Accelerating Data Handling with Pandas and Python: What's the Rush?
In the world of programming, Python is often perceived as slower than compiled languages due to its interpreted, dynamically typed nature. However, a closer look at Python's performance reveals that it is frequently optimized through clever coding techniques, rather than relying solely on inherent language speed.
The key to Python's performance lies in several factors:
- Dynamic typing and reference semantics: Python determines types during runtime, introducing overhead. Optimized code reduces type variability and uses stable type patterns that just-in-time (JIT) compilers or interpreters like PyPy can leverage to produce faster machine code paths.
- Careful coding: Avoiding global variables and favouring local variables can improve execution speed, as accessing local variables is faster in Python.
- Efficient use of language features: Using list comprehensions, generator expressions, and appropriate data structures (such as dictionaries or sets) can significantly reduce looping overhead and lookup time, resulting in faster performance.
- Leverage built-in libraries and vectorized operations: Libraries like NumPy perform computation in optimized native code, bypassing Python's slower loops. Utilizing multiprocessing, multithreading, async programming, and concurrency frameworks can improve throughput and reduce latency in I/O-bound and real-time applications.
- Code-level optimizations guide interpreter optimization: Writing code that avoids cases leading to type ambiguity or global state allows interpreters or JIT compilers to simplify or hoist guard checks, thus enabling faster execution paths.
Recent experiments conducted by an author have shed light on these factors. The experiments involved processing a large dataset, the 7+ Million Company dataset, using two approaches: a conventional single-thread approach and a multi-processing strategy using the Pool class. The workload consisted of a list of 24 file paths, totaling almost 24.5 GB of data and containing 185 million rows.
The results showed a significant difference in results and power based on the algorithm design (single versus multi-core operations). The multi-processing approach used 31.77% less power than the single CPU approach and completed the task in 140.03 seconds, while the conventional approach took 411.92 seconds. This demonstrates that Python's performance can be significantly improved by leveraging multi-processing strategies.
The author's code for the experiment can be found on their GitHub account, and they have written an article demonstrating how to use Rapids and CuDF to speed up Python and Pandas. The author suggests that 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.
In conclusion, while Python's "slowness" is often associated with its dynamic, flexible nature and interpreter overhead, performance improves greatly when developers write code to minimize these overheads, select effective algorithms, use optimized libraries, and apply concurrency where suitable. This focus on coding techniques rather than language redesign is why Python performance is often optimized at the code level.
References:
- Real Python
- NumPy Documentation
- PyPy Documentation
- Python Speed Optimization Techniques
- Python Multithreading
- Data-and-cloud-computing technology plays a crucial role in optimizing Python's performance, particularly in large-scale processing tasks, as leveraging multi-processing strategies, like those demonstrated in the experiment, can significantly reduce power consumption and execution time.
- The focus on technology in data-and-cloud computing enables developers to optimize Python's performance not only through language-specific optimizations but also by implementing efficient techniques such as concurrency, leveraging optimized libraries, and carefully coding their applications.
