How can I use Dask DataFrames for large datasets?

How can I use Dask DataFrames for large datasets?

Dask DataFrames allow you to work with large datasets that don't fit into memory by breaking them into smaller chunks. You can use familiar Pandas-like syntax to manipulate these chunks.

Can you show me a simple example of creating 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_dataset.csv')

What operations can I perform on a Dask DataFrame?

You can perform operations like filtering, grouping, and aggregating, similar to Pandas. For example, to filter rows:

df[df['column_name'] > value]

How do I compute the results after performing operations?

You can call the .compute() method to execute the operations and bring the results into memory. For example:

result = df.compute()