How to swap month and day values in a DataFrame?

I have a DataFrame in which the first column contains information in the form of a date and time.

But for me this information is not recognized correctly.

When I took information from the database and sent the data to the DataFrame table, it looks like I "swapped places" - "day" and "month".
"Day" and "month" are reversed - the day value must be the same value as the month.
"Month" and "day" are reversed - the month value must be a day value.
How can I flip and make the day be like a month, and the month like a day?

In a DataFrame table, a column is recognized as a type - datetime.
But the recognition of the month and day is wrong - they are swapped.

How to swap month and day values in a DataFrame?

now the date time is being recognized so
df['datetime'] = pd.to_datetime(df["datetime"].dt.strftime('%Y-%m-%d'))

and the real location of the month and day is
df['datetime'] = pd.to_datetime(df["datetime"].dt.strftime('%Y-%d-%m'))

--
before

      id            datetime  temp_narvoz   q_virob   
0      1 2023-01-02 10:00:00          1.4  0.688331  
1      2 2023-01-02 11:00:00          1.4  0.800867  
2      3 2023-01-02 12:00:00          1.4  0.810746 
3      4 2023-01-02 13:00:00          1.4  0.805522  
4      5 2023-01-02 14:00:00          2.1  0.802979 



1	Jan. 2, 2023, 10 a.m.	1.4	0.6883313373044648	
2	Jan. 2, 2023, 11 a.m.	1.4	0.8008674575779552	
3	Jan. 2, 2023, noon	1.4	0.8107462069822856	
4	Jan. 2, 2023, 1 p.m.	1.4	0.8055222730239303	
5	Jan. 2, 2023, 2 p.m.	2.1	0.8029786055112401	
6	Jan. 2, 2023, 3 p.m.	2.1	0.7854097839013776	
7	Jan. 2, 2023, 4 p.m.	2.1	0.7950360149694395	
8	Jan. 2, 2023, 5 p.m.	1.6	0.8296386761628508	
9	Jan. 2, 2023, 6 p.m.	1.6	0.83005095964985	
10	Jan. 2, 2023, 7 p.m.	1.6	0.8569995535066821	
11	Jan. 2, 2023, 8 p.m.	-0.5	0.8710515387962285	
12	Jan. 2, 2023, 9 p.m.	-0.5	0.864164249456128	
13	Jan. 2, 2023, 10 p.m.	-0.5	0.8514567681549778	
14	Jan. 2, 2023, 11 p.m.	0.3	0.8078847547912567	
15	Feb. 2, 2023, midnight	0.3	0.7834063591629548	

--
after

      id            datetime  temp_narvoz   q_virob   
0      1 2023-02-01 10:00:00          1.4  0.688331  
1      2 2023-02-01 11:00:00          1.4  0.800867  
2      3 2023-02-01 12:00:00          1.4  0.810746 
3      4 2023-02-01 13:00:00          1.4  0.805522  
4      5 2023-02-01 14:00:00          2.1  0.802979 



1	Feb. 1, 2023, 10 a.m.	1.4	0.6883313373044648	
2	Feb. 1, 2023, 11 a.m.	1.4	0.8008674575779552	
3	Feb. 1, 2023, noon	1.4	0.8107462069822856	
4	Feb. 1, 2023, 1 p.m.	1.4	0.8055222730239303	
5	Feb. 1, 2023, 2 p.m.	2.1	0.8029786055112401	
6	Feb. 1, 2023, 3 p.m.	2.1	0.7854097839013776	
7	Feb. 1, 2023, 4 p.m.	2.1	0.7950360149694395	
8	Feb. 1, 2023, 5 p.m.	1.6	0.8296386761628508	
9	Feb. 1, 2023, 6 p.m.	1.6	0.83005095964985	
10	Feb. 1, 2023, 7 p.m.	1.6	0.8569995535066821	
11	Feb. 1, 2023, 8 p.m.	-0.5	0.8710515387962285	
12	Feb. 1, 2023, 9 p.m.	-0.5	0.864164249456128	
13	Feb. 1, 2023, 10 p.m.	-0.5	0.8514567681549778	
14	Feb. 1, 2023, 11 p.m.	0.3	0.8078847547912567	
15	Feb. 1, 2023, midnight	0.3	0.7834063591629548	

import pandas as pd

df = pd.DataFrame({'date': ['11/02/2022', '12/02/2023', '13/02/2023']})

df['date'] = pd.to_datetime(df['date'])

df['date'] = df['date'].apply(lambda x: x.replace(day=x.month, 
month=x.day))
print(df)