When preparing text data for an AI model, you’re often working with millions of rows. For this reason, many practitioners are interested in Polars NLP Pre-processing for both efficiency and scalability. Using pandas.apply() is a major bottleneck—it’s slow and single-threaded.
Polars performs these text operations in parallel, making it 10-100x faster. Let’s clean a dataset using the Polars Expression API.
The Goal
We want to take messy user reviews and turn them into clean “tokens” (words) ready for a model.
import polars as pl
import re
df = pl.DataFrame({
"review": [
"I LOVE this product! 10/10",
"It was... okay? Not bad.",
"Worst item ever. DO NOT BUY!!",
]
})The Polars “Chained” Expression
Let’s do all our cleaning in one single, optimized command.
- Convert to lowercase.
- Remove all punctuation and numbers.
- Split the clean sentence into a list of words.
clean_df = df.with_columns(
pl.col("review")
.str.to_lowercase()
.str.replace_all(r"[^a-z\s]", "") # Regex: Keep only letters and spaces
.str.split(by=" ")
.alias("tokens")
)
print(clean_df)Output:
shape: (3, 2) ┌───────────────────────────┬──────────────────────────────────┐ │ review ┆ tokens │ │ --- ┆ --- │ │ str ┆ list[str] │ ╞═══════════════════════════╪══════════════════════════════════╡ │ I LOVE this product! 10/10┆ ["i", "love", "this", "product",… │ │ It was... okay? Not bad. ┆ ["it", "was", "okay", "not", "ba… │ │ Worst item ever. DO NOT B…┆ ["worst", "item", "ever", "do", … │ └───────────────────────────┴──────────────────────────────────┘
This is the modern, high-speed way to prepare text data. This tokens column is now ready to be fed into a Hugging Face Tokenizer or a classic TfidfVectorizer.
Key Takeaways
- Preparing text data for AI models requires handling millions of rows efficiently.
- Polars NLP Pre-processing is significantly faster than pandas, as it operates in parallel up to 100x.
- The goal is to clean messy user reviews into organized tokens suitable for models.
- Using the Polars Expression API allows for streamlined cleaning in a single command.
- The cleaned tokens are ready for use with Hugging Face Tokenizer or TfidfVectorizer.
