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tags, aliases, date, time, description
| tags | aliases | date | time | description |
|---|---|---|---|---|
| 2024-11-10 | 16:57:31 |
可以用來代替pandas
Now, listen up-the thing is, Pandas is great at data exploration and for middle-sized datasets. But people just use it for everything, like it’s some magic solution that’s going to solve every problem in data, and quite frankly, it isn’t. Working with Pandas on huge datasets can turn your machine into a sputtering fan engine, and memory overhead just doesn’t make sense for some workflows.
Why pandas Is Overrated:
Memory Usage: As Pandas operates mainly in-memory, any operation on a large dataset will badly hit performance.
Limited Scalability: Scaling with Pandas isn’t easy. It was never designed for big data.
What You Should Use Instead: Polars
Polars is an ultra-fast DataFrame library in Rust using Apache Arrow. Optimized for memory efficiency and multithreaded performance, this makes it perfect for when you want to crunch data without heating up your CPU.
import polars as pl
df = pl.read_csv("big_data.csv")
filtered_df = df.filter(pl.col("value") > 50)
print(filtered_df)
Why **Polars**? It will process data that would bring Pandas to its knees, and it handles operations in a fraction of the time. Besides that, it also has lazy evaluation-meaning it is only computing what’s needed.