Comments (2)
nguyen-tien-tung
commented on August 21, 2024
take
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a-theron
commented on August 21, 2024
After reviewing the documentation, I think this might not be a bug.
I think it is a result of how Pandas handles none types.
In Panda's none type documentation it states
pandas uses different sentinel values to represent a missing (also referred to as NA) depending on the data type.
Which means that the different none types (None, NaN, NaT) are used depending on the dtype of the column.
So when a column only has missing values (as shown in the "bug"), Pandas has no reference dtypes so all missing values are represented by the default (NaN) none type.
So correct me if I am wrong, but I believe this might actually part of the Panda's feature set, rather than a bug.
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