![]() 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. DBEAVER REVIEW CODEIf 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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