Aggregating with Generators and Dask in Python 2026 – Best Practices
Generators are excellent for memory-efficient data processing, but aggregation (sum, mean, count, groupby, etc.) requires special handling when combined with Dask. In 2026, the most effective pattern is to use generators to produce filtered or transformed data lazily, then feed them into Dask for parallel aggregation.
TL;DR — Recommended Pattern
- Use a generator to yield filtered/transformed records
- Convert the generator stream into a Dask Bag or Dask DataFrame
- Perform aggregation using Dask’s parallel operations
- Compute only the final aggregated result
1. Generator → Dask Bag → Aggregation
def sales_generator(file_path):
"""Memory-efficient generator that yields one record at a time."""
with open(file_path, 'r') as f:
next(f) # skip header
for line in f:
row = line.strip().split(',')
try:
amount = float(row[3])
if amount > 500:
yield {
"region": row[4],
"amount": amount,
"category": row[5]
}
except:
continue
import dask.bag as db
# Convert generator to Dask Bag
bag = db.from_sequence(sales_generator("large_sales.log"), npartitions=64)
# Parallel aggregation
result = (
bag.groupby(lambda x: x["region"])
.map(lambda group: {
"region": group[0],
"total_sales": sum(item["amount"] for item in group[1]),
"transaction_count": len(group[1]),
"avg_sale": sum(item["amount"] for item in group[1]) / len(group[1])
})
.compute()
)
print(result)
2. Generator → Dask DataFrame (More Structured)
import dask.dataframe as dd
from dask import delayed
import pandas as pd
def chunk_generator():
for chunk in pd.read_csv("large_sales.csv", chunksize=50_000):
filtered = chunk[chunk["amount"] > 500]
if not filtered.empty:
yield filtered
# Create delayed chunks from generator
delayed_chunks = [delayed(chunk) for chunk in chunk_generator()]
# Build Dask DataFrame
ddf = dd.from_delayed(delayed_chunks, meta=pd.DataFrame({
"region": "string",
"amount": "float32",
"category": "string"
}))
# Efficient parallel aggregation
summary = (
ddf.groupby(["region", "category"])
.agg({
"amount": ["sum", "mean", "count"]
})
.compute()
)
print(summary)
3. Best Practices for Aggregating with Generators in 2026
- Use generators to filter data **before** it enters Dask to save memory
- Prefer Dask Bag for simple key-value or unstructured data
- Use Dask DataFrame when you need proper column names and dtypes
- Always provide a proper `meta` when using `dd.from_delayed()`
- After aggregation, the result is small enough to `.compute()` safely
- Monitor memory usage in the Dask Dashboard during development
Conclusion
Aggregating with generators combined with Dask is a powerful, memory-efficient pattern for processing massive or streaming datasets. In 2026, the recommended workflow is: **generator for lazy filtering → Dask Bag or DataFrame for parallel aggregation → compute only the final small result**. This approach gives you clean code, excellent scalability, and very low memory footprint.
Next steps:
- Try converting one of your current aggregation loops into a generator + Dask pattern