What is Dask DataFrame and how can I use it?

What is Dask DataFrame and how can I use it?

Dask DataFrame is a parallel computing library in Python that allows you to work with large datasets that don't fit into memory. It mimics the Pandas DataFrame API, making it easier to scale your data processing.

Can you give me an example of how to create a Dask DataFrame?

Sure! You can create a Dask DataFrame from a CSV file like this:

import dask.dataframe as dd

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

How do I perform operations like filtering or grouping on a Dask DataFrame?

You can perform operations similarly to Pandas. For example, to filter:

df_filtered = df[df['column_name'] > value]

And for grouping:

df_grouped = df.groupby('column_name').mean()

What are the advantages of using Dask over Pandas?

Dask can handle larger-than-memory datasets, parallelizes operations across multiple cores, and integrates well with other libraries like NumPy and Scikit-learn.