Modern Data Processing Libraries: Beyond Pandas

As discussed in my previous article about data architectures emphasizing emerging trends, data processing is one of the key components in the modern data architecture. This article discusses various alternatives to Pandas library for better performance in your data architecture. 

Data processing and data analysis are crucial tasks in the field of data science and data engineering. As datasets grow larger and more complex, traditional tools like pandas can struggle with performance and scalability. This has led to the development of several alternative libraries, each designed to address specific challenges in data manipulation and analysis.

Introduction

The following libraries have emerged as powerful tools for data processing:

1. Pandas – The traditional workhorse for data manipulation in Python
2. Dask – Extends pandas for large-scale, distributed data processing
3. DuckDB – An in-process analytical database for fast SQL queries
4. Modin – A drop-in replacement for pandas with improved performance
5. Polars – A high-performance DataFrame library built on Rust
6. FireDucks – A compiler-accelerated alternative to pandas
7. Datatable – A high-performance library for data manipulation

Each of these libraries offers unique features and benefits, catering to different use cases and performance requirements. Let's explore each one in detail:

Pandas

Pandas is a versatile and well-established library in the data science community. It offers robust data structures (DataFrame and Series) and comprehensive tools for data cleaning and transformation. Pandas excels at data exploration and visualization, with extensive documentation and community support. 

However, it faces performance issues with large datasets, is limited to single-threaded operations, and can have high memory usage for large datasets. Pandas is ideal for smaller to medium-sized datasets (up to a few GB) and when extensive data manipulation and analysis are required.

Dask

Dask extends pandas for large-scale data processing, offering parallel computing across multiple CPU cores or clusters and out-of-core computation for datasets larger than available RAM. It scales pandas operations to big data and integrates well with the PyData ecosystem. 

However, Dask only supports a subset of the pandas API and can be complex to set up and optimize for distributed computing. It's best suited for processing extremely large datasets that don't fit in memory or require distributed computing resources.

import dask.dataframe as dd
import pandas as pd
import time

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}

# Pandas benchmark
start_time = time.time()
df_pandas = pd.DataFrame(data)
result_pandas = df_pandas.groupby('A').sum()
pandas_time = time.time() - start_time

# Dask benchmark
start_time = time.time()
df_dask = dd.from_pandas(df_pandas, npartitions=4)
result_dask = df_dask.groupby('A').sum()
dask_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"Dask time: {dask_time:.4f} seconds")
print(f"Speedup: {pandas_time / dask_time:.2f}x")

For better performance, load data with Dask using dd.from_dict(data, npartitions=4 in place of the Pandas dataframe dd.from_pandas(df_pandas, npartitions=4)

Output

Pandas time: 0.0838 seconds
Dask time: 0.0213 seconds
Speedup: 3.93x

DuckDB

DuckDB is an in-process analytical database that offers fast analytical queries using a columnar-vectorized query engine. It supports SQL with additional features and has no external dependencies, making setup simple. DuckDB provides exceptional performance for analytical queries and easy integration with Python and other languages. 

However, it's not suitable for high-volume transactional workloads and has limited concurrency options. DuckDB excels in analytical workloads, especially when SQL queries are preferred.

import duckdb
import pandas as pd
import time

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}
df = pd.DataFrame(data)

# Pandas benchmark
start_time = time.time()
result_pandas = df.groupby('A').sum()
pandas_time = time.time() - start_time

# DuckDB benchmark
start_time = time.time()
duckdb_conn = duckdb.connect(':memory:')
duckdb_conn.register('df', df)
result_duckdb = duckdb_conn.execute("SELECT A, SUM(B) FROM df GROUP BY A").fetchdf()
duckdb_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"DuckDB time: {duckdb_time:.4f} seconds")
print(f"Speedup: {pandas_time / duckdb_time:.2f}x")

Output

Pandas time: 0.0898 
seconds DuckDB time: 0.1698 
seconds Speedup: 0.53x

Modin

Modin aims to be a drop-in replacement for pandas, utilizing multiple CPU cores for faster execution and scaling pandas operations across distributed systems. It requires minimal code changes to adopt and offers potential for significant speed improvements on multi-core systems. 

However, Modin may have limited performance improvements in some scenarios and is still in active development. It's best for users looking to speed up existing pandas workflows without major code changes.

import modin.pandas as mpd
import pandas as pd
import time

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}

# Pandas benchmark
start_time = time.time()
df_pandas = pd.DataFrame(data)
result_pandas = df_pandas.groupby('A').sum()
pandas_time = time.time() - start_time

# Modin benchmark
start_time = time.time()
df_modin = mpd.DataFrame(data)
result_modin = df_modin.groupby('A').sum()
modin_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"Modin time: {modin_time:.4f} seconds")
print(f"Speedup: {pandas_time / modin_time:.2f}x")

Output

Pandas time: 0.1186 
seconds Modin time: 0.1036 
seconds Speedup: 1.14x

Polars

Polars is a high-performance DataFrame library built on Rust, featuring a memory-efficient columnar memory layout and a lazy evaluation API for optimized query planning. It offers exceptional speed for data processing tasks and scalability for handling large datasets. 

However, Polars has a different API from pandas, requiring some learning, and may struggle with extremely large datasets (100 GB+). It's ideal for data scientists and engineers working with medium to large datasets who prioritize performance.

import polars as pl
import pandas as pd
import time

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}

# Pandas benchmark
start_time = time.time()
df_pandas = pd.DataFrame(data)
result_pandas = df_pandas.groupby('A').sum()
pandas_time = time.time() - start_time

# Polars benchmark
start_time = time.time()
df_polars = pl.DataFrame(data)
result_polars = df_polars.group_by('A').sum()
polars_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"Polars time: {polars_time:.4f} seconds")
print(f"Speedup: {pandas_time / polars_time:.2f}x")

Output

Pandas time: 0.1279 seconds
Polars time: 0.0172 seconds
Speedup: 7.45x

FireDucks

FireDucks offers full compatibility with the pandas API, multi-threaded execution, and lazy execution for efficient data flow optimization. It features a runtime compiler that optimizes code execution, providing significant performance improvements over pandas. FireDucks allows for easy adoption due to its pandas API compatibility and automatic optimization of data operations. 

However, it's relatively new and may have less community support and limited documentation compared to more established libraries.

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

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}

# Pandas benchmark
start_time = time.time()
df_pandas = pd.DataFrame(data)
result_pandas = df_pandas.groupby('A').sum()
pandas_time = time.time() - start_time

# FireDucks benchmark
start_time = time.time()
df_fireducks = fpd.DataFrame(data)
result_fireducks = df_fireducks.groupby('A').sum()
fireducks_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"FireDucks time: {fireducks_time:.4f} seconds")
print(f"Speedup: {pandas_time / fireducks_time:.2f}x")

Output

Pandas time: 0.0754 seconds
FireDucks time: 0.0033 seconds
Speedup: 23.14x

Datatable

Datatable is a high-performance library for data manipulation, featuring column-oriented data storage, native-C implementation for all data types, and multi-threaded data processing. It offers exceptional speed for data processing tasks, efficient memory usage, and is designed for handling large datasets (up to 100 GB). Datatable's API is similar to R's data.table. 

However, it has less comprehensive documentation compared to pandas, fewer features, and is not compatible with Windows. Datatable is ideal for processing large datasets on a single machine, particularly when speed is crucial.

import datatable as dt
import pandas as pd
import time

# Sample data
data = {'A': range(1000000), 'B': range(1000000, 2000000)}

# Pandas benchmark
start_time = time.time()
df_pandas = pd.DataFrame(data)
result_pandas = df_pandas.groupby('A').sum()
pandas_time = time.time() - start_time

# Datatable benchmark
start_time = time.time()
df_dt = dt.Frame(data)
result_dt = df_dt[:, dt.sum(dt.f.B), dt.by(dt.f.A)]
datatable_time = time.time() - start_time

print(f"Pandas time: {pandas_time:.4f} seconds")
print(f"Datatable time: {datatable_time:.4f} seconds")
print(f"Speedup: {pandas_time / datatable_time:.2f}x")

Output

Pandas time: 0.1608 seconds
Datatable time: 0.0749 seconds
Speedup: 2.15x

Performance Comparison

• Data loading: 34 times faster than pandas for a 5.7GB dataset
• Data sorting: 36 times faster than pandas
• Grouping operations: 2 times faster than pandas

Datatable excels in scenarios involving large-scale data processing, offering significant performance improvements over pandas for operations like sorting, grouping, and data loading. Its multi-threaded processing capabilities make it particularly effective for utilizing modern multi-core processors

Conclusion

In conclusion, the choice of library depends on factors such as dataset size, performance requirements, and specific use cases. While pandas remains versatile for smaller datasets, alternatives like Dask and FireDucks offer strong solutions for large-scale data processing. DuckDB excels in analytical queries, Polars provides high performance for medium-sized datasets, and Modin aims to scale pandas operations with minimal code changes.

The bar diagram below shows the performance of the libraries, using the DataFrame for comparison. The data is normalized for showing the percentages.

For the Python code that shows the above bar chart with normalized data, refer to the Jupyter Notebook. Use Google Colab as FireDucks is available only on Linux

Comparison Chart

This table provides a quick overview of each library's strengths, limitations, and best use cases, allowing for easy comparison across different aspects such as performance, scalability, and API similarity to pandas.