pandera
objectiv-analytics
Our great sponsors
| pandera | objectiv-analytics | |
|---|---|---|
| 7 | 22 | |
| 3,007 | 469 | |
| 5.2% | - | |
| 9.1 | 0.0 | |
| 3 days ago | over 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
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
objectiv-analytics
- Get tools to test, validate and debug your tracking instrumentation → Set up error-free user behavior tracking → No more missing/faulty data downstream.
- Open your python notebook → Work directly on super-structured raw data that's designed for modeling → Build analytics models without data prepwork.
- Open your python notebook → Take pre-built analytics models (or build your own) → Turn them into SQL with one command → Build BI dashboards in minutes.
- Run product analytics from your notebook with full control over data & models.
- Powerful product analytics for data teams. Objectiv is a data collection & modeling platform. Built for data teams to run product analytics from their notebooks with full control over data and models.
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Open-source data collection & modeling platform for product analytics
Hope you like it. Check out the project on GitHub, and please do give us a star so others can find us too! https://github.com/objectiv/objectiv-analytics
- The complete toolkit for product analytics modeling. Notebook-native, open-source and free to use.
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A community developing a Hugging Face for customer data modeling
One of the creators of Objectiv here. Would like to get your opinion on our community-driven open-source project, which aims to do for customer data modeling what Hugging Face did for NLP: https://github.com/objectiv/objectiv-analytics
- Objectiv is ready-to-use infrastructure for advanced product analytics
What are some alternatives?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
zillion - Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/
Schematics - Python Data Structures for Humans™.
pandas-paddles - Simple, composable column selector for loc[], iloc[], assign() and others.
jsonschema - An implementation of the JSON Schema specification for Python
logos-shift-client - Replace expensive LLM calls with finetunes automatically
pointblank - Data quality assessment and metadata reporting for data frames and database tables
data-diff - Compare tables within or across databases
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Restaurant-Monitoring-System - Backend system for restaurant management
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
Snowplow - The enterprise-grade behavioral data engine (web, mobile, server-side, webhooks), running cloud-natively on AWS and GCP