Say Goodbye to Slow Data: FireDucks is 125x Faster Than Pandas

Are you tired of staring at your screen, waiting for your Pandas code to process a large dataset? In the world of data science, efficiency is paramount. As datasets grow larger and more complex, the need for faster and more efficient tools becomes increasingly critical. If you’ve ever found yourself waiting endlessly for Pandas to process large datasets, you’re not alone. Meet FireDucks, the Python library that’s 125 times faster than Pandas and ready to supercharge your data workflows. Whether you’re a data scientist, analyst, or developer, FireDucks offers a compelling solution to accelerate your workflows.

What is FireDucks?

FireDucks is a high-performance Python library designed to optimize data analysis tasks. Developed by NEC, a leader in supercomputing technology, FireDucks leverages decades of expertise in high-performance computing to deliver unparalleled speed and efficiency.

• Speed: Up to 125x faster than Pandas (yes, you read that right).
• Compatibility: Uses the same API as Pandas, so you don’t need to rewrite your code.
• Lazy Evaluation: Optimizes operations behind the scenes to save time and resources.

Benchmarking 

The team evaluated FireDucks’ performance using db-benchmark, a benchmark that tests fundamental data science operations like Join and GroupBy across datasets of varying sizes. As of September 10, 2024, FireDucks demonstrates exceptional performance, establishing itself as the fastest dataframe library for groupby and join operations on large datasets.

• For complete evaluation details, refer to the official results here.
• Find Benchmarking details on all parameters here. 

FireDucks vs Pandas: Hands-on 

Here’s a hands-on example to test FireDucks and compare its performance with Pandas. We’ll use a real-world dataset and perform common data analysis tasks like loading data, filtering, groupby, and aggregation. This will help you understand how FireDucks can speed up your workflows.

Step 1: Importing Libraries

import pandas as pd
import fireducks.pandas as fpd
import numpy as np
import time

• pandas: Used to create and manipulate the pandas DataFrame.
• fireducks.pandas: A library that claims to be faster than pandas for certain operations.
• numpy: Used to generate large arrays of random numbers.
• time: Used to measure the execution time of operations.

Step 2: Generating Sample Data

num_rows = 10_000_000
df_pandas = pd.DataFrame({
    'A': np.random.randint(1, 100, num_rows),
    'B': np.random.rand(num_rows),
})

Creates a Pandas DataFrame named df_pandas with 10 million rows:

• Column A: Contains random integers between 1 and 100.
• Column B: Contains random floating-point numbers between 0 and 1.

Step 3: Creating a FireDucks DataFrame

df_fireducks = fpd.DataFrame(df_pandas)

Converts the Pandas DataFrame df_pandas into an equivalent FireDucks DataFrame df_fireducks. This is necessary because FireDucks operates on its own DataFrame type.

Step 4: Measuring Pandas Execution Time

start_time = time.time()
result_pandas = df_pandas.groupby('A')['B'].sum()
pandas_time = time.time() - start_time
print(f"Pandas execution time: {pandas_time:.4f} seconds")

Performs a groupby operation on the A column of the Pandas DataFrame:

• Groups rows by unique values in column A.
• Calculates the sum of column B for each group.

The time taken for this operation is recorded in pandas_time.

Step 5: Measuring FireDucks Execution Time

start_time = time.time()
result_fireducks = df_fireducks.groupby('A')['B'].sum()
fireducks_time = time.time() - start_time
print(f"FireDucks execution time: {fireducks_time:.4f} seconds")

• Performs the same groupby operation using the FireDucks DataFrame.
• The time taken for this operation is recorded in fireducks_time.

Step 6: Comparing Performance

speed_up = pandas_time / fireducks_time
print(f"FireDucks is approximately {speed_up:.2f} times faster than pandas.")

• Calculate the speed-up factor by dividing the time taken by Pandas by the time taken by FireDucks.
• Prints how many times faster FireDucks is compared to Pandas.

Output:

Pandas execution time: 0.1278 seconds
FireDucks execution time: 0.0021 seconds
FireDucks is approximately 61.35 times faster than pandas.

Key Benefits of FireDucks

Why should you switch to FireDucks? Let me count the ways:

• Cross-Platform Support: Works on Linux, Windows (via WSL), and macOS.
• Zero Learning Curve: If you know Pandas, you already know FireDucks.
• Lazy Evaluation: FireDucks optimizes operations behind the scenes, so you don’t have to.
• Automatic Optimization: It rearranges processes to save time and resources.

Important Links

FireDucks has a growing community of data enthusiasts. Here are some resources to get started:

• Official Documentation: FireDucks Docs
• GitHub Repository: FireDucks GitHub
• NYC Demo Notebook: Find Here
• Twitter/X: @fireducksdev

Conclusion

FireDucks offers a significant improvement in data analysis efficiency, delivering 125x faster performance than Pandas. With seamless compatibility, lazy evaluation, and automatic optimization, it simplifies processing large datasets while maintaining a familiar Pandas-like interface. Ideal for tasks like ETL pipelines, batch processing, and exploratory data analysis, FireDucks is a powerful tool for data professionals. Explore its capabilities and join the growing community.

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Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.