Frequently Asked Questions¶

Installation¶

How do I install atoti?¶

Everything is explained in the installation guide.

Which versions of Python does atoti support?¶

Python >= 3.7.0

Features¶

Going to production¶

Can I run it without the notebook?¶

Yes, notebooks can be converted to executable Python scripts with nbconvert.

Comparison with other tools¶

What is the benefit of atoti compared to Pandas?¶

atoti stores can handle more data than Pandas DataFrame. We have experienced low performance with Pandas starting with 4GB datasets while atoti scales more efficiently.
Using atoti, you can build an advanced data model using joins between stores. The data is not duplicated like when you perform a merge in Pandas.
atoti has embedded interactive data visualization tools to give you quick insights.
Building scenarios is easy: once your model is defined, you can compare several versions of your data while in Pandas you would have to re-apply all the transformations on each dataset.

Does atoti replace Pandas or Spark?¶

Not really, atoti is meant to be integrated in an environment with Pandas or Spark. We think they are very good tools to clean and transform the data while atoti is made for analysis and visualization. We have actually built connectors to load DataFrames from Pandas or Spark into atoti stores.

What is the benefit of atoti compared to Pyplot or Matplotlib?¶

No need to write code to define your graph.
The view is not frozen: you can build a dashboard and share it with other users who can still interact with it (for example by adding filters).

What is the benefit of atoti compared to Tableau or Qlik?¶

Interactivity: you don’t have to export the data and load it into another software. With atoti, all the measures are defined in Python, adding a new measure or more data is easy.
Performance: when visualizing large datasets, atoti will still answer very quickly.