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When parsing date and time from separate columns the information contained in the raw data is used to generate a datetime object. It is common that only partial information is available in certain rows (e.g., a date is available without a time). This results in missing values (NaT). Since the original columns containing the information are not returned in the dataset, a significant part of the information is lost.
Functions with this issue: get_qwdata, get_gwlevels.
To Reproduce
These are two examples from the wto functions mentioned above:
get_qwdata. Run:
site_id = "04024000"
data = nwis.get_qwdata(sites=site_id)
df = data[0]
Explore df and observe the missing (NaT) values in the index.
Click here to see the raw data from this query (notice there are no missing dates here)
get_gwlevels. Run:
site_id = "375907091432201"
data = get_gwlevels(sites=site_id)
df = data[0]
Explore df and observe the missing (NaT) values in the index.
Click here to see the raw data from this query (notice there are no missing dates here)
Expected behavior
For all these missing values, in both functions, there is a date value available in the raw data queried. The output dataframe should contain all original columns to avoid losing data.
The text was updated successfully, but these errors were encountered:
Issue
When parsing date and time from separate columns the information contained in the raw data is used to generate a datetime object. It is common that only partial information is available in certain rows (e.g., a date is available without a time). This results in missing values (NaT). Since the original columns containing the information are not returned in the dataset, a significant part of the information is lost.
Functions with this issue: get_qwdata, get_gwlevels.
To Reproduce
These are two examples from the wto functions mentioned above:
get_qwdata. Run:
site_id = "04024000"
data = nwis.get_qwdata(sites=site_id)
df = data[0]
Explore df and observe the missing (NaT) values in the index.
Click here to see the raw data from this query (notice there are no missing dates here)
get_gwlevels. Run:
site_id = "375907091432201"
data = get_gwlevels(sites=site_id)
df = data[0]
Explore df and observe the missing (NaT) values in the index.
Click here to see the raw data from this query (notice there are no missing dates here)
Expected behavior
For all these missing values, in both functions, there is a date value available in the raw data queried. The output dataframe should contain all original columns to avoid losing data.
The text was updated successfully, but these errors were encountered: