Pandas' operations do not support parallelization. As a result, it adheres to a single-core computation, even when other cores are available. This makes it inefficient and challenging, especially on large datasets. "Pandarallel" allows you to parallelize its operations to multiple CPU cores - by changing just one line of code. Supported methods include apply(), applymap(), groupby(), map() and rolling().
Parallelize Pandas with Pandarallel
Parallelize Pandas with Pandarallel
Parallelize Pandas with Pandarallel
Pandas' operations do not support parallelization. As a result, it adheres to a single-core computation, even when other cores are available. This makes it inefficient and challenging, especially on large datasets. "Pandarallel" allows you to parallelize its operations to multiple CPU cores - by changing just one line of code. Supported methods include apply(), applymap(), groupby(), map() and rolling().