2002074266              2002074444
TimeStamp   Total        Freq       Total       Freq
HH:MM        Pc           Hz         Pc          Hz
06:50        3            10         7           13
06:55        4            11         8           14
07:00        5            12         9           15

df['TimeStamp'] = pd.to_datetime(df['TimeStamp'])
df = df.set_index('TimeStamp')

df = pd.read_csv("Equipment.csv", header=[0,1], skiprows = 3, index_col=0)

df = pd.read_csv("Equipment.csv", header=[0,1], skiprows = [2], index_col=0)

print (df)
      2002074266      2002074444
           Total Freq      Total Freq
06:50          3   10          7   13
06:55          4   11          8   14
07:00          5   12          9   15

df.index = pd.to_datetime(df.index, format='%H:%M')
df = df.resample('1min').ffill()
print (df)
                    2002074266      2002074444
                         Total Freq      Total Freq
1900-01-01 06:50:00          3   10          7   13
1900-01-01 06:51:00          3   10          7   13
1900-01-01 06:52:00          3   10          7   13
1900-01-01 06:53:00          3   10          7   13
1900-01-01 06:54:00          3   10          7   13
1900-01-01 06:55:00          4   11          8   14
1900-01-01 06:56:00          4   11          8   14
1900-01-01 06:57:00          4   11          8   14
1900-01-01 06:58:00          4   11          8   14
1900-01-01 06:59:00          4   11          8   14
1900-01-01 07:00:00          5   12          9   15

df.index = pd.to_timedelta(df.index + ':00')
df = df.resample('1min').ffill()
print (df)
         2002074266      2002074444
              Total Freq      Total Freq
06:50:00          3   10          7   13
06:51:00          3   10          7   13
06:52:00          3   10          7   13
06:53:00          3   10          7   13
06:54:00          3   10          7   13
06:55:00          4   11          8   14
06:56:00          4   11          8   14
06:57:00          4   11          8   14
06:58:00          4   11          8   14
06:59:00          4   11          8   14
07:00:00          5   12          9   15