Aggregating while Ignoring NaNs with Dask in Python 2026 – Best Practices
When working with real-world scientific or sensor data, missing values (NaNs) are common. Dask provides convenient methods to perform aggregations while ignoring NaNs.
Example
import dask.array as da
arr = da.random.random((1000000, 100), chunks=(100000, 100))
arr[::1000, ::10] = da.nan # introduce some NaNs
# Aggregate while ignoring NaNs
mean_values = da.nanmean(arr, axis=0).compute()
sum_values = da.nansum(arr, axis=0).compute()
print("Mean ignoring NaNs:", mean_values)
Best Practices
- Use
nanmean,nansum,nanstd, etc. instead of regular aggregations - Be aware that NaN-aware functions can be slightly slower
- Consider filling NaNs with a sensible value before aggregation when appropriate
Conclusion
Dask’s NaN-aware aggregation functions make it easy to handle missing data in large arrays without manual preprocessing.
Next steps:
- Try using NaN-aware aggregations on your real datasets