You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Aug 17, 2024. It is now read-only.
Describe the bug
When data is loaded from a file locally, DataFrame treats all values as string types (empty string) due to which "missing values" are not differentiated/tracked, due to which fillMissingValues() doesn't work as expected.
However, this works when the DataFrame is prepared on the fly (using new DataFrame(...))
To Reproduce
Steps to reproduce the behavior:
Copy the content below to a file & save (for e.g. test.csv)
name,age
Adam,10
Amy,
Run the code below (assuming DataFrame dependency is added):
Describe the bug
When data is loaded from a file locally, DataFrame treats all values as string types (empty string) due to which "missing values" are not differentiated/tracked, due to which
fillMissingValues()
doesn't work as expected.However, this works when the DataFrame is prepared on the fly (using new DataFrame(...))
To Reproduce
Steps to reproduce the behavior:
Expected behavior
Second row should have been updated with
0
as the age.Screenshots
Desktop (please complete the following information):
Additional context
This works as expected when the data is prepared in-house, i.e.
Code:
Output:
The text was updated successfully, but these errors were encountered: