Pandas: Adding new column to dataframe which is a copy of the index column

Question

I think you need reset_index:

df3 = df3.reset_index()

Possible solution, but I think inplace is not good practice, check this and this:

df3.reset_index(inplace=True)

But if you need new column, use:

df3['new'] = df3.index

I think you can read_csv better:

df = pd.read_csv('university2.csv', 
                 sep=";", 
                 skiprows=1,
                 index_col="YYYY-MO-DD HH-MI-SS_SSS",
                 parse_dates="YYYY-MO-DD HH-MI-SS_SSS") #if doesnt work, use pd.to_datetime

And then omit:

#Changing datetime
df['YYYY-MO-DD HH-MI-SS_SSS'] = pd.to_datetime(df['YYYY-MO-DD HH-MI-SS_SSS'], 
                                               format="%Y-%m-%d %H:%M:%S:%f")
#Set index from column
df = df.set_index('YYYY-MO-DD HH-MI-SS_SSS')

EDIT: If MultiIndex or Index is from groupby operation, possible solutions are:

df = pd.DataFrame({'A':list('aaaabbbb'),
                   'B':list('ccddeeff'),
                   'C':range(8),
                   'D':range(4,12)})
print (df)
   A  B  C   D
0  a  c  0   4
1  a  c  1   5
2  a  d  2   6
3  a  d  3   7
4  b  e  4   8
5  b  e  5   9
6  b  f  6  10
7  b  f  7  11

df1 = df.groupby(['A','B']).sum()
print (df1)
      C   D
A B        
a c   1   9
  d   5  13
b e   9  17
  f  13  21

Add parameter as_index=False:

df2 = df.groupby(['A','B'], as_index=False).sum()
print (df2)
   A  B   C   D
0  a  c   1   9
1  a  d   5  13
2  b  e   9  17
3  b  f  13  21

Or add reset_index:

df2 = df.groupby(['A','B']).sum().reset_index()
print (df2)
   A  B   C   D
0  a  c   1   9
1  a  d   5  13
2  b  e   9  17
3  b  f  13  21

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