Converting date formats in pandas dataframe

Question

I have a dataframe and the Date column has two different types of date formats going on.

eg. 1983-11-10 00:00:00 and 10/11/1983

I want them all to b

Answer 1

I believe you need parameter dayfirst=True in to_datetime:

df = pd.DataFrame({'Date': {0: '1983-11-10 00:00:00', 1: '10/11/1983'}})
print (df)
                  Date
0  1983-11-10 00:00:00
1           10/11/1983


df['Date'] = pd.to_datetime(df.Date, dayfirst=True)
print (df)
        Date
0 1983-11-10
1 1983-11-10

because:

df['Date'] = pd.to_datetime(df.Date)
print (df)
        Date
0 1983-11-10
1 1983-10-11

---

Or you can specify both formats and then use combine_first:

d1 = pd.to_datetime(df.Date, format='%Y-%m-%d %H:%M:%S', errors='coerce')
d2 = pd.to_datetime(df.Date, format='%d/%m/%Y', errors='coerce')

df['Date'] = d1.combine_first(d2)
print (df)
        Date
0 1983-11-10
1 1983-11-10

General solution for multiple formats:

from functools import reduce 

def convert_formats_to_datetimes(col, formats):
    out = [pd.to_datetime(col, format=x, errors='coerce') for x in formats]
    return reduce(lambda l,r: pd.Series.combine_first(l,r), out)

formats = ['%Y-%m-%d %H:%M:%S', '%d/%m/%Y']
df['Date'] = df['Date'].pipe(convert_formats_to_datetimes, formats)
print (df)
        Date
0 1983-11-10
1 1983-11-10