polars-row-collector 0.3.0

Facade to collect rows one-by-one into a Polars DataFrame (in the least-bad way)

polars-row-collector

Facade to collect rows one-by-one into a Polars DataFrame (in the least-bad way)

• Docs: https://DeflateAwning.github.io/polars-row-collector
• GitHub: https://github.com/DeflateAwning/polars-row-collector
• PyPI: https://pypi.org/project/polars-row-collector/

Getting Started Example

Add the library to your dependencies: uv add polars_row_collector

import polars as pl
from polars_row_collector import PolarsRowCollector

collector = PolarsRowCollector(
    # Note: Schema is optional, but recommended.
    schema={"col1": pl.Int64, "col2": pl.Float64}
)

for item in items:
    row = {
        "col1": item.value1,
        "col2": item.value2,
    }
    collector.add_row(row)

df = collector.to_df()

You can think of collector as filling the same niche as the following alternatives: * list_of_dfs: list[pl.DataFrame] * list_of_dicts: list[dict[str, Any]], then pl.from_dicts(list_of_dicts)

Features

• Highly performant and memory-optimized.
  • 93% lower memory usage compares to a list-of-dicts approach.
• Optionally supply a schema for the incoming rows.
• Thread-safe (when GIL is enabled - default in Python <= 3.15).
• Configuration arguments for safety vs. performance tradeoffs:
  • Behaviour if there are missing columns: Enforce all columns present or allow missing columns.
  • Behaviour if there are extra columns: Drop silently or raise.
  • Maintain insertion order.

Example Applications

• Gathering data in a web scraping/parsing tool.
• Gathering/batching incoming log messages or event logs before writing in bulk to some destination.
• Gathering data in a markup/document parsing pipeline (e.g., XML with lots of conditionals).

Benchmarks

• Benchmark: Collecting 50M rows. Each row has 3 columns.
  • Average Speed: 0.42µs/row for both (consistent).
    • Conclusion: No additional elapsed runtime overhead.
  • Peak memory usage: 93% decrease compared to a naive implementation.
    • Baseline (list-of-dicts): 26,011.93 MiB
    • PolarsRowCollector: 1,860.16 MiB

Baseline (list-of-dicts)

> COLLECT_MODE=dicts uv run perf_scripts/perf_test_script.py

Collected DataFrame. Current RSS: 26,011.93 MiB | Peak RSS: 26,011.93 MiB
Final overall time per row: 0.42µs/row

PolarsRowCollector

> COLLECT_MODE=prc uv run perf_scripts/perf_test_script.py

Collected DataFrame. Current RSS: 1,860.16 MiB | Peak RSS: 1,860.16 MiB
Final overall time per row: 0.42µs/row

Future Features

• Intermediate to-disk storage to temporary parquet files to larger-than-memory collections.
• Further optimize appending many rows at once.
• Read the dataframe so-far, in the middle of gathering rows.
• Documentation.

Disclaimer

As the project's description says, this is the "least-bad way" to accomplish this pattern.

If you can implement your code in such a way that you're not collecting individual rows of a dataframe, you are likely better-off doing it that way (e.g., collecting a list[pl.DataFrame]).

However, there are always exceptions to the best practices. In those cases, this library is an ideal choice, and is significantly more memory-efficient than collecting into a list[dict[str, Any]] then converting to a DataFrame later.