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

You can directly access in the index and get it plotted, following is an example:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

#Get index in horizontal axis
plt.plot(df.index, df[0])
plt.show()

 #Get index in vertiacal axis
 plt.plot(df[0], df.index)
 plt.show()

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')