Tips and Tricks to Use Jupyter Notebooks Effectively

This page summarizes the projects mentioned and recommended in the original post on dev.to
Workflow Machine Learning Data Science data-engineering Jupyter

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  • ploomber

    The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

    • If you're looking to improve your Jupyter workflow, check out Ploomber's open-source projects: Ploomber for developing modular data pipelines, Soorgeon for refactoring and cleaning), or nbsnapshot for notebook testing.

  • soorgeon

    Convert monolithic Jupyter notebooks 📙 into maintainable Ploomber pipelines. 📊

    • If you're looking to improve your Jupyter workflow, check out Ploomber's open-source projects: Ploomber for developing modular data pipelines, Soorgeon for refactoring and cleaning), or nbsnapshot for notebook testing.

  • 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.

    InfluxDB logo

  • nbsnapshot

    Automated Jupyter notebook testing. 📙

    • If you're looking to improve your Jupyter workflow, check out Ploomber's open-source projects: Ploomber for developing modular data pipelines, Soorgeon for refactoring and cleaning), or nbsnapshot for notebook testing.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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