polars_agent.md

Polars is a high-performance DataFrame library in Python, similar to pandas but optimized for speed and memory efficiency. FireCrawl is a scalable web scraping framework, and subagents can collect data in parallel. Combining these allows you to quickly process and create temporary CSV files for intermediate storage. Below is a guide and complete Python example:

Step 1: Install Required Libraries

pip install polars firecrawl tempfile

Step 2: Setup FireCrawl Subagents

FireCrawl subagents are used to scrape web data concurrently. You can gather data into a list of dictionaries for easier DataFrame handling.

# Example: simulate FireCrawl subagent results  
scraped_data = [
{"title": "Product A", "price": 10.5, "url": "https://example.com/a"},
{"title": "Product B", "price": 15.0, "url": "https://example.com/b"},
{"title": "Product C", "price": 7.25, "url": "https://example.com/c"}
]

Step 3: Create Polars DataFrame

import polars as pl
# Convert scraped data into a Polars DataFrame  
df = pl.DataFrame(scraped_data)
# Optional: perform transformations if needed  
df = df.with_column(
pl.col("price") * 1.1  # e.g., apply a 10% markup
)

Step 4: Generate a Temporary CSV File

Python's tempfile module allows creating temporary files safely.

import tempfile
# Create a temporary CSV file  
with tempfile.NamedTemporaryFile(mode="w+", suffix=".csv", delete=False) as tmp_file:
tmp_filename = tmp_file.name
df.write_csv(tmp_filename)  # Polars method to write CSV
print(f"Temporary CSV file created at: {tmp_filename}")

Step 5: Use the CSV for Further Processing

Once created, your subagents or main app can read the CSV back when needed:

# Read temporary CSV  
df_loaded = pl.read_csv(tmp_filename)
print(df_loaded)

Notes and Best Practices

1. Polars Speed: Polars is faster than pandas for both CSV reading and writing, especially with large web-scraped datasets.
2. Temporary Files: delete=False allows inspection after the script ends; change to True for automatic deletion.
3. Subagent Integration: Each FireCrawl subagent can independently write its scraped data to a separate temporary CSV and later merge using Polars’ concat.
4. Parallel Processing: Polars supports multi-threaded execution, reducing bottlenecks in large-scale scraping pipelines.

Example Summary

# Combined workflow  
import polars as pl
import tempfile
scraped_data = [{"title":"A","price":10},{"title":"B","price":20}]
df = pl.DataFrame(scraped_data)
with tempfile.NamedTemporaryFile(mode="w+", suffix=".csv", delete=False) as tmp_file:
df.write_csv(tmp_file.name)
print(f"CSV created at {tmp_file.name}")

This approach allows FireCrawl subagents to efficiently store temporary CSVs using Polars, enabling fast data post-processing and aggregation before final storage.

Source(s):

• https://www.datacamp.com/tutorial/python-polars-tutorial-complete-guide-for-beginners
• https://www.geeksforgeeks.org/python/an-introduction-to-polars-pythons-tool-for-large-scale-data-analysis/
• https://docs.pola.rs/user-guide/getting-started/
• https://www.freecodecamp.org/news/how-to-use-the-polars-library-in-python-for-data-analysis/