tidypolars
polars
Our great sponsors
tidypolars | polars | |
---|---|---|
7 | 144 | |
309 | 26,218 | |
- | 6.1% | |
8.0 | 10.0 | |
3 months ago | about 2 hours ago | |
Python | Rust | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tidypolars
-
Modern Polars: a side-by-side comparison with Pandas
I recommend trying tidypolars
-
Modern Polars: an extensive side-by-side comparison of Polars and Pandas
There’s a tidypolars package that appears to be well-maintained https://github.com/markfairbanks/tidypolars
-
R Tidyverse / dplyr is life changing!
tidypolars is one I’ve seen. Still very new, but it’s built on top of polars (which is a little more like dplyr to begin with), so it’s much faster than pandas.
-
Introducing tidypolars - a Python data frame package with syntax familiar to R tidyverse users
If I misunderstood your question - feel free to open a discussion with a small code example and we can talk through how you can do it in tidypolars.
- Introducing tidypolars - a Python data frame package for R tidyverse users
-
Tidyverse appreciation thread
Try out tidypolars. It's really close to tidyverse syntax and it's a lot faster than pandas as well
polars
-
Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
-
Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
-
Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
tidytable - Tidy interface to 'data.table'
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
dtplyr - Data table backend for dplyr
modin - Modin: Scale your Pandas workflows by changing a single line of code
db-benchmark - reproducible benchmark of database-like ops
datafusion - Apache DataFusion SQL Query Engine
extendr - R extension library for rust designed to be familiar to R users.
DataFrames.jl - In-memory tabular data in Julia
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
datatable - A Python package for manipulating 2-dimensional tabular data structures
pandoc - Universal markup converter