Pandarallel

is a Python library that provides easy parallel computing for pandas operations. It allows you to replace standard pandas apply , map , and other functions with parallelized versions, leveraging all CPU cores of your machine.

In the world of Python data science, Pandas is the gold standard for data manipulation. However, as datasets grow into the millions of rows, standard operations like .apply() can become a major bottleneck because they typically run on a single CPU core. pandarallel

Pandarallel is excellent for on medium to large datasets where you want minimal code changes. It's not a silver bullet—always profile your code first. For purely numeric operations, vectorized pandas/numpy may still be faster. But for complex row-wise operations, Pandarallel provides near-linear speedup with CPU cores. is a Python library that provides easy parallel

df = pd.DataFrame( 'group': np.random.choice(['A', 'B', 'C'], 100000), 'value': np.random.randn(100000) ) However, as datasets grow into the millions of

❌