Speed Up Pandas apply() on Multiple Cores: dask, pandas-parallel, and Other Techniques

• By default, Pandas' apply() executes operations on a DataFrame or Series one row or element at a time.
• This can be slow for large datasets, especially when the function you're applying is computationally expensive.

Parallelization Libraries

To speed up apply() on multi-core systems, you can use libraries designed for parallel processing:

1. dask (integrates well with Pandas):

   • Install: pip install dask
   • Import: import dask.dataframe as dd
   • Create a Dask DataFrame: ddf = dd.from_pandas(df)
   • Use ddf.apply() for parallel apply() operations.

2. pandas-parallel (easier to use, but may have limitations):

   • Install: pip install pandas-parallel
   • Import: from pandas_parallel import ParallelPool
   • Create a pool: pool = ParallelPool(processes=n_cores) (replace n_cores with the desired number of cores)
   • Use pool.map(func, df) instead of df.apply(func).

3. modin (alternative Pandas implementation for parallel execution):

   • Install: pip install modin[ray] (or [dask]) depending on the backend
   • Import: import modin.pandas as pd
   • Replace import pandas as pd
   • modin.pandas.DataFrame automatically parallelizes operations.

Important Considerations

• Not all apply() functions benefit from parallelization. If the function involves operations that can't be easily divided into independent tasks, parallelization may not provide a significant speedup.
• The overhead of parallelization can outweigh the benefits for small datasets.

Choosing the Right Library

• dask is a powerful choice for large, complex datasets and integrates seamlessly with existing Pandas code.
• pandas-parallel is simpler to use, but may have limitations for more advanced scenarios.
• modin offers a complete parallel Pandas replacement, but it's a larger dependency. Choose it if you're heavily reliant on apply() or other parallelizable operations.

General Tips

• Profile your code to identify bottlenecks before attempting parallelization.
• Experiment with different libraries and core counts to find the optimal configuration for your hardware and workload.
• Consider vectorization with NumPy for performance gains when possible.

Example Codes for Parallelizing Pandas apply()

Using dask

import pandas as pd
import dask.dataframe as dd

# Sample data (replace with your actual DataFrame)
data = {'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']}
df = pd.DataFrame(data)

# Define a function to apply (replace with your actual function)
def slow_function(row):
    # Simulate a time-consuming operation
    import time
    time.sleep(1)  # Replace with your actual function logic
    return row['col1'] * 2

# Create a Dask DataFrame
ddf = dd.from_pandas(df)

# Apply the function in parallel using dask.dataframe.apply()
start_time = time.time()
ddf = ddf.apply(slow_function, axis=1)  # Apply to each row (axis=1)
result_df = ddf.compute()  # Trigger computation and convert back to Pandas DataFrame
end_time = time.time()

print(f"Time taken with dask.dataframe.apply: {end_time - start_time:.2f} seconds")
print(result_df)

Using pandas-parallel

import pandas as pd
from pandas_parallel import ParallelPool

# Sample data (replace with your actual DataFrame)
data = {'col1': [1, 2, 3], 'col2': ['a', 'b', 'c']}
df = pd.DataFrame(data)

# Define a function to apply (replace with your actual function)
def slow_function(row):
    # Simulate a time-consuming operation
    import time
    time.sleep(1)  # Replace with your actual function logic
    return row['col1'] * 2

# Get the number of cores (adjust as needed)
n_cores = 4

# Create a parallel pool
pool = ParallelPool(processes=n_cores)

# Apply the function in parallel using pool.map()
start_time = time.time()
result = pool.map(slow_function, df.itertuples(index=False))  # Extract data without index
result_df = pd.DataFrame(result, columns=df.columns)  # Create DataFrame from results
end_time = time.time()

print(f"Time taken with pandas-parallel.ParallelPool.map: {end_time - start_time:.2f} seconds")
print(result_df)

• Import: import swifter
• Usage: df = df.swifter.apply(func, axis=1) (similar to Pandas apply())
• Pros: Easy to use, often faster than Pandas apply(), can automatically choose between Dask or normal Pandas depending on the situation.
• Cons: Might have limitations for complex functions or specific hardware configurations.

mapply

• Import: import mapply
• Usage: result = mapply(func, df) (returns a list, convert to DataFrame if needed)
• Pros: Simple to use, efficient for some operations.
• Cons: Less mature than other libraries, might not be suitable for all use cases.

Joblib (with sklearn)

• Install: pip install scikit-learn (Joblib comes bundled with scikit-learn)
• Import: from joblib import Parallel, delayed
• Usage:

def apply_with_joblib(df, func):
  delayed_func = delayed(func)
  with Parallel(n_jobs=n_cores) as parallel:
      results = parallel(delayed_func(row) for index, row in df.iterrows())
  return pd.DataFrame(results, columns=df.columns)

• Pros: Lightweight, leverages existing scikit-learn library.
• Cons: Requires more code compared to other options, might not be as performant as dedicated parallelization libraries.

Spark

• Spark is a distributed data processing framework that can handle very large datasets.
• While not a direct replacement for Pandas apply(), it can be used for similar parallel computations on large datasets.
• Requires more setup and expertise compared to Pandas libraries.

• Consider the size and complexity of your dataset.
• Evaluate the ease of use and performance requirements for your specific use case.
• Experiment with different options to find the best fit for your needs.