New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Support updating the underlying Series in DataColumn #170

Closed

gsheni opened this issue Sep 29, 2020 · 0 comments · Fixed by #233

Assignees

Labels

Contributor

gsheni commented Sep 29, 2020

Currently, we support changing the underlying dtypes in DataTables. So we cast to the inferred/provided Logical Type.
However, we don't support this functionality at the DataColumn Level

import pandas as pd 
import woodwork as ww

dc = ww.DataColumn(pd.Series(["2020-09-10", "2020-01-10 00:00:00", "01/01/2000 08:30"]), logical_type='DateTime')
# series does not have datetimes, did not do `as_type("datetime")`
dc.series

DateTable's changing underlying dtype of Series

data = pd.DataFrame({'time': pd.Series(["2020-09-10", "2020-01-10 00:00:00", "01/01/2000 08:30"], dtype="string")})
dt = ww.DataTable(data, logical_types={'time': 'Datetime'})
# series has correct datetime values
dt['time'].series.dtype
dt['time'].series

gsheni added the new feature label

gsheni assigned thehomebrewnerd

thehomebrewnerd mentioned this issue

Update dtype on DataColumn object instead of DataTable #233

Merged

thehomebrewnerd closed this as completed in #233

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment