Sum(sq_sums), Sum(counts)
        Note over Local Plan: Final Stage
        Local Plan->>Local Plan: pop_var = (sq_sum/n) - (sum/n)²
        Local Plan->>Local Plan: sample_var = pop_var * n/(n-ddof)
        Local Plan->>Local Plan: if n <= ddof then null else sample_var
        Local Plan-->>User: Result
    end

Codecov Report

❌ Patch coverage is 10.18519% with 97 lines in your changes missing coverage. Please review.
✅ Project coverage is 43.31%. Comparing base (7bc6c81) to head (e28af4e).

@@            Coverage Diff             @@
##             main    #6105      +/-   ##
==========================================
- Coverage   43.35%   43.31%   -0.05%     
==========================================
  Files         917      918       +1     
  Lines      113004   113102      +98     
==========================================
- Hits        48996    48987       -9     
- Misses      64008    64115     +107     

@aaron-ang if you don't mind, in the future could you ask to work on the issue? I ask because someone else was already assigned to the issue. But otherwise, great work, will TAL!

@aaron-ang if you don't mind, in the future could you ask to work on the issue? I ask because someone else was already assigned to the issue. But otherwise, great work, will TAL!

ok, I will choose to work on unassigned issues in the future.