Pandarallel ^hot^ 【SECURE ★】

def heavy_func(x): return sum(np.sin(x) * np.cos(x) for _ in range(100)) start = time.time() result_pd = df['x'].apply(heavy_func) print(f"Pandas: time.time() - start:.2fs") Pandarallel start = time.time() result_pll = df['x'].parallel_apply(heavy_func) print(f"Pandarallel: time.time() - start:.2fs") Common Issues & Solutions 1. PicklingError (lambdas with closures) # This will fail df.parallel_apply(lambda row: row['a'] + external_var) Solution: Define a regular function def add_external(row): return row['a'] + external_var

What is Pandarallel? 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. Installation pip install pandarallel For full features (progress bars, etc.): pandarallel

df = pd.DataFrame('x': np.random.rand(500000)) def heavy_func(x): return sum(np