Ploomber is a fantastic tool for data manipulation and generating analytical reports.

graph LR la[Load dataset A] --> ca[Clean dataset A] lb[Load dataset B] --> cb[Clean dataset B] ca --> m[Merge] --> p[Generate report] cb --> m
Example: BigQuery and Cloud Storage pipeline
pip install ploomber
ploomber examples -n templates/google-cloud -o google-cloud
Example: Exploratory data analysis pipeline
pip install ploomber
ploomber examples -n templates/exploratory-analysis -o exploratory-analysis

Modularize your project

Instead of coding everything in a single notebook (which is difficult to maintain and collaborate), you can quickly break down your analysis into multiple parts.

Faster iterations

Finding data insights is an iterative process, with Ploomber’s incremental builds you can rapidly iterate on your data since the framework skips redundant computations and only executes tasks whose source code has changed since the last execution.

Automated report generation

Once your pipeline is ready, you can easily create HTML reports from your scripts/notebooks. Just change the extension of the task, and Ploomber will automatically convert the output for you.

  - source: tasks/
    # execute your .py file and generate an .html version of it
    # all tables and charts are included
    product: output/report.html