Combine Feature Groups into a single FeatureGroup | Teradata Package for Python - Combining one or more Feature Groups into a single FeatureGroup - Teradata VantageCloud Lake

You can combine multiple FeatureGroup objects using the + operator. Combining multiple FeatureGroup objects creates a new FeatureGroup object.

Let’s look at an example to see how this works.

Create two different FeatureGroups

Before creating two FeatureGroups, let’s look at two different data sets: patient_profile and medical_readings.

Patient Profile

>>> load_example_data('dataframe', 'patient_profile')
>>> patient_profile_df = DataFrame('patient_profile')
>>> patient_profile_df

                      record_timestamp  pregnancies  age   bmi  skin_thickness
patient_id                                                                    
17          2024-04-10 11:10:59.000000            7   31  29.6             0.0
34          2024-04-10 11:10:59.000000           10   45  27.6            31.0
13          2024-04-10 11:10:59.000000            1   59  30.1            23.0
53          2024-04-10 11:10:59.000000            8   58  33.7            34.0
11          2024-04-10 11:10:59.000000           10   34  38.0             0.0
51          2024-04-10 11:10:59.000000            1   26  24.2            15.0
32          2024-04-10 11:10:59.000000            3   22  24.8            11.0
15          2024-04-10 11:10:59.000000            7   32  30.0             0.0
99          2024-04-10 11:10:59.000000            1   31  49.7            51.0
0           2024-04-10 11:10:59.000000            6   50  33.6            35.0

Medical Readings

>>> load_example_data('dataframe', 'medical_readings')
>>> medical_readings_df = DataFrame('medical_readings')
>>> medical_readings_df

                      record_timestamp  glucose  blood_pressure  insulin  diabetes_pedigree_function  outcome
patient_id                                                                                                   
17          2024-04-10 11:10:59.000000      107              74        0                       0.254        1
34          2024-04-10 11:10:59.000000      122              78        0                       0.512        0
13          2024-04-10 11:10:59.000000      189              60      846                       0.398        1
53          2024-04-10 11:10:59.000000      176              90      300                       0.467        1
11          2024-04-10 11:10:59.000000      168              74        0                       0.537        1
51          2024-04-10 11:10:59.000000      101              50       36                       0.526        0
32          2024-04-10 11:10:59.000000       88              58       54                       0.267        0
15          2024-04-10 11:10:59.000000      100               0        0                       0.484        1
99          2024-04-10 11:10:59.000000      122              90      220                       0.325        1
0           2024-04-10 11:10:59.000000      148              72        0                       0.627        1
>>>

Create two FeatureGroups for the two datasets

Let's first create individual FeatureGroups.

>>> patient_profile_fg = FeatureGroup.from_DataFrame(
...    name='PatientProfile', 
...    df=patient_profile_df, 
...    entity_columns='patient_id', 
...    timestamp_col_name='record_timestamp'
)

>>> medical_readings_fg = FeatureGroup.from_DataFrame(
...    name='MedicalReadings', 
...    df=medical_readings_df, 
...    entity_columns='patient_id', 
...    timestamp_col_name='record_timestamp'
)

>>> print(patient_profile_fg.features)
[Feature(name=pregnancies), Feature(name=age), Feature(name=bmi), Feature(name=skin_thickness)]

>>> print(medical_readings_fg.features)
[Feature(name=glucose), Feature(name=blood_pressure), Feature(name=insulin), Feature(name=diabetes_pedigree_function), Feature(name=outcome)] 

Combine the two FeatureGroups

>>> new_fg = patient_profile_fg + medical_readings_fg 

Examine the combined FeatureGroup properties

>>> print(new_fg.name)
'PatientProfile_MedicalReadings'

>>> print(new_fg.features)
[Feature(name=pregnancies), Feature(name=age), Feature(name=bmi), Feature(name=skin_thickness), Feature(name=glucose), Feature(name=blood_pressure), Feature(name=insulin), Feature(name=diabetes_pedigree_function), Feature(name=outcome)]
>>> print(new_fg.entity)
Entity(name=PatientProfile_MedicalReadings)

>>> print(new_fg.data_source)
DataSource(name=PatientProfile_MedicalReadings)