How can I integrate Dask with Pandas for better performance?

How can I integrate Dask with Pandas for better performance?

You can integrate Dask with Pandas by using Dask DataFrames, which mimic Pandas DataFrames but allow for parallel computing.

What are the benefits of using Dask with Pandas?

The main benefits include handling larger-than-memory datasets, parallel processing, and improved performance on complex computations.

Can you show me a simple code example?

Sure! Here's a basic example:

import dask.dataframe as dd
import pandas as pd

# Create a Pandas DataFrame
pdf = pd.DataFrame({'x': range(100), 'y': range(100)})

# Convert it to a Dask DataFrame
ddf = dd.from_pandas(pdf, npartitions=2)

# Perform a computation
result = ddf['x'].mean().compute()
print(result)