How can I use Dask with Pandas for large datasets?

How can I use Dask with Pandas for large datasets?

You can use Dask to parallelize your Pandas operations, which is great for handling large datasets. First, install Dask using pip install dask. Then, you can create a Dask DataFrame from a Pandas DataFrame or directly from a CSV file.

Can you show me an example of creating a Dask DataFrame?

Sure! Here's a simple example:

import dask.dataframe as dd

df = dd.read_csv('large_dataset.csv')

This reads a CSV file into a Dask DataFrame, allowing you to work with it in parallel.

What operations can I perform on a Dask DataFrame?

You can perform many of the same operations as with Pandas, like filtering, grouping, and aggregating. Just remember to call .compute() to execute the operations.

Are there any limitations I should be aware of?

Yes, Dask DataFrames don't support all Pandas operations, especially those that require a single machine's memory. It's best for operations that can be parallelized.