In January, I adopted a refined methodology for identifying the top pytest plugins by leveraging BigQuery for data collection. This new approach allows access to a comprehensive dataset of the top 200 plugins, enhancing the accuracy of the results. I am currently working on creating an exclusion list to filter out less relevant plugins. Additionally, documenting these process changes will help maintain consistency and efficiency in future updates, ultimately aiding users in learning pytest more effectively.
By utilizing BigQuery for data collection, I've enhanced the methodology for identifying top pytest plugins, aiming for a more accurate reflection of usage metrics.
The new process allows for a customizable exclusion list, giving me the flexibility to refine which plugins are included based on specific criteria.
Documenting the changes in this process will be crucial, ensuring that future updates are streamlined and based on the latest data practices.
Grabbing the top 200 pytest plugins will provide a comprehensive overview, making it easier to learn pytest efficiently and effectively.
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