Polars 대 Pandas Python 데이터프레임의 새로운 시대?

Polars vs. Pandas: What's the Difference?

If you've been keeping up with recent Python developments, you’ve probably heard of Polars, a new library for working with data. While pandas has been the go-to library for a long time, Polars is making waves, especially for handling big datasets. So, what’s the big deal with Polars? How is it different from pandas? Let’s break it down.

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What is Polars?

Polars is a free, open-source library built in Rust (a fast, modern programming language). It’s designed to help Python developers handle data in a faster, more efficient way. Think of it as an alternative to pandas one that shines when you're working with really large datasets that pandas might struggle with.

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Why Was Polars Created?

Pandas has been around for years, and many people still love using it. But as data has gotten bigger and more complex, pandas has started to show some weaknesses. Ritchie Vink, the creator of Polars, noticed these issues and decided to create something faster and more efficient. Even Wes McKinney, the creator of pandas, admitted in a blog post titled "10 Things I Hate About pandas" that pandas could use some improvement, especially with large datasets.

That’s where Polars comes in it’s designed to be blazing fast and memory efficient, two things pandas struggles with when handling big data.

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Key Differences: Polars vs. Pandas

1. Speed

Polars is really fast. In fact, some benchmarks show that Polars can be up to 5–10 times faster than pandas when performing common operations, like filtering or grouping data. This speed difference is especially noticeable when you’re working with large datasets.

2. Memory Usage

Polars is much more efficient when it comes to memory. It uses about 5 to 10 times less memory than pandas, which means you can work with much larger datasets without running into memory issues.

3. Lazy Execution

Polars uses something called lazy execution, which means it doesn’t immediately run each operation as you write it. Instead, it waits until you’ve written a series of operations, then runs them all at once. This helps it optimize and run things faster. Pandas, on the other hand, runs every operation immediately, which can be slower for big tasks.

4. Multithreading

Polars can use multiple CPU cores at the same time to process data, which makes it even faster for big datasets. Pandas is mostly single threaded, meaning it can only use one CPU core at a time, which slows things down, especially with large datasets.

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Why is Polars So Fast?

Polars is fast for a couple of reasons:

• It’s built in Rust, a programming language known for its speed and safety, making it super efficient.
• It uses Apache Arrow, a special way of storing data in memory that makes it easier and faster to work with across different programming languages.

This combination of Rust and Apache Arrow gives Polars the edge over pandas when it comes to speed and memory use.

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Strengths and Limitations of Pandas

While Polars is great for big data, pandas still has its place. Pandas works really well with small to medium-sized datasets and has been around for so long that it’s got tons of features and a huge community. So, if you’re not working with huge datasets, pandas might still be your best option.

However, as your datasets get larger, pandas tends to use more memory and gets slower, making Polars a better choice in those situations.

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When Should You Use Polars?

You should consider using Polars if:

• 대규모 데이터세트(수백만 또는 수십억 행)로 작업하고 있습니다.
• 작업을 빠르게 완료하려면 속도와 성능이 필요합니다.
• 메모리 제약이 있어서 RAM 사용량을 절약해야 합니다.

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결론

폴라와 팬더 모두 장점이 있습니다. 중소 규모의 데이터 세트로 작업하는 경우 Pandas는 여전히 훌륭한 도구입니다. 하지만 대규모 데이터 세트를 처리하고 더 빠르고 메모리 효율적인 것이 필요하다면 Polars는 확실히 시도해 볼 가치가 있습니다. Rust 및 Apache Arrow 덕분에 성능이 향상되어 데이터 집약적인 작업에 환상적인 옵션이 됩니다.

Python이 계속 발전함에 따라 Polars는 빅 데이터 처리를 위한 새로운 goto 도구가 될 수도 있습니다.

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