</>
pythonintermediate

Dask Parallel DataFrame Processing

Process datasets larger than RAM using Dask's parallel, lazy DataFrame API.

python
import dask.dataframe as dd

ddf = dd.read_csv('s3://bucket/data/*.csv')

result = (
    ddf[ddf['value'] > 0]
    .groupby('category')['value']
    .agg(['mean', 'sum', 'count'])
    .compute()
)
print(result)

Use Cases

  • out-of-core processing
  • distributed analytics
  • large file ingestion

Tags

#dask#parallel#big-data#pandas

Related Snippets

Similar patterns you can reuse in the same workflow.

pythonbeginner

Pandas DataFrame Transformations

Common pandas DataFrame transformations including column operations, type casting, and string methods.

Best for: Cleaning raw data files for analysis

#pandas#dataframe
pythonbeginner

Pandas DataFrame Filtering Techniques

Filter DataFrames using boolean masks, query syntax, isin, between, and string matching methods.

Best for: Extracting subsets of data for reporting

#pandas#filtering
pythonintermediate

Pandas GroupBy Aggregation Examples

GroupBy operations with multiple aggregations, named aggregations, and transform for DataFrame analysis.

Best for: Sales reporting by region and time period

#pandas#groupby
pythonadvanced

Spark SQL Query Example

PySpark DataFrame operations with SQL queries, window functions, and aggregations for big data.

Best for: Processing large-scale datasets with Spark

#spark#pyspark