Are you struggling with slow Pandas performance? Tired of waiting for your data analysis pipelines to complete? This comprehensive guide shows you how to speed up your Pandas code by up to 20x using FireDucks - without changing your existing code.

TL;DR:

• FireDucks is a drop-in replacement for Pandas that runs up to 20x faster
• Works with existing Pandas code - no rewriting needed
• Uses multi-core processing and lazy evaluation
• Perfect for data scientists working with large datasets

Why Your Pandas Code is Slow

Before diving into FireDucks, let's understand why traditional Pandas can be slow:

1. Single-core Processing: Pandas only uses one CPU core, leaving your computer's full power untapped
2. Memory-intensive Operations: Pandas creates multiple copies of data during operations
3. Eager Execution: Every operation triggers immediate computation, preventing optimization
4. According to FireDucks’ benchmarks, it performs up to 20 times faster than pandas and twice as fast as Polars for specific query types, as demonstrated in various tests

What is FireDucks?

FireDucks is a high-performance alternative to Pandas that:

• Maintains 100% compatibility with Pandas API
• Utilizes all available CPU cores
• Implements lazy evaluation for optimal performance
• Reduces memory usage through smart optimization

Installation and Setup

Quick Installation

pip install fireducks

Three Ways to Use FireDucks

1. Jupyter/IPython Integration:

%load_ext fireducks.pandas

1. Direct Import Method:

# Replace this:
# import pandas as pd
# With this:
import fireducks.pandas as pd

1. Script Execution:

python -m fireducks.pandas your_script.py

Practical Examples

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

# Generate large sample dataset
def create_sample_data(size=10_000_000):
    return {
        'id': range(size),
        'value': np.random.randn(size),
        'category': np.random.choice(['A', 'B', 'C', 'D'], size),
        'timestamp': pd.date_range('2024-01-01', periods=size, freq='S')
    }

# Performance comparison
def benchmark_operation():
    data = create_sample_data()
    
    # Traditional Pandas
    start = time.time()
    df_pandas = pd.DataFrame(data)
    pandas_result = df_pandas.groupby('category').agg({
        'value': ['mean', 'std'],
        'timestamp': ['min', 'max']
    })
    pandas_time = time.time() - start
    
    # FireDucks
    start = time.time()
    df_fd = fd.DataFrame(data)
    fd_result = df_fd.groupby('category').agg({
        'value': ['mean', 'std'],
        'timestamp': ['min', 'max']
    })
    fd_time = time.time() - start
    
    return pandas_time, fd_time

pandas_time, fd_time = benchmark_operation()
print(f"💡 Performance Comparison:")
print(f"Pandas: {pandas_time:.2f}s")
print(f"FireDucks: {fd_time:.2f}s")
print(f"Speedup: {pandas_time/fd_time:.1f}x")

Conclusion:

You can find the code here: Google Colab.

FireDucks documentation is available here: FireDucks docs.

FireDucks transforms Pandas performance with:

• Up to 20x faster processing
• Zero code changes required
• Automatic multi-core utilization
• Intelligent memory management

FAQs

Q: Will FireDucks work with my existing Pandas code? A: Yes, FireDucks is a drop-in replacement with 100% Pandas API compatibility.

Q: How does FireDucks compare to other alternatives like Polars? A: FireDucks offers native Pandas compatibility while matching or exceeding Polars' performance.

Q: What system requirements do I need? A: Currently supports Linux x86_64. Minimum 8GB RAM recommended.