Python – Converts string values to datetime

I currently have a data frame with a column containing datetime values as object data types.

    col1    col2            col3
0    A       10     2016-06-05 11:00:00
0    B       11     2016-06-04 00:00:00
0    C       12     2016-06-02 05:00:00
0    D       13     2016-06-03 02:00:00

What I want to do is convert col3 to a datetime value so it will give me:

 Year-Month-Day-Hour

For some later datetime feature projects. When I try :

df['col3'] = pd.to_datetime(df['col3'])

I’m getting this error :

OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 3008-07-25 00:00:00

Any ideas?

Thanks

You can use the parameter errors='coerce' to convert values that exceed the limit to NaT:

print (df)
  col1  col2                 col3
0    A    10  2016-06-05 11:00:00
0    B    11  2016-06-04 00:00:00
0    C    12  2016-06-02 05:00:00
0    D    13  3008-07-25 00:00:00

df['col3'] = pd.to_datetime(df['col3'], errors='coerce')
print (df)
  col1  col2                col3
0    A    10 2016-06-05 11:00:00
0    B    11 2016-06-04 00:00:00
0    C    12 2016-06-02 05:00:00
0    D    13                 NaT

Timestamp limitation :

In [68]: pd. Timestamp.min
Out[68]: Timestamp('1677-09-21 00:12:43.145225')

In [69]: pd. Timestamp.max
Out[69]: Timestamp('2262-04-11 23:47:16.854775807')

You can also create Periods, But it’s not easy from the string :

def conv(x):
    return pd. Period(year = int(x[:4]), 
                     month = int(x[5:7]), 
                     day = int(x[8:10]),
                     hour = int(x[11:13]), freq='H')

df['col3'] = df['col3'].apply(conv)

print (df)
  col1  col2             col3
0    A    10 2016-06-05 11:00
0    B    11 2016-06-04 00:00
0    C    12 2016-06-02 05:00
0    D    13 3008-07-25 00:00