Using Python's glob Module with Dask in Python 2026

Using Python's glob Module with Dask in Python 2026 – Best Practices

Python's built-in glob module is very useful when you need more control over which files to read with Dask. While Dask supports simple wildcards directly, combining it with glob.glob() gives you greater flexibility for complex file selection patterns.

1. Basic Usage with glob

import glob
import dask.dataframe as dd

# Get list of files using glob
csv_files = glob.glob("data/sales_*.csv")
parquet_files = glob.glob("data/year=2025/month=*/part-*.parquet")

print(f"Found {len(csv_files)} CSV files")
print(f"Found {len(parquet_files)} Parquet files")

# Read with Dask
if csv_files:
    df = dd.read_csv(csv_files, blocksize="64MB")

if parquet_files:
    ddf = dd.read_parquet(parquet_files)

2. Advanced Glob Patterns

import glob

# Complex patterns
log_files = glob.glob("logs/2025/**/*.log", recursive=True)        # recursive search
json_files = glob.glob("data/[A-Z]*/**/*.jsonl", recursive=True)   # specific folder patterns

# Filter files by size or date if needed
large_files = [f for f in glob.glob("data/*.csv") if os.path.getsize(f) > 100_000_000]

df = dd.read_csv(large_files, blocksize="128MB")

3. Best Practices in 2026

• Use glob.glob() when you need complex file selection logic
• Use recursive=True for searching subdirectories
• Combine glob with list comprehensions for custom filtering (file size, date, etc.)
• Pass the resulting file list directly to dd.read_csv() or dd.read_parquet()
• Prefer Dask’s built-in wildcards for simple cases — use glob only when you need more control
• Monitor the Dask Dashboard to see how the selected files are distributed across partitions

Conclusion

Python's glob module is a valuable companion when working with Dask. While Dask supports simple wildcards natively, using glob.glob() gives you fine-grained control for complex file selection scenarios. In 2026, combining glob with Dask is a common and powerful pattern for processing large collections of files efficiently.

Next steps:

• Try using glob.glob() to select specific subsets of files in your Dask workflows