Accelerating Data Handling with Pandas and Python: Explanation Required
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In a recent experiment, the perceived slowness of Python and its popular data analysis library, Pandas, has been called into question. The study, carried out by an anonymous author, aimed to demonstrate that Python's reputation for being slow is often more a result of suboptimal coding practices rather than inherent limitations in the language itself.
The experiment involved processing a large dataset, the 7+ Million Company dataset, which totals over 1.1 GB and contains 185 million rows. The dataset is licensed under the creative commons CC0.01, making it freely available for use.
The author compared two approaches: a conventional single-thread approach and a multi-processing strategy. In the single-thread scenario, the author iterated over the work and passed it off to a worker function that read a file into a Pandas dataframe, grouped it, cleaned it, and returned the dataframe. In the multi-processing scenario, the author used the Pool class to chunk up the work and spread it to available workers.
The results were striking. The multi-processing strategy resulted in a three times faster code execution compared to the single CPU approach. In fact, the conventional single-thread approach took 411.92 seconds, while the multi-processing strategy using Pool() took a mere 140.03 seconds.
Moreover, the multi-processing approach also had a significant impact on power consumption. The power consumption during the single CPU approach was 0.1kW for 7 minutes, while during the multi-processing approach, it was 0.2kW for just 2.33 minutes. This translates to a 31.77% reduction in power usage.
This experiment highlights the importance of efficient coding practices when working with Python and Pandas. While Python is an interpreted language that can run slower than compiled languages, much of the perceived slowness comes from how the code is written rather than inherent limitations in Python itself.
Performance depends heavily on coding style and practices. Using vectorized operations in Pandas or NumPy, which delegate heavy computations to optimized C or Fortran code, dramatically speeds up data processing compared to naive Python loops. Utilizing built-in functions like , , and list comprehensions can also be much faster than equivalent explicit loops because these are implemented in C.
Avoiding global variables, reducing redundant calculations, caching results, and profiling to find bottlenecks allow targeted optimizations. Libraries like Pandas are not slow themselves; rather, the bottleneck often lies in how users apply them. For instance, writing inefficient loops over DataFrame rows instead of using vectorized DataFrame operations that leverage underlying compiled code can significantly slow down performance.
As the debate about Python's slowness continues, it's clear that with proper techniques, Python and Pandas can perform very efficiently for a wide range of tasks. This is especially important as long-running ETL jobs, deep neural network training, and other tasks may soon become part of the ESG debate and come under scrutiny for Green House Gas emission reduction challenges.
The author has written an article about speeding up Python and Pandas using Rapids and CuDF on a capable GPU, further demonstrating the potential for improved performance in data-intensive tasks. The script for the experiment can be found on the author's GitHub account at ReadingList/parallelism.py.
In conclusion, while Python may not be the fastest language out of the box, with careful coding practices, it can deliver performance that rivals or even surpasses compiled languages for many tasks. By embracing efficient coding practices and leveraging libraries like Pandas and NumPy, Python developers can unlock the full potential of their data analysis workflows.
Data-and-cloud-computing techniques, such as multi-processing strategies, can significantly enhance the performance of technology like Python and its data analysis library, Pandas. However, it's crucial to implement efficient coding practices, such as avoiding global variables and reducing redundant calculations, to fully realize the benefits of such technology.
