日期时间列在 mm/dd/yyyy 中，需要 dd/mm/yyyy

Question

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.

All of the input datetime data is in the format dd/mm/yyyy HH:MM. However, when I make this "df1_DateTimes", 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 "df1_DateTimes" 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.

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.

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'`

Thanks for reading and hope to hear back.

Answer 1

import datetime
df1__DateTimes = k

k['TITLE OF DATES COLUMN'] = k['TITLES OF DATES COLUMN'].datetime.strftime('%d/%m/%y')

I think using the above snippet solves your issue. It assigns the date column to the formatted version (dd/mm/yy) of itself.

More on the Kite docs