How can I use Numba with Pandas for faster data processing?

How can I use Numba with Pandas for faster data processing?

You can use Numba to compile Python functions to machine code, which can significantly speed up operations on Pandas DataFrames. Just decorate your function with @jit or @njit.

Can you give me an example of how to apply Numba to a Pandas DataFrame?

Sure! Here's a simple example:

import pandas as pd
import numpy as np
from numba import jit

# Sample DataFrame
df = pd.DataFrame({'a': np.random.rand(1000000), 'b': np.random.rand(1000000)})

@jit
def compute_sum(a, b):
    return a + b

# Apply Numba function
result = compute_sum(df['a'].values, df['b'].values)

What are the limitations of using Numba with Pandas?

Numba works best with NumPy arrays, so you may face limitations when using complex Pandas operations or non-numeric data types. It's also not compatible with all Python features.

Is there a performance difference when using Numba with small DataFrames?

Yes, for small DataFrames, the overhead of compiling the function with Numba may outweigh the performance benefits. It's generally more effective with larger datasets.