Elegant and Efficient way retain date values as is without OOB error

Question

I have a dataframe which is like as shown below

df1_new = pd.DataFrame({\'person_id\': [1, 1, 3, 3, 5, 5],\'obs_date\': [\'7/23/2377  12:00:00 AM\', \'NA-NA-         


        

Answer 1

You can convert values to datetimes and then to day Period for only possible format in pandas for represent out of bound values.

If omit it, then working with python datetimes objects, not with pandas datetimes (timestamps).

from datetime import datetime
def str2time(x):
    try:
        return pd.Period(datetime.strptime(x, '%m/%d/%Y %I:%M:%S %p'), 'D')
    except:
        return np.nan

df1_new['obs_date'] = df1_new['obs_date'].apply(str2time)
print(df1_new)
   person_id    obs_date
0          1  2377-07-23
1          1         NaT
2          3         NaT
3          3  2277-07-27
4          5  2077-07-13
5          5         NaT

print(df1_new['obs_date'].dtype)
period[D]

If possible multiple formats:

def str2time(x):
    try:
        #MM/DD/YYYY II:MM:SS pp like 7/23/2377  12:00:00 AM
        return pd.Period(datetime.strptime(x, '%m/%d/%Y %I:%M:%S %p'), 'D')
    except:
        try:
            #YYYY-MM-DD HH:MM:SS like 2377-07-23 00:00:00
            return pd.Period(datetime.strptime(x, '%Y-%m-%d %H:%M:%S'), 'D')
        except:
            return np.nan

df1_new['obs_date'] = df1_new['obs_date'].apply(str2time)