swifter
pandera
| swifter | pandera | |
|---|---|---|
| 3 | 7 | |
| 2,468 | 3,012 | |
| - | 2.5% | |
| 5.5 | 9.1 | |
| about 2 months ago | 4 days ago | |
| Python | Python | |
| 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.
swifter
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Tidyverse equivalent in Python?
With concat, merge, melt, and pivot_table, that may cover everything I have ever needed. There may be more efficient ways at times, but swifter promises to do that for you, maybe it is true.
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[D] A hacky work-around for slow linear algebra operations on pyspark.
Since you already have a working python script, you can try swifter with minimal effort to see if it brings about a significant speedup before digging further.
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What Is The Best Performance Fix You Ever
With few lines of code? Swifter for quicker pandas apply and then there's numba. With concurrent.futures, it'll be a bit more lines of code.
pandera
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Unit testing functions that input/output dataframes?
I use Pandera, so I just need to define the expected input/output schemas (i.e. column names, types, and constraints on them), and Pandera automatically generates fake data for the unit tests, and validates the result: https://github.com/unionai-oss/pandera
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Great Expectations is annoyingly cumbersome
Please DM me! Or we can discuss in this issue which I just created: https://github.com/unionai-oss/pandera/issues/1042
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Data validation for dashboards
In my opinion for simple data validation tasks the best solution is always Pandera.
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Show HN: Pandera 0.8.0 – validate pandas, dask, modin, and koalas dataframes
* adds support for mypy static type-linting if you need that extra type safety
Repo: https://github.com/pandera-dev/pandera
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Pandera 0.8.0: Schema Validation for Pandas, Dask, Modin, and Koalas DataFrames. Oh, and also out-of-the-box Pydantic and Mypy support :)
Repo: https://github.com/pandera-dev/pandera
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How heavily do you use Great Expectations?
pandera
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
Dask - Parallel computing with task scheduling
Schematics - Python Data Structures for Humans™.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
jsonschema - An implementation of the JSON Schema specification for Python
siuba - Python library for using dplyr like syntax with pandas and SQL
pointblank - Data quality assessment and metadata reporting for data frames and database tables
xarray - N-D labeled arrays and datasets in Python
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
xgboost_ray - Distributed XGBoost on Ray
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.