Changing values of a list of namedtuples

Named tuples are immutable, so you cannot manipulate them.

Right way of doing it:

If you want something mutable, you can use recordtype.

from recordtype import recordtype

Book = recordtype('Book', 'author title genre year price instock')
books = [
   Book('Suzane Collins','The Hunger Games', 'Fiction', 2008, 6.96, 20),
   Book('J.K. Rowling', "Harry Potter and the Sorcerer's Stone", 'Fantasy', 1997, 4.78, 12)]

for book in books:
    book.price *= 1.1
    print(book.price)

PS: You may need to pip install recordtype if you don't have it installed.

Bad way of doing it:

You may also keep using namedtuple with using the _replace() method.

from collections import namedtuple

Book = namedtuple('Book', 'author title genre year price instock')
books = [
   Book('Suzane Collins','The Hunger Games', 'Fiction', 2008, 6.96, 20),
   Book('J.K. Rowling', "Harry Potter and the Sorcerer's Stone", 'Fantasy', 1997, 4.78, 12)]

for i in range(len(books)):
    books[i] = books[i]._replace(price = books[i].price*1.1)
    print(books[i].price)

In Python >= 3.7 you can use dataclass decorator with the new variable annotations feature to produce mutable record types:

from dataclasses import dataclass


@dataclass
class Book:
    author: str
    title: str
    genre: str
    year: int
    price: float
    instock: int


BSI = [
    Book("Suzane Collins", "The Hunger Games", "Fiction", 2008, 6.96, 20),
    Book(
        "J.K. Rowling",
        "Harry Potter and the Sorcerer's Stone",
        "Fantasy",
        1997,
        4.78,
        12,
    ),
]

for item in BSI:
    item.price *= 1.10
    print(f"New price for '{item.title}' book is {item.price:,.2f}")

Output:

New price for 'The Hunger Games' book is 7.66
New price for 'Harry Potter and the Sorcerer's Stone' book is 5.26

This looks like a task for Python's data analysis library, pandas. It's really, really easy to do this sort of thing:

In [6]: import pandas as pd
In [7]: df = pd.DataFrame(BSI, columns=Book._fields)
In [8]: df
Out[8]: 
           author                                  title    genre  year  \
0  Suzane Collins                       The Hunger Games  Fiction  2008   
1    J.K. Rowling  Harry Potter and the Sorcerers Stone  Fantasy  1997   

   price  instock  
0   6.96       20  
1   4.78       12  

In [9]: df['price'] *= 100
In [10]: df
Out[10]: 
           author                                  title    genre  year  \
0  Suzane Collins                       The Hunger Games  Fiction  2008   
1    J.K. Rowling  Harry Potter and the Sorcerer's Stone  Fantasy  1997   

   price  instock  
0    696       20  
1    478       12  

Now isn't that just much, much better than labouring with namedtuples?