I work with Cloudera VM 5.2 and pandas 0.18.0.

I have the following data

```
adclicksDF = pd.read_csv('/home/cloudera/Eglence/ad-clicks.csv',
               parse_dates=['timestamp'],
       skipinitialspace=True).assign(adCount=1)

adclicksDF.head(n=5)
Out[107]: 
            timestamp  txId  userSessionId  teamId  userId  adId   adCategory  \
0 2016-05-26 15:13:22  5974           5809      27     611     2  electronics   
1 2016-05-26 15:17:24  5976           5705      18    1874    21       movies   
2 2016-05-26 15:22:52  5978           5791      53    2139    25    computers   
3 2016-05-26 15:22:57  5973           5756      63     212    10      fashion   
4 2016-05-26 15:22:58  5980           5920       9    1027    20     clothing   

   adCount  
0        1  
1        1  
2        1  
3        1  
4        1  

```

Datatype fields are

```
for col in adclicksDF:
    print(col)
    print(type(adclicksDF[col][1]))

timestamp
<class 'pandas.tslib.Timestamp'>
txId
<class 'numpy.int64'>
userSessionId
<class 'numpy.int64'>
teamId
<class 'numpy.int64'>
userId
<class 'numpy.int64'>
adId
<class 'numpy.int64'>
adCategory
<class 'str'>
adCount
<class 'numpy.int64'>

```

I want to truncate minutes and seconds in Timestamp.

I tried 

```
adclicksDF["timestamp"] = pd.to_datetime(adclicksDF["timestamp"],format='%Y-%m-%d %H')

adclicksDF.head(n=5)
Out[110]: 
            timestamp  txId  userSessionId  teamId  userId  adId   adCategory  \
0 2016-05-26 15:13:22  5974           5809      27     611     2  electronics   
1 2016-05-26 15:17:24  5976           5705      18    1874    21       movies   
2 2016-05-26 15:22:52  5978           5791      53    2139    25    computers   
3 2016-05-26 15:22:57  5973           5756      63     212    10      fashion   
4 2016-05-26 15:22:58  5980           5920       9    1027    20     clothing   

   adCount  
0        1  
1        1  
2        1  
3        1  
4        1  

```

This doesn't truncate minutes and seconds.

How can I truncate minutes and seconds?