A Preliminary Analysis on the Use of Python Notebooks

The adoption of Python notebooks to perform data analysis has considerably increased, becoming a de-facto standard within data scientists communities.

Notebooks are interactive human-readable documents, which contain the analysis description and the results (e.g, figures, tables) as well as the Python code used to perform data analysis. Thus, notebooks help scientists to both keep track of the results of their work, and make it easy to share the code with others.

Inspired by this blog post about the most useful open source python libraries for data analytics in 2017, where in addition of libraries description and category, the author gave insights about how popular these libraries are, using metrics such as number of commits and contributors extracted mainly from their home repository in Github, we are going to run some analysis from different perspectives.

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Making community managers life easier

In Bitergia we have a list of  happy customers using the Metrics Grimoire toolset (fork them at GitHub!) to produce metrics about their communities. Tracking tech communities is not that simple and this needs of some infrastructure. And one of the main issues usually consists of aggregating all of the information.

  • How to have aggregated information for a given project from several data sources?
  • How to aggregate information from a specific developer from several data sources?
  • How to aggregate information for a given company from several data sources?
  • How to manage the several identities (IRC nickname, Jira user name, …) across data sources of a developer?
  • And what about managing the several affiliations of a developer?

And even more, is there a place where I can easily have a glimpse and check how my community is going?

The following is an example of the OPNFV community where the Git repositories, Gerrit projects, tickets from Jira, mailing lists, IRC channels and the Askbot instance is summarized in the entry page of the OPNFV dashboard.


The Bitergia toolset covers all of these issues with the retrieval of raw information, cleaning and massaging of the data and visualization. Indeed any of these steps are fully independent, what helps you to add any of your favourite tools in any of the several steps.

Let’s imagine that you’re interested in using your favourite visualization tool to play with the data. You can have direct access to the databases or to the post-processed data. It’s your data and Bitergia worries about providing a trustable service where all of the tools and data are open source.

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