Polars
docs.pola.rs › py-polars › html › reference › dataframe › index.html
DataFrame — Polars documentation
When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output ... Hash and combine the rows in this DataFrame.
Polars
pola.rs
Polars — DataFrames for the new era
Polars is a high-performance DataFrame library for Python and Rust. Built on Apache Arrow, it's the fastest way to work with tabular data at any scale.
I'm sceptic about polars
I went Polars for the syntax, not for the speed tbh More on reddit.com
179
83
July 17, 2024Pandas or Polars to work with dataframes?
I rewrote some old code that used Pandas to graph statistics recorded at high frequency over long periods. Think around 50 columns, and millions of rows. But the catch? Each row was a span of time, not an instantaneous measurement - and the time spans were variable and overlapping. (specifically, job logs on an HPC cluster). The pandas 1.x code took about half an hour to run - mostly because of the majority of the work being stuck in a single thread. I rewrote it in Polars (with the help of an expert on the Polars discord) and it brought that down to three minutes. In contrast to the original code, this was able to leverage the cores available properly. I did not touch anything directly to do threading, like reading files in a pool or such. 100% the doing of Polars, there. More on reddit.com
68
79
April 10, 202310:16
Polars Tutorial 1: Intro to Polars and Your First DataFrame - YouTube
08:19
Working With Python Polars: Getting Started with DataFrames - YouTube
Learning the Polars DataFrame Library!
01:49:41
Learning the Polars DataFrame Library! - YouTube
15:12
How to Use Polars, the Modern Pandas Alternative! Getting Started ...
The Secret to Polars' Ultra-Fast DataFrame Operations
GitHub
github.com › pola-rs › polars
GitHub - pola-rs/polars: Extremely fast Query Engine for DataFrames, written in Rust · GitHub
1 week ago - Polars is an analytical query engine for DataFrames, written in Rust. It is designed to be fast, easy to use and expressive.
Author pola-rs
CodeMag
codemag.com › Article › 2212051 › Using-the-Polars-DataFrame-Library
Using the Polars DataFrame Library
Wei-Meng shows you how Polars powers up speed and takes a bite out of inefficiencies in large datasets. Polars is a DataFrame library written in Rust, and despite its cute name, it brings all the p...
Databricks
databricks.com › blog › polars-vs-pandas
Polars vs Pandas | Databricks
Pandas enjoys extensive documentation, community support and mature plotting libraries. It’s popular for small-to-medium sized datasets and exploratory analysis. Polars is a fast, Rust-based, columnar DataFrame library with a Python API.
Polars
docs.pola.rs › api › python › stable › reference › dataframe › api › polars.DataFrame.rows.html
polars.DataFrame.rows — Polars documentation
Try distributed Polars on Kubernetes or AWS for free. Get started now ... Returns all data in the DataFrame as a list of rows of python-native values.
Pola-rs
pola-rs.github.io › r-polars › man › pl__DataFrame.html
DataFrame - Polars R Package
library("polars") # Constructing a DataFrame from vectors: pl$DataFrame(a = 1:2, b = 3:4)
Reddit
reddit.com › r/dataengineering › i'm sceptic about polars
r/dataengineering on Reddit: I'm sceptic about polars
July 17, 2024 -
I've first heard about polars about a year ago, and It's been popping up in my feeds more and more recently.
But I'm just not sold on it. I'm failing to see exactly what role it is supposed to fit.
The main selling point for this lib seems to be the performance improvement over python. The benchmarks I've seen show polars to be about 2x faster than pandas. At best, for some specific problems, it is 4x faster.
But here's the deal, for small problems, that performance gains is not even noticeable. And if you get to the point where this starts to make a difference, then you are getting into pyspark territory anyway. A 2x performance improvement is not going to save you from that.
Besides pandas is already fast enough for what it does (a small-data library) and has a very rich ecosystem, working well with visualization, statistics and ML libraries. And in my opinion it is not worth splitting said ecosystem for polars.
What are your perspective on this? Did a lose the plot at some point? Which use cases actually make polars worth it?
Top answer 1 of 5
164
I went Polars for the syntax, not for the speed tbh
2 of 5
70
For the size of data we work with, (100s of GB), Polars is the best choice. Pandas would on choke data that size. Spark would be overkill for us. We can run Polars on a single machine without the hassle of setting up and maintaining a Spark cluster. From my experience Polars is orders of magnitude faster than Pandas (when Pandas doesn't choke altogether). Polars has the additional advantage that its API encourages you to write good clean code. Optimization is done for you without having to resort to coding tricks. In my opinion it's advantages will eventually lead to Polars edging out Pandas in the dataframe library space.
Polars
docs.pola.rs › api › python › stable › reference › dataframe › api › polars.DataFrame.sample.html
polars.DataFrame.sample — Polars documentation
>>> df = pl.DataFrame( ... { ... "foo": [1, 2, 3], ... "bar": [6, 7, 8], ... "ham": ["a", "b", "c"], ...
Practical Business Python
pbpython.com › polars-intro.html
Introduction to Polars - Practical Business Python
January 14, 2024 - As you work with pandas and polars there are convenience functions for moving back and forth between the two. Here’s an example of creating a pandas dataframe from polars:
Dskrzypiec
dskrzypiec.dev › polars
Polars - modern data frame library
April 15, 2023 - Recently I was exposed to polars. As they describe *Lightning-fast DataFrame library for Rust and Python*. Initially I was neutral, thinking "ok just another data frame library just written in rust", but it turned out to be better in several aspects than other libraries of this kind.
Reddit
reddit.com › r/python › pandas or polars to work with dataframes?
r/Python on Reddit: Pandas or Polars to work with dataframes?
April 10, 2023 -
I've been working with Pandas long time ago and recently I noticed that Pandas 2.0.0 was released (https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html)
However, I see lots of people pointing up that the (almost new) library Polars is much faster than Pandas.
I also did 2 analyses on this and it looks like Polars is faster:
1- https://levelup.gitconnected.com/pandas-vs-polars-vs-pandas-2-0-fight-7398055372fb
2- https://medium.com/gitconnected/pandas-vs-polars-vs-pandas-2-0-round-2-e1b9acc0f52f
What is your opinion on this? Do you like more Polars?
Do you think Pandas 2.0 will decrease the time difference between Pandas and Polars?
Top answer 1 of 18
99
I rewrote some old code that used Pandas to graph statistics recorded at high frequency over long periods. Think around 50 columns, and millions of rows. But the catch? Each row was a span of time, not an instantaneous measurement - and the time spans were variable and overlapping. (specifically, job logs on an HPC cluster). The pandas 1.x code took about half an hour to run - mostly because of the majority of the work being stuck in a single thread. I rewrote it in Polars (with the help of an expert on the Polars discord) and it brought that down to three minutes. In contrast to the original code, this was able to leverage the cores available properly. I did not touch anything directly to do threading, like reading files in a pool or such. 100% the doing of Polars, there.
2 of 18
27
Depends what you're doing. If you're doing data manipulation polars will let you parallelize your operations across multiple cores. If you're using libraries designed to work with pandas, then you'll need pandas. If you're doing both it might make sense to do manipulations in polars and convert to pandas to use other libraries.
Polars
docs.pola.rs › api › python › dev › reference › api › polars.from_pandas.html
polars.from_pandas — Polars documentation
Construct a Polars DataFrame or Series from a pandas DataFrame, Series, or Index.
Polars
docs.pola.rs › py-polars › html › reference › dataframe › attributes.html
Attributes — Polars documentation - DataFrame
Get or set column names · Get the column data types
Polars
docs.pola.rs › user-guide › getting-started
Getting started - Polars user guide
Polars supports reading and writing for common file formats (e.g., csv, json, parquet), cloud storage (S3, Azure Blob, BigQuery) and databases (e.g., postgres, mysql). Below, we create a small dataframe and show how to write it to disk and read it back.
Medium
medium.com › data-science › an-introduction-to-polars-for-pandas-users-2a52b2a03017
An Introduction to Polars for Pandas Users | by David Hundley | TDS Archive | Medium
March 7, 2023 - If you’re like me, you may be hearing a lot of hype about this new Polars library but are not sure what it is or how to get started. If you’re totally new to it, the simplest way to understand Polars is that it is a very fast alternative to the more traditional Pandas DataFrame library.
Polars
docs.pola.rs › user-guide › ecosystem
Ecosystem - Polars user guide
Hugging Face is a platform for working with machine learning datasets and models. Polars can be used to work with datasets downloaded from Hugging Face. A DataFrame can be transformed into a PyTorch format using to_torch or into a JAX format using to_jax.
Polars
docs.pola.rs › py-polars › html › reference › dataframe › api › polars.DataFrame.dtypes.html
polars.DataFrame.dtypes — Polars documentation
>>> df = pl.DataFrame( ... { ... "foo": [1, 2, 3], ... "bar": [6.0, 7.0, 8.0], ... "ham": ["a", "b", "c"], ...
Polars
docs.pola.rs › py-polars › html › reference › io.html
Input/output — Polars documentation
read_avro(source, *[, columns, n_rows]) · Read into a DataFrame from Apache Avro format