How can I efficiently handle large datasets in Python?
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How can I efficiently handle large datasets in Python?
You can use libraries like Pandas for data manipulation, Dask for parallel computing, and NumPy for numerical operations.
What are the advantages of using Dask over Pandas?
Dask allows for out-of-core computation, meaning it can handle datasets larger than memory by breaking them into smaller chunks.
Can you give me an example of using Dask?
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').
What about performance? How does Dask compare to Pandas?
Dask can be faster for large datasets since it parallelizes operations, but for smaller datasets, Pandas may be more efficient.
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