One has probably come across this problem numerous times. There are two versions of a tabular data set with a lot of columns of different types, and one wants to quickly identify any differences between the two. For example, the pipeline providing data to a predictive model might have been updated, and the goal is to understand if there have been any side effects of this update for the training data.

One solution is to start to iterate over the columns of the two tables, computing five-number summaries and plotting histograms or identifying distinct values and plotting bar charts, depending on the column’s type. However, this can quickly get out of hand and evolve into an endeavor for the rest of the day.

An alternative is to leverage the amazing tools that already exist in the data community.


The key takeaway is the following three lines of code, excluding the import:

import tensorflow_data_validation as dv

statistics_1 = dv.generate_statistics_from_dataframe(data_1)
statistics_2 = dv.generate_statistics_from_dataframe(data_2)

This is all it takes to get a versatile dashboard embedded right into a cell of a Jupyter notebook. The visualization itself is based on Facets, and it is conveniently provided by TensorFlow Data Validation (which does not have much to do with TensorFlow and can be used stand-alone).

It is pointless to try to describe in words what the dashboard can do; instead, here is a demonstration taken from Facets where the tool is applied the UCI Census Income data set:

Go ahead and give a try to all the different controls!

In this case, it is helpful to toggle the “percentages” checkbox, since the data sets are of different sizes. Then it becomes apparent that the two partitions are fairly balanced. The only problem is that Target, which represents income, happened to be encoded incorrectly in the partition for testing.

Lastly, an example in a Jupyter notebook can be found on GitHub.


It can be difficult to navigate and particularly challenging to compare wide data sets. A lot of effort can be put into this exercise. However, the landscape of open-source tools has a lot to offer too. Facets is one such example. The library and its straightforward availability via TensorFlow Data Validation are arguably less known. This short note can hopefully rectify this to some extent.