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Top 23 polar Open-Source Projects
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Mimesis
Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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DataFrame
C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
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functime
Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.
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time-series-streaming-analytics-template
Template to quickstart streaming analytics using Apache Kafka for ingestion, QuestDB for time-series storage and analytics, Grafana for near real-time dashboards, and Jupyter Notebook for data science
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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-...).
Project mention: Show HN: Hashquery, a Python library for defining reusable analysis | news.ycombinator.com | 2024-04-23I really don't understand the appeal of dbt vs a proper programming language. The templating approach leads to massive spaghetti. I look forward to trying out something like Ibis [0]
0: https://ibis-project.org/
Project mention: New multithreaded version of C++ DataFrame was released | news.ycombinator.com | 2024-02-13
Thanks for the detailed feedback @snidane!
As maintainer of qsv, here's my reply:
- Given qsv's rapid release cycle (173 releases over three years), the auto-update check is essential at the moment. Once we reach 1.0, I'll turn it off. For now, given your feedback, I've only made it check 10% of the time.
- Pivot is in the backlog and I'll be sure to add unpivot when I implement it. (https://github.com/jqnatividad/qsv/issues/799)
- I'll add a dedicated summing command with the group by (-by) and window by (-over) capability (https://github.com/jqnatividad/qsv/issues/1514). Do note that `stats` has basic sum as @ezequiel-garzon pointed out.
- With the `enum` command, qsv can achieve what you proposed with `laminate`. E.g. qsv enum --new-column newcol --constant newconstant mydata.csv --output laminated-data.csv
- With the cat rowskey command, qsv can already concatenate files with mismatched headers.
- other file formats. qsv supports parquet, csv, tsv, excel, ods, datapackage, sqlite and more (see https://github.com/jqnatividad/qsv/tree/master#file-formats). Fixed-format though is not supported yet and quite interesting, and have added it to the backlog (https://github.com/jqnatividad/qsv/issues/1515)
- as to "enable embedding outputs of commands", qsv is composable by design, so you can use standard stdin/stdout redirection/piping techniques to have it work with other CLI tools like jq, awk, etc.
Finally, just released v0.120.0 that already incorporates the less aggressive self-update check. https://github.com/jqnatividad/qsv/releases/tag/0.120.0
Project mention: Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor) | news.ycombinator.com | 2023-12-06Hey, HN community!
We're stoked to launch JupySQL today! JupySQL is an open-source library that brings a modern SQL experience to Jupyter. JupySQL is compatible with all major databases, such as Snowflake, Redshift, PostgreSQL, MySQL, MariaDB, DuckDB, SQL Server, Clickhouse, Trino, and more!
To get started, check out our tutorial: https://jupysql.ploomber.io/en/latest/quick-start.html
SQL is the defacto language for data analysis; however, analysis often requires a mix of SQL and Python. JupySQL bridges this gap, allowing users to execute SQL queries seamlessly in Jupyter and continue their analysis in Python. Add %%sql to the top of your cell and start writing SQL.
Here are some of JupySQL's main features:
- Syntax highlighting
Project mention: 👉 New Awesome Polars release! What's new in the world of Polars in June 2023 ? Let's find out! 🚀 | /r/dataengineering | 2023-06-28
Polars: A blazingly fast DataFrame library written in Rust for data manipulation and analysis
Rust still has some key pieces missing, but looks promising, see: https://github.com/wiseaidev/rust-data-analysis
F# has a very decent data community: https://datascienceinfsharp.com
And obviously Julia is also something to consider.
See the full examples here: https://github.com/pola-rs/pyo3-polars/tree/main/example/derive_expression
Project mention: Business day arithmetic in Polars is...easy! Just use the `polars-business` plugin | /r/datascience | 2023-11-14
Project mention: Show HN: Snowflake Data Quality Checks in Python | news.ycombinator.com | 2024-02-11
Project mention: Show HN: Open-source template for end-to-end streaming analytics | news.ycombinator.com | 2024-02-08
I have added documentation for all supported functions here.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
polars related posts
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Polars R Package
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Using Deno with Jupyter Notebook to build a data dashboard
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👉 New Awesome Polars release! What's new in the world of Polars in June 2023 ? Let's find out! 🚀
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👉 New Awesome Polars release! What's new in the world of Polars in June 2023 ? Let's find out! 🚀
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👉 New Awesome Polars release! What's new in the world of Polars in the last 3 weeks ? A polars-df gems to use Polars with Ruby. 🚀
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👉 New Awesome Polars release! What's new in the world of Polars in the last 3 weeks ? Let's find out! 🚀
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👉 New Awesome Polars release (04-21-2023) ! 🚀 What's new in #Polars? Let's find out!
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A note from our sponsor - SaaSHub
www.saashub.com | 9 May 2024
Index
What are some of the best open-source polar projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | polars | 26,378 |
| 2 | Mimesis | 4,309 |
| 3 | ibis | 4,241 |
| 4 | DataFrame | 2,280 |
| 5 | qsv | 2,234 |
| 6 | functime | 914 |
| 7 | jupysql | 610 |
| 8 | awesome-polars | 598 |
| 9 | geopolars | 487 |
| 10 | datacompy | 394 |
| 11 | r-polars | 390 |
| 12 | nodejs-polars | 313 |
| 13 | rust-data-analysis | 284 |
| 14 | rust-mlops-template | 273 |
| 15 | pyo3-polars | 198 |
| 16 | polars-xdt | 153 |
| 17 | biobear | 122 |
| 18 | cuallee | 107 |
| 19 | s2protocol-rs | 102 |
| 20 | fastexcel | 72 |
| 21 | time-series-streaming-analytics-template | 44 |
| 22 | lightning-mlflow-hf | 44 |
| 23 | dply-rs | 38 |
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