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validation errors Extra(nan) or Invalid(nan) #49
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I've been looking at this closely and discovered a handful of un-handled corner cases related to As a stopgap, you could do the following:
Going forward, I will file a related issue/bug for this with the goal of allowing the use of NaN values directly:
I'll post a follow-up to this issue once I have patched this behavior. |
Thanks. I will follow this. |
This is done: |
@upretip, I've just pushed some new "how to" docs that give detail regarding NaN validation and behavior. You can view it in the latest docs here:
|
Thanks for the help. Closing this issue now! |
Shaun,
I am trying your package to see if I can validate a csv file by reading it in pandas. I am getting Extra(nan)
dt.validate.superset()
or Invalid(nan)dt.validate()
. Is there a way I can include thosenan
in my validation sets?Error looks like
Note: I am reading this particular column as
str
Let me know if you find a solution or can help me debug
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