How to remove constant prefix and suffix character [duplicate]

Question

I have a data frame where numeric data is stored in String with some Prefix character which I need to remove. On top of this it has double quotes inside the quotes i.e. ' "" '.

dict_1 = {"Col1" : [1001, 1002, 1003, 1004, 1005], 
          "Col2" : ['"Rs. 5131"', '"Rs. 0"', '"Rs 351157"', '"Rs 535391"', '"Rs. 6513"']}
a = pd.DataFrame(dict_1)
a.head(6)

|    | Col1     |  Col2       |       
|----|----------|-------------|
| 0  |1001      |"Rs. 5131"   |
| 1  |1002      |"Rs. 0"      | 
| 2  |1003      |"Rs 351157"  |    
| 3  |1004      |"Rs 535391"  |    
| 4  |1005      |"Rs. 6513"   |

As you can see I want to remove Quotes defined inside Col2 and along with this I have to remove Rs.

I tried following code to subset

b = a['Col2'][0]
b = b[5:]
b = b[:-1]
b

But the issue in some observation it is defined as Rs. and in some Rs without period.

The result should be a column of integers.

Answer 1

Given the sample data in the OP, use .replace

a['Col2'] = a['Col2'].replace({'"': ''}, regex=True)
a['Col2'] = a['Col2'].replace({'Rs.': ''}, regex=True)
a['Col2'] = a['Col2'].replace({'Rs': ''}, regex=True)
a['Col2'] = a['Col2'].replace({' ': ''}, regex=True)

Answer 2

You can simply use removeprefix and removesuffix methods for string after you get the value of the particular columns For a complete answer as comments are demanding

col3=[]
lis = dic['col2']
for b in lis:
    b=b.removeprefix('"').removesuffix('"').removeprefix("Rs.").removeprefix("Rs ")
    col3.append(int(b))
dic['col2']=col3

By this even if there will be Rs. with a period or without period both will be removed without any error. Edit: Change suggested by @Jhanzaib Humayun. I found an easier answer out there on this link for whole of the series alltogether extract number from string

Answer 3

Or use .str.replace():

a["Col2"] = a["Col2"].str.replace('Rs. ', '').replace('"', '')

Update use replace:

a["Col2"].replace(r"Rs\.?\s+", '', regex=True, inplace=True).astype(int)