I have the following list created from a sorted csv
list1 = sorted(csv1, key=operator.itemgetter(1))
I would actually like to sort the list
Python has a stable sort, so provided that performance isn't an issue the simplest way is to sort it by field 2 and then sort it again by field 1.
That will give you the result you want, the only catch is that if it is a big list (or you want to sort it often) calling sort twice might be an unacceptable overhead.
list1 = sorted(csv1, key=operator.itemgetter(2))
list1 = sorted(list1, key=operator.itemgetter(1))
Doing it this way also makes it easy to handle the situation where you want some of the columns reverse sorted, just include the 'reverse=True' parameter when necessary.
Otherwise you can pass multiple parameters to itemgetter or manually build a tuple. That is probably going to be faster, but has the problem that it doesn't generalise well if some of the columns want to be reverse sorted (numeric columns can still be reversed by negating them but that stops the sort being stable).
So if you don't need any columns reverse sorted, go for multiple arguments to itemgetter, if you might, and the columns aren't numeric or you want to keep the sort stable go for multiple consecutive sorts.
Edit: For the commenters who have problems understanding how this answers the original question, here is an example that shows exactly how the stable nature of the sorting ensures we can do separate sorts on each key and end up with data sorted on multiple criteria:
DATA = [
('Jones', 'Jane', 58),
('Smith', 'Anne', 30),
('Jones', 'Fred', 30),
('Smith', 'John', 60),
('Smith', 'Fred', 30),
('Jones', 'Anne', 30),
('Smith', 'Jane', 58),
('Smith', 'Twin2', 3),
('Jones', 'John', 60),
('Smith', 'Twin1', 3),
('Jones', 'Twin1', 3),
('Jones', 'Twin2', 3)
]
# Sort by Surname, Age DESCENDING, Firstname
print("Initial data in random order")
for d in DATA:
print("{:10s} {:10s} {}".format(*d))
print('''
First we sort by first name, after this pass all
Twin1 come before Twin2 and Anne comes before Fred''')
DATA.sort(key=lambda row: row[1])
for d in DATA:
print("{:10s} {:10s} {}".format(*d))
print('''
Second pass: sort by age in descending order.
Note that after this pass rows are sorted by age but
Twin1/Twin2 and Anne/Fred pairs are still in correct
firstname order.''')
DATA.sort(key=lambda row: row[2], reverse=True)
for d in DATA:
print("{:10s} {:10s} {}".format(*d))
print('''
Final pass sorts the Jones from the Smiths.
Within each family members are sorted by age but equal
age members are sorted by first name.
''')
DATA.sort(key=lambda row: row[0])
for d in DATA:
print("{:10s} {:10s} {}".format(*d))
This is a runnable example, but to save people running it the output is:
Initial data in random order
Jones Jane 58
Smith Anne 30
Jones Fred 30
Smith John 60
Smith Fred 30
Jones Anne 30
Smith Jane 58
Smith Twin2 3
Jones John 60
Smith Twin1 3
Jones Twin1 3
Jones Twin2 3
First we sort by first name, after this pass all
Twin1 come before Twin2 and Anne comes before Fred
Smith Anne 30
Jones Anne 30
Jones Fred 30
Smith Fred 30
Jones Jane 58
Smith Jane 58
Smith John 60
Jones John 60
Smith Twin1 3
Jones Twin1 3
Smith Twin2 3
Jones Twin2 3
Second pass: sort by age in descending order.
Note that after this pass rows are sorted by age but
Twin1/Twin2 and Anne/Fred pairs are still in correct
firstname order.
Smith John 60
Jones John 60
Jones Jane 58
Smith Jane 58
Smith Anne 30
Jones Anne 30
Jones Fred 30
Smith Fred 30
Smith Twin1 3
Jones Twin1 3
Smith Twin2 3
Jones Twin2 3
Final pass sorts the Jones from the Smiths.
Within each family members are sorted by age but equal
age members are sorted by first name.
Jones John 60
Jones Jane 58
Jones Anne 30
Jones Fred 30
Jones Twin1 3
Jones Twin2 3
Smith John 60
Smith Jane 58
Smith Anne 30
Smith Fred 30
Smith Twin1 3
Smith Twin2 3
Note in particular how in the second step the reverse=True
parameter keeps the firstnames in order whereas simply sorting then reversing the list would lose the desired order for the third sort key.