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Description
Description
prepare_measurement_table returns error
MissingConceptError: The DataFrame is missing some columns, namely:
- measurement_date
there is often issues with "date columns" in spark + Pandas. We should only use measurement_datetime column.
Solution : delete measurement_date in variable "_measurement_required_columns" in utils.check_data.check_data_and_select_columns_measurement.
How to reproduce the bug
prepare_measurement_table issue
import eds_scikit
from eds_scikit.biology import prepare_measurement_table, ConceptsSet
from eds_scikit.io import HiveData
data = HiveData(MyDB)
leukocytes_set = ConceptsSet("Leukocytes_Blood_Count")
measurement = prepare_measurement_table(
data,
start_date="2022-01-01",
end_date="2022-05-01",
concept_sets=[leukocytes_set],
convert_units=False,
get_all_terminologies=True,
)date columns issue
sql("SELECT measurement_date FROM measurement limit 10").toPandas()returns : "AttributeError: Can only use .dt accessor with datetimelike values"
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