phpolars

Data & Workflow

Statistics, Modeling & Visualization

Data & Workflow · Tables, pipelines, and notebooks

phpolars

Polars data frames for PHP

Read the docs Try an example Source
Install 
pie install phpstats/phpolars
Requires
PIE
Builds with
Rust toolchain libclang
Platforms
Linux macOS NTS (non-thread-safe) PHP

phpolars is a native PHP extension, written in Rust, that brings the Polars columnar engine to PHP. PIE installs it as the php_polars extension for PHP 8.3, 8.4, and 8.5 on Linux and macOS.

phibble's PolarsTable holds a table in Polars through this extension, so phplyr pipelines can run in Polars. phibble and phplyr work without phpolars.

In phplyr's backend benchmark (Apple M5 Ultra, PHP 8.5.10, October 2026), pipelines on a PolarsTable ran 1.3 to 15 times faster than on an in-memory phibble data frame. The pipeline that filters with a PHP closure gained the least.

Example

use function Phpolars\{col, len, read_csv};

$sales = read_csv('sales.csv', tryParseDates: true);

$top = $sales
    ->filter(col('units')->gt(0))
    ->withColumns(col('units')->mul(col('price'))->alias('revenue'))
    ->groupBy('product', maintainOrder: true)
    ->agg(col('revenue')->sum()->round(2), ['orders' => len()], col('price')->mean()->round(2)->alias('avg_price'))
    ->sort('revenue', descending: true);

echo $top;

Output

shape: (4, 4)
┌─────────┬─────────┬────────┬───────────┐
│ product ┆ revenue ┆ orders ┆ avg_price │
│ ---     ┆ ---     ┆ ---    ┆ ---       │
│ str     ┆ f64     ┆ u32    ┆ f64       │
╞═════════╪═════════╪════════╪═══════════╡
│ cherry  ┆ 1307.38 ┆ 44     ┆ 4.09      │
│ banana  ┆ 1285.76 ┆ 48     ┆ 4.35      │
│ apple   ┆ 1227.68 ┆ 42     ┆ 4.38      │
│ date    ┆ 1153.58 ┆ 41     ┆ 4.46      │
└─────────┴─────────┴────────┴───────────┘

Run it live

All vignettes →
Lazy queries over a large file Scan a Parquet or CSV file with up to 5 million rows, let Polars push the filter and column selection into the scan, and compare the time with reading the whole file first. phplyr pipelines on Polars Run one phplyr pipeline on phibble's in-memory DataFrame and on a PolarsTable, check that the results agree, and compare the times. Rolling windows and asof joins Price a year of daily sales with an asof join, smooth it with a time-based rolling mean, and summarize it by month or quarter. Polars pipeline The same pipeline on an in-memory data frame and in Polars, with timings for up to 300,000 rows.