Alternatives and similar repositories for pytest_examples:

Users that are interested in pytest_examples are comparing it to the libraries listed below

• canyon289 / PyTestforDataScience_PyDataLA
  Pytest for Data Science Beginners
  ☆58Updated 6 years ago
• tgoldenberg / kedro-mlflow-example
  ☆43Updated 2 years ago
• Bergvca / pyspark_dist_explore
  Data Exploration in PySpark made easy - Pyspark_dist_explore provides methods to get fast insights in your Spark DataFrames.
  ☆103Updated 5 years ago
• klemag / pydataLDN_2019-maintainable-code-for-data-science
  ☆47Updated 5 years ago
• trainindata / testing-and-monitoring-ml-deployments
  Example project for the course "Testing & Monitoring Machine Learning Model Deployments"
  ☆134Updated last year
• trallard / airflow-tutorial
  🐍💨 Airflow tutorial for PyCon 2019
  ☆86Updated 2 years ago
• priceloop / conventions
  ∞ Priceloop Engineering Conventions for Scala, Python, Git Workflow etc
  ☆101Updated 2 years ago
• harnav / pydata-docker-tutorial
  Tutorial given at PyData LA 2018
  ☆97Updated 7 months ago
• drivendataorg / nbautoexport
  Automatically export Jupyter notebooks to various file formats (.py, .html, and more) on save.
  ☆77Updated last year
• marcosmarxm / airflow-testing-ci-workflow
  (project & tutorial) dag pipeline tests + ci/cd setup
  ☆85Updated 4 years ago
• SauceCat / pydqc
  python automatic data quality check toolkit
  ☆283Updated 4 years ago
• tamsanh / kedro-great
  The easiest way to integrate Kedro and Great Expectations
  ☆53Updated 2 years ago
• bgweber / DS_Production
  ☆154Updated 4 years ago
• monte-carlo-data / data-downtime-challenge
  ☆84Updated 2 years ago
• great-expectations / great_expectations_action
  A GitHub Action that makes it easy to use Great Expectations to validate your data pipelines in your CI workflows.
  ☆80Updated 11 months ago
• whiteboxml / teaching-ml-in-production
  ☆54Updated 2 years ago
• eugeneyan / papermill-mlflow
  🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.
  ☆180Updated 9 months ago
• cambridgespark / pydata-testing-for-data-science
  ☆25Updated 9 months ago
• ericmjl / data-testing-tutorial
  A short tutorial for data scientists on how to write tests for code + data.
  ☆119Updated 4 years ago
• tamsanh / kedro-introduction-tutorial
  It's the Complete Beginner's Guide to Kedro! See the video here: https://youtu.be/x97ChYDd12U
  ☆22Updated 3 years ago
• mzjp2 / kedro-dataframe-dropin
  A Kedro plugin that provides pandas dropin replacements for the pandas datasets (e.g modin and cuDF)
  ☆12Updated 4 years ago
• amueller / dabl
  Data Analysis Baseline Library
  ☆131Updated 5 months ago
• StevenMMortimer / ge-sklearn-pipeline-example
  Example using Great Expectations to Validate Data in a scikit-learn Pipeline
  ☆21Updated 4 years ago
• mlflow / mlp-regression-template
  Example repo to kickstart integration with mlflow pipelines.
  ☆76Updated 2 years ago
• ianozsvald / dtype_diet
  Tries to shrink your Pandas column dtypes with no data loss so you have more spare RAM
  ☆84Updated last year
• tdda / tdda
  Test-Driven Data Analysis Functions
  ☆298Updated last week
• schmidtbri / simple-ml-model-abc
  Code demonstrating a simple Machine Learning model abstract base class and its uses.
  ☆14Updated last year
• ColtAllen / btyd
  Buy Till You Die and Customer Lifetime Value statistical models in Python.
  ☆116Updated 11 months ago
• ZaxR / bulwark
  Bulwark is a package for convenient property-based testing of pandas dataframes.
  ☆224Updated 4 years ago
• deliveryhero / pyconde2019-airflow-ml-workshop
  PyConDE & PyData Berlin 2019 Airflow Workshop: Airflow for machine learning pipelines.
  ☆47Updated last year