How to join many fragmented time series in one regular Pandas DataFrame in Python

First join both DataFrames together by concat with DataFrame.set_index and if possible duplicates use sum for unique MultiIndex created by timestamps and Sensors.

Then add missing rows with DataFrame.reindex by MultiIndex.from_product with minumal and maximal dates by date_range:

df = (pd.concat([df_a.set_index(['Timestamp','Sensor']), 
                df_b.set_index(['Timestamp','Sensor'])], sort=True)
        .sum(level=[0,1],min_count=1))

d = df.index.get_level_values(0)
mux = pd.MultiIndex.from_product([pd.date_range(d.min(), d.max(), freq='5Min'), 
                                  df.index.get_level_values(1).unique()], names=df.index.names)
df = df.reindex(mux).reset_index()
print (df)

             Timestamp    Sensor  Humidity  Pressure  Temperature
0  2019-05-25 10:00:00  Sensor_1      60.0       NaN         25.0
1  2019-05-25 10:00:00  Sensor_2      45.0       NaN         30.0
2  2019-05-25 10:00:00  Sensor_3       NaN       NaN          NaN
3  2019-05-25 10:05:00  Sensor_1       NaN    1020.0         26.0
4  2019-05-25 10:05:00  Sensor_2      46.0     956.0         30.0
5  2019-05-25 10:05:00  Sensor_3       NaN     990.0          NaN
6  2019-05-25 10:10:00  Sensor_1      63.0    1021.0         27.0
7  2019-05-25 10:10:00  Sensor_2       NaN     957.0          NaN
8  2019-05-25 10:10:00  Sensor_3       NaN     992.0          NaN
9  2019-05-25 10:15:00  Sensor_1       NaN    1019.0          NaN
10 2019-05-25 10:15:00  Sensor_2       NaN       NaN          NaN
11 2019-05-25 10:15:00  Sensor_3       NaN       NaN          NaN
12 2019-05-25 10:20:00  Sensor_1      62.0       NaN         28.0
13 2019-05-25 10:20:00  Sensor_2       NaN       NaN          NaN
14 2019-05-25 10:20:00  Sensor_3       NaN       NaN          NaN