np.loadtxt function showing error, could not convert string to float: 'ï»¿"Date"'
The problem might arise because of the meta-text in the .csv or .txt file that is not really written there but is copied when its content is loaded somewhere. 

I think it is better to first import your text in an array or a string and then split it and save into the dataframe specifically when your data is not too large.

import csv 
arrays = []
path = "C:\\Users\\Souro\\Downloads\\AXISBANK.csv"
with open(path, 'r') as f: 
   reader = csv.reader(f) 
   for row in reader: 
       row = str(row).replace('\\', '') #deleting backslash
       arrays.append(row)


Then take a look at arrays[:10] to find where the meta data ends and delete the unwanted data (meta data) 
and then converting the 'arrays' array into the dataframe.
for instance: 

arrays = arrays[9:]
df = pd.DataFrame(arrays[1:], columns=arrays[0]) #arrays[0] is the columns names


about your comments:

if you look at the text in each row (print each row), you would find out that a backslash is at the end of each row, so by replace('\',' ') we are substituting each backslash with nothing(''). why two \? It is the way that we declare backslash, otherwise, it won't be recognized.

row=str(row).replace('\\',' ') 


and about 

open('text.txt','r')


It opens the file 'text.txt' in reading mode (r).