Pandas, convert datetime format mm/dd/yyyy to dd/mm/yyyy

You can use the parse_dates and dayfirst arguments of pd.read_csv, see: the docs for read_csv()

df = pd.read_csv('myfile.csv', parse_dates=['Date'], dayfirst=True)

This will read the Date column as datetime values, correctly taking the first part of the date input as the day. Note that in general you will want your dates to be stored as datetime objects.

Then, if you need to output the dates as a string you can call dt.strftime():

df['Date'].dt.strftime('%d/%m/%Y')

This solution will work for all cases where a column has mixed date formats. Add more conditions to the function if needed. Pandas to_datetime() function was not working for me, but this seems to work well.

import date
def format(val):
    a = pd.to_datetime(val, errors='coerce', cache=False).strftime('%m/%d/%Y')
    try:
        date_time_obj = datetime.datetime.strptime(a, '%d/%m/%Y')
    except:
        date_time_obj = datetime.datetime.strptime(a, '%m/%d/%Y')
    return date_time_obj.date()

Saving the changes to the same column.

df['Date'] = df['Date'].apply(lambda x: format(x))

Saving as CSV.

df.to_csv(f'{file_name}.csv', index=False, date_format='%s')

When I use again this: df['Date'] = pd.to_datetime(df['Date']), it gets back to the previous format.

No, you cannot simultaneously have the string format of your choice and keep your series of type datetime. As remarked here:

datetime series are stored internally as integers. Any human-readable date representation is just that, a representation, not the underlying integer. To access your custom formatting, you can use methods available in Pandas. You can even store such a text representation in a pd.Series variable:

formatted_dates = df['datetime'].dt.strftime('%m/%d/%Y')

The dtype of formatted_dates will be object, which indicates that the elements of your series point to arbitrary Python times. In this case, those arbitrary types happen to be all strings.

Lastly, I strongly recommend you do not convert a datetime series to strings until the very last step in your workflow. This is because as soon as you do so, you will no longer be able to use efficient, vectorised operations on such a series.