вы можете использовать объединить , как это: (я адаптировал образец)
data1 = """
Sensor_ID Sensor_Street_Name Sensor_Lat Sensor_Long Sensor_Type UOM Time_Instant
14121 Milano-viaCarloPascal 45.478452 9.235016 Ammonia µg/m YYYY/MM/DD
17127 Milano-vialeMarche 45.496067 9.193023 Benzene µg/m YYYY/MM/DD_HH24:MI
17126 Milano-viaCarloPascal 45.478452 9.235016 Benzene µg/m YYYY/MM/DD_HH24:MI
6057 Milano-viaSenato 45.470780 9.197180 Benzene µg/m YYYY/MM/DD_HH24:MI
6062 Milano-P.zzaZavattari 45.476089 9.143509 Benzene µg/m YYYY/MM/DD_HH24:MI
"""
data2 = """
Sensor_ID,Time_Instant,Measurement
14121,2013-11-01 00:00:00,0.8
14121,2013-11-01 03:00:00,0.6
14121,2013-11-01 06:00:00,0.4
14121,2013-11-01 09:00:00,0.4
17127,2013-11-01 12:00:00,0
"""
import pandas as pd
df1 = pd.read_csv(pd.compat.StringIO(data1), sep='\s+')
df2 = pd.read_csv(pd.compat.StringIO(data2), sep=',')
s1 = pd.merge(df2, df1, how='left', on=['Sensor_ID'])
затем вы отбрасываете неиспользуемые столбцы из кадра данных s1 и переименовываете столбец Sensor_Type в Pollutants, а Time_Instant_x в Time_Instant
cols_to_delete = ['Sensor_Street_Name', 'Sensor_Lat','Sensor_Long','Time_Instant_y', 'UOM']
s1.drop(cols_to_delete, axis=1, inplace=True)
s1.rename(columns={'Time_Instant_x': 'Time_Instant', 'Sensor_Type': 'Pollutants'}, inplace=True)
с этим примером результата:
Sensor_ID Time_Instant Measurement Pollutants
0 14121 2013-11-01 00:00:00 0.8 Ammonia
1 14121 2013-11-01 03:00:00 0.6 Ammonia
2 14121 2013-11-01 06:00:00 0.4 Ammonia
3 14121 2013-11-01 09:00:00 0.4 Ammonia
4 17127 2013-11-01 12:00:00 0.0 Benzene