How can I convert mixed datetime formats into one?-CodePudding

I am facing a little problem with time data in a dataframe. I am trying to convert mixed formats with single digits and slashes (2/19/22) as well as double digits with hyphen (2020-02-19) to zero padded data with point (19.02.2022).

Data:

d = {'Date':['24.06.2022','2022-01-23', '7/2/22',  '9/23/18', '23.05.2022'], 
      'Flight': ['Rome', 'Zurich', 'Dallas', 'Madrid', 'Stockholm']}
df = pd.DataFrame(data=d, index=[0, 1, 2, 3, 4])

My code:

def autoconvert_datetime(value):
    formats = ['%-m/%-d/%y', '%Y-%m-%d']  
    result_format = '%d.%m.%Y' 
    for dt_format in formats:
        try:
            dt_obj = datetime.strptime(value, dt_format)
            return dt_obj.strftime(result_format)
        except Exception as e:  # throws exception when format doesn't match
            pass
    return value  

df['Date'] = df['Date'].apply(autoconvert_datetime)

For other datetime formats it worked without any problem.

CodePudding user response：

Use pd.to_datetime and let Pandas infers the format:

df['Date2'] = pd.to_datetime(df['Date'])
print(df)

# Output
         Date     Flight      Date2
0  24.06.2022       Rome 2022-06-24
1  2022-01-23     Zurich 2022-01-23
2      7/2/22     Dallas 2022-07-02
3     9/23/18     Madrid 2018-09-23
4  23.05.2022  Stockholm 2022-05-23

If you want more control, you can directly use dateutil.parser used by Pandas.