You Will NEVER Use Pandas’ Describe Method After Using These Two Libraries
Generate a comprehensive data summary in seconds.
Probably the first (or second) thing I do when I load any Pandas or Polars DataFrame is describe it, using the
However, I always find its output to be pretty naive and almost of no use. In other words, it hardly highlights any key information about the data.
But some time back, I came across two pretty cool libraries that IMMENSELY supercharge this DataFrame summary.
Since then, I don’t think I have ever used the
Let me introduce you to them today.
The first one is Skimpy.
It is a Jupyter-based tool that provides a standardized and comprehensive data summary.
This includes data shape, column data types, column summary statistics, distribution charts, missing stats, etc., as shown below:
What’s more, the summary is grouped by datatypes for faster analysis.
This is the code to use Skimpy:
One thing I really love about Skimpy is that it works seamlessly with Polars, which I have started using more often than I use Pandas these days.
The second one is SummaryTools, which does almost the exact same thing as Skimpy, i.e., it generates a standardized report:
This is the code to use SummaryTools:
Two pretty cool things about SummaryTools are that it can create:
A collapsible summary of the dataset, as illustrated below:
A tabbed summary of the dataset, as shown below:
The only thing I don’t like about SummaryTools is that it is not compatible with Polars (yet).
Nonetheless, I find both of them extremely promising for understanding my dataset with more granularity than Pandas’
Aren’t they interesting?
👉 Over to you: What other cool Pandas-related tools are you aware of?
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