You can even publish the notebook to an internal sharing platform like knowledge-repo for wider consumption. Once the changes are reviewed, you can resolve the discussion and share your insight with the team. Notebook diffs on GitHub are typically very noisy but ReviewNB shows human readable rich diffs for Notebooks as shown above. Once you have made changes to the notebook, you can review the diff side by side. As soon as your teammate makes a suggestion, you get an email notification & can act on it. They can review changes and make comments on the web without the need to pull code at all. This lowers the technical barrier as your analysts do not have to understand Git. Step 1 - Review PR onlineĪnyone can view the notebook and add comments on a particular notebook cell via ReviewNB. Jupyter notebooks act as documentation, presentation and collaboration tool for your analysis. This kind of workflow is not possible with just the SQL script or a screenshot of your finding. They can share their feedback directly on the Jupyter Notebook cells. With ReviewNB, you can publish your result and invite a teammate to review your analysis. Luckily, there are efforts that make collaboration in Jupyter Notebooks a lot easier now. Notebooks have an infamous reputation that they’re difficult to version control or to collaborate with. You can easily share notebooks with non-technical stakeholders as well. Jupyter Notebook is flexible and fits extremely well with exploratory data analysis. barplot ( x = 'ProductId', y = 'Unit', data = sales_df ) ax. groupby ( 'ProductId', as_index = False ). Import seaborn as sns sales = % sql SELECT * FROM SALES sales_df = sales. To do this, you need to use the magic function with the inline magic % or cell magic %%. You can make use of the ipython_sql library to make queries in a notebook. No one will be able to reproduce the dataset after 6 months. In contrast, if your analysis is reading data from an anonymous exported CSV, it is almost guaranteed that the definition of the data will be lost. ![]() If you find bugs in your code, you can modify the code and re-run the analysis. It allows you to generate analysis with richer content.
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