ranks = pd.read_csv("/tmp_file.csv")
print(ranks)
mask=(ranks["Date/Time"] > pd.Timestamp(start_time)) & (ranks["Date/Time"] < pd.Timestamp(end_time)) & (ranks["Op/sc"]>100)
df = ranks.loc[mask]
print(df)
df.replace(regex=r'2019.*$', value='2018', inplace=True)
print(df)

Date/Time        Rank  Op/sc
0 2019-03-18 03:07:57     0      6
1 2019-03-18 03:08:12     0      5
2 2019-03-18 03:08:27     0      4
3 2019-03-18 03:08:42     0      4
4 2019-03-18 03:08:57     0      7

Date/Time       Rank  Op/sc
25    2019-03-18 03:14:12     0    160
26    2019-03-18 03:14:27     0    103
27    2019-03-18 03:14:42     0    129
32    2019-03-18 03:15:57     0    119

Date/Time       Rank  Op/sc
25    2019-03-18 03:14:12     0    160
26    2019-03-18 03:14:27     0    103
27    2019-03-18 03:14:42     0    129
32    2019-03-18 03:15:57     0    119

ranks = pd.read_csv("/tmp_file.csv", parse_dates=['Date/Time'])