Aggregating with Delayed Functions in Dask

Aggregating with Delayed Functions in Dask – Python 2026 Best Practices

When you need custom aggregation logic that doesn’t fit neatly into Dask DataFrame’s built-in methods, you can use dask.delayed to create flexible, parallel aggregation pipelines. In 2026, this pattern is widely used for complex groupby operations, custom metrics, and multi-step aggregations on large datasets.

TL;DR — Recommended Pattern

• Wrap custom aggregation functions with @delayed
• Build the computation graph using loops or list comprehensions
• Use dd.from_delayed() or dask.compute() to execute
• Combine with Dask DataFrame for best performance when possible

1. Basic Aggregation with Delayed Functions

from dask import delayed
import dask.dataframe as dd
import pandas as pd

@delayed
def custom_aggregate(chunk):
    """Custom aggregation logic per chunk."""
    return {
        "total_amount": chunk["amount"].sum(),
        "unique_customers": chunk["customer_id"].nunique(),
        "max_amount": chunk["amount"].max(),
        "avg_duration": chunk["trip_duration"].mean()
    }

# Load data and create delayed tasks
df = dd.read_parquet("trips/year=2025/*.parquet")

# Apply custom aggregation to each partition
delayed_results = df.map_partitions(custom_aggregate).to_delayed()

# Combine results
final = delayed(sum)([d["total_amount"] for d in delayed_results])

print("Total amount across all trips:", final.compute())

2. Grouped Aggregation with Delayed Functions

@delayed
def aggregate_by_region(group):
    """Custom logic for each group."""
    return pd.Series({
        "region": group.name,
        "total_sales": group["amount"].sum(),
        "avg_sale": group["amount"].mean(),
        "transaction_count": len(group)
    })

# Group and apply delayed aggregation
grouped = df.groupby("region")
delayed_groups = [aggregate_by_region(group) for name, group in grouped]

# Compute all in parallel
results = dask.compute(*delayed_groups)

# Convert to DataFrame
final_df = pd.DataFrame(results)
print(final_df)

3. Best Practices for Aggregating with Delayed Functions in 2026

• Use @delayed only when built-in Dask methods are insufficient
• Keep delayed functions pure and stateless when possible
• Combine map_partitions() with delayed functions for chunk-level aggregation
• Use dask.compute(*list_of_delayed) to run many delayed tasks in parallel
• Visualize the task graph with .visualize() to understand dependencies
• For simple aggregations, prefer native Dask DataFrame methods (faster and more optimized)

Conclusion

Aggregating with delayed functions gives you maximum flexibility when Dask’s built-in methods are not enough. In 2026, the best approach is to use dask.delayed strategically for custom logic while leveraging Dask DataFrame’s optimized groupby and aggregation methods whenever possible. This hybrid strategy delivers both flexibility and high performance.

Next steps:

• Identify complex aggregations in your codebase and try wrapping them with @delayed
• Related articles: Parallel Programming with Dask in Python 2026 • Deferring Computation with `delayed` in Dask – Python 2026 Best Practices • Composing Functions with Dask in Python 2026