Pandarallel

df = pd.DataFrame(np.random.rand(1000, 1000))

# Installation # pip install pandarallel from pandarallel import pandarallel # Initialize with desired number of workers pandarallel.initialize(progress_bar=True) # Use it on your DataFrame df.parallel_apply(my_complex_function, axis=1) Use code with caution. Key Considerations pandarallel

: Parallelizing operations can increase memory usage since each process will have its own copy of the data. This needs to be considered when working with very large datasets. df = pd

Pandarallel works by dividing the DataFrame into smaller chunks and processing each chunk in parallel using multiple CPU cores. This approach is particularly effective for operations that are computationally expensive, such as data aggregation, filtering, and grouping. df = pd.DataFrame(np.random.rand(1000