How do I use Dask DataFrames for large datasets?

How do I use Dask DataFrames for large datasets?

You can use Dask DataFrames to handle large datasets that don't fit into memory. First, install Dask using pip install dask. Then, you can create a Dask DataFrame from a CSV file like this: import dask.dataframe as dd; df = dd.read_csv('large_file.csv').

Can I perform operations like filtering and grouping on Dask DataFrames?

Yes, you can perform operations like filtering and grouping. For example, to filter rows, you can use df[df['column_name'] > value]. For grouping, use df.groupby('column_name').mean().

How do I compute the results after performing operations?

To compute the results, you can call the .compute() method. For example, result = df[df['column_name'] > value].compute() will give you the filtered DataFrame as a Pandas DataFrame.

What are the advantages of using Dask over Pandas?

Dask allows you to work with larger-than-memory datasets, parallelizes operations, and integrates seamlessly with existing Pandas code, making it easier to scale your data processing.