How to correctly change format of date where day and month is changes in position in Data Frame in Python Pandas?

I have DataFrame in Pandas like below:

data type of COL1 is "object"

COL1
------
1-05-2019
22-04-2019  
5-06-2019

And I need to have this column as data type "object" and in format dd-mm-yyyy for example 01-05-2019.

When I use code like follow: df["COL2"] = df["COL1"].astype("datetime64").dt.strftime('%d-%m-%Y')

I have result like below:

COL1       | COL2
-----------|------
1-05-2019  | 05-01-2019
22-04-2019 | 22-04-2019
5-06-2019  | 06-05-2019

As you can see, for dates from COL1 like: 1-05-2019 and 5-06-2019 my code change position of day and month but for dates like 22-04-2019 works correctly.

I need to have an output like below in "object" data type:

COL1       | COL2
-----------|------
1-05-2019  | 01-05-2019
22-04-2019 | 22-04-2019
5-06-2019  | 05-06-2019

How can I do taht in Python Pandas ?

>Solution :

Convert COL1 to a datetime of a specific format, then format back to a string:

import pandas as pd

df = pd.DataFrame(['1-05-2019','22-04-2019','5-06-2019'], columns=['COL1'])
print(df)
print()
df["COL2"]  = pd.to_datetime(df["COL1"], format='%d-%m-%Y').dt.strftime('%d-%m-%Y')
print(df)

Output:

         COL1
0   1-05-2019
1  22-04-2019
2   5-06-2019

         COL1        COL2
0   1-05-2019  01-05-2019
1  22-04-2019  22-04-2019
2   5-06-2019  05-06-2019