Introduction to Dask DataFrames
Replace pd.read_csv and pd.DataFrame with dask equivalents, call compute() to trigger execution, and profile task graphs.
What Is Dask?
Dask is a parallel computing library for Python that extends NumPy and Pandas to datasets larger than RAM. Its dask.dataframe module provides a DataFrame API almost identical to Pandas, but instead of executing operations immediately, Dask builds a task graph and executes it lazily when you call .compute(). This allows Dask to parallelise work across multiple cores or even multiple machines with minimal code changes.
Installing and Importing Dask
Dask is installed with pip install dask[dataframe]. The import convention is import dask.dataframe as dd. Under the hood, a Dask DataFrame is partitioned into many smaller Pandas DataFrames, each processed independently. Operations on the Dask DataFrame create a lazy task graph — nothing runs until .compute() is called. This separation between describing and executing computation is the key insight of Dask.
import dask.dataframe as dd
# Read a large CSV — returns a Dask DataFrame immediately (no data loaded yet)
ddf = dd.read_csv('large_sales.csv')
print(type(ddf)) # dask.dataframe.core.DataFrame
print(ddf.columns.tolist())
print(ddf.dtypes)All lessons in this course
- Streaming CSV with chunksize
- Incremental Aggregation Across Chunks
- Introduction to Dask DataFrames
- Parquet: Fast Columnar Storage