I have a significant amount of experience with both of them. For pandas, it is fine for initial exploratory analysis (input, plot, reshape and export, etc). How

I have a significant amount of experience with both of them. For pandas, it is fine for initial exploratory analysis (input, plot, reshape and export, etc). However, it's API has inconsistencies and subtle "features" around silent data coercion that make it hard to use in production. Seaborn is much nicer to work with than matplotlib and Jupyter is useful for making interactive presentations. So I believe Pandas/Jupyter have a place, but then there was a tendency to create a Pandas-like wrapper for all data retrieval such as: https://github.com/ibis-project/ibis https://github.com/ibis-project/ibis https://github.com/blaze/blaze https://github.com/blaze/blaze