Panda Index Datetime Switching Months and Days - pandas

Question

I have a panda df.index in the format below.
It's a string of day/month/year, so the first item is 05Sep2017 etc:
05/09/17 #05Sep2017
07/09/17 #07Sep2017
...
18/10/17 #18Oct2017
Applying
df.index = pd.to_datetime(df.index)
to the above, transforms it to:
2017-05-09 #09May2017
2017-07-09 #09Jul2017
...
2017-10-18 #18Oct2017
What seems to be happening is that the first entries are having the Day and Month switched. The last entry instead, where the day is greater than 12, is converted correctly.
I tried to switch month days by converting the index to a column and applying:
df['date'] = df.index
df['date'].apply(lambda x: dt.datetime.strftime(x, '%Y-%d-%m'))
as well as:
df['date'].apply(lambda x: dt.datetime.strftime(x, '%Y-%m-%d'))
but to no avail.
How can i convert the index to datetime, where all entries are day/month/year please?

Answer 1

In pandas is default format of dates YY-MM-DD.
df = df.set_index('date_col')
df.index = pd.to_datetime(df.index)
print (df)
val
2017-05-09 4
2017-07-09 8
2017-10-18 2
print (df.index)
DatetimeIndex(['2017-05-09', '2017-07-09', '2017-10-18'], dtype='datetime64[ns]', freq=None)
You need strftime, but lost datetimes, because get strings:
df.index = pd.to_datetime(df.index).strftime('%Y-%d-%m')
print (df.index)
Index(['2017-09-05', '2017-09-07', '2017-18-10'], dtype='object')
df.index = pd.to_datetime(df.index).strftime('%d-%b-%Y')
print (df)
val
09-May-2017 4
09-Jul-2017 8
18-Oct-2017 2