title: DateTime column coming in mm/dd/yyyy, want dd/mm/yyyy
body: <p>In the long run, I'm trying to be able to merge different dataframes of data coming from different sources.  The dataframes themselves are all a time series.  I'm having difficulty with one dataset.  The first column is DateTime.  The initial data has a temporal resolution of 15 s, but in my code I have it being resampled and averaged for each minute (this is to have the same temporal resolution as my other datasets).  What I'm trying to do, is make this 0 key of the datetimes, and then concatenate this horizontally to the initial data.  I'm doing this because when I set the index column to 'DateTime', it seems to delete that column (when I export as csv and open this in excel, or print the dataframe, this column is no longer there), and concatenating the 0 (or df1_DateTimes, as in the code below) to the dataframe seems to reapply this lost data.  The 0 key is automatically generated when I run the df1_DateTimes, I think it just makes the column header titled 0.</p>
  <p>All of the input datetime data is in the format dd/mm/yyyy HH:MM.  However, when I make this &quot;df1_DateTimes&quot;, the datetimes are mm/dd/yyyy HH:MM.  And the column length is equal to that of the data before it was resampled.
  I'm wondering if anyone knows of a way to make this &quot;df1_DateTimes&quot; in the format dd/mm/yyyy HH:MM, and to have the length of the column to be the same length of the resampled data?  The latter isn't as important because I could just have a bunch of empty data.  I've tried things like putting format='%d%m%y %H:%M', but it wasn't seeming to work.</p>
  <p>Or if anyone knows how to resample the data and not lose the DateTimes?  And have the DateTimes in 1 min increments as well?  Any information on any of this would be greatly appreciated.  Just as long as the end result is a dataframe with the values resampled to every minute, and the DateTime column intact, with the datatype of the DateTime column to be datetime64 (so I can merge it with my other datasets).  I have included my code below.</p>
  <pre><code>df1 = pd.read_csv('PATH',
                             
  
  parse_dates=True, usecols=[0,7,10,13,28], 
                             infer_datetime_format=True, index_col='DateTime')
  
  # Resample data to take minute averages
  
  df1.dropna(inplace=True) # Drops missing values
  
  df1=(df1.resample('Min').mean())
  
  df1.to_csv('df1', index=False, encoding='utf-8-sig')
  
  df1_DateTimes = pd.to_datetime(df1.index.values)
  
  df1_DateTimes = df1_DateTimes.to_frame()
  
  df1_DateTimes.to_csv('df1_DateTimes', index=False, encoding='utf-8-sig'`
  </code></pre>
  <p>Thanks for reading and hope to hear back.</p>