Python – SyntaxError: keyword argument repeated
It does not cause any extra queries this way — Django won’t evaluate the queryset until necessary.
An alternative is to build up the filter with Q objects.
from django.db.models import Q q = Q(ptrf__istartswith='ptrf-mt23run1-') | Q(ptrf__istartswith='ptrf-20251-') qs = qs.exclude(q) Related Solutions
Python – What does the “yield” keyword do
To understand what yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.
Iterables
When you create a list, you can read its items one by one. Reading its items one by one is called iteration:
>>> mylist = [1, 2, 3] >>> for i in mylist: . print(i) 1 2 3 mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
>>> mylist = [x*x for x in range(3)] >>> for i in mylist: . print(i) 0 1 4 Everything you can use » for. in. » on is an iterable; lists , strings , files.
These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.
Generators
Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:
>>> mygenerator = (x*x for x in range(3)) >>> for i in mygenerator: . print(i) 0 1 4 It is just the same except you used () instead of [] . BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.
Yield
yield is a keyword that is used like return , except the function will return a generator.
>>> def create_generator(): . mylist = range(3) . for i in mylist: . yield i*i . >>> mygenerator = create_generator() # create a generator >>> print(mygenerator) # mygenerator is an object! >>> for i in mygenerator: . print(i) 0 1 4 Here it’s a useless example, but it’s handy when you know your function will return a huge set of values that you will only need to read once.
To master yield , you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky.
Then, your code will continue from where it left off each time for uses the generator.
The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield , then it’ll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hitting yield . That can be because the loop has come to an end, or because you no longer satisfy an «if/else» .
Your code explained
# Here you create the method of the node object that will return the generator def _get_child_candidates(self, distance, min_dist, max_dist): # Here is the code that will be called each time you use the generator object: # If there is still a child of the node object on its left # AND if the distance is ok, return the next child if self._leftchild and distance - max_dist < self._median: yield self._leftchild # If there is still a child of the node object on its right # AND if the distance is ok, return the next child if self._rightchild and distance + max_dist >= self._median: yield self._rightchild # If the function arrives here, the generator will be considered empty # there is no more than two values: the left and the right children # Create an empty list and a list with the current object reference result, candidates = list(), [self] # Loop on candidates (they contain only one element at the beginning) while candidates: # Get the last candidate and remove it from the list node = candidates.pop() # Get the distance between obj and the candidate distance = node._get_dist(obj) # If distance is ok, then you can fill the result if distance = min_dist: result.extend(node._values) # Add the children of the candidate in the candidate's list # so the loop will keep running until it will have looked # at all the children of the children of the children, etc. of the candidate candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) return result This code contains several smart parts:
- The loop iterates on a list, but the list expands while the loop is being iterated. It’s a concise way to go through all these nested data even if it’s a bit dangerous since you can end up with an infinite loop. In this case, candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) exhaust all the values of the generator, but while keeps creating new generator objects which will produce different values from the previous ones since it’s not applied on the same node.
- The extend() method is a list object method that expects an iterable and adds its values to the list.
Usually we pass a list to it:
>>> a = [1, 2] >>> b = [3, 4] >>> a.extend(b) >>> print(a) [1, 2, 3, 4] But in your code, it gets a generator, which is good because:
- You don’t need to read the values twice.
- You may have a lot of children and you don’t want them all stored in memory.
And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples, and generators! This is called duck typing and is one of the reasons why Python is so cool. But this is another story, for another question.
You can stop here, or read a little bit to see an advanced use of a generator:
Controlling a generator exhaustion
>>> class Bank(): # Let's create a bank, building ATMs . crisis = False . def create_atm(self): . while not self.crisis: . yield "$100" >>> hsbc = Bank() # When everything's ok the ATM gives you as much as you want >>> corner_street_atm = hsbc.create_atm() >>> print(corner_street_atm.next()) $100 >>> print(corner_street_atm.next()) $100 >>> print([corner_street_atm.next() for cash in range(5)]) ['$100', '$100', '$100', '$100', '$100'] >>> hsbc.crisis = True # Crisis is coming, no more money! >>> print(corner_street_atm.next()) >>> wall_street_atm = hsbc.create_atm() # It's even true for new ATMs >>> print(wall_street_atm.next()) >>> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains empty >>> print(corner_street_atm.next()) >>> brand_new_atm = hsbc.create_atm() # Build a new one to get back in business >>> for cash in brand_new_atm: . print cash $100 $100 $100 $100 $100 $100 $100 $100 $100 . Note: For Python 3, use print(corner_street_atm.__next__()) or print(next(corner_street_atm))
It can be useful for various things like controlling access to a resource.
Itertools, your best friend
The itertools module contains special functions to manipulate iterables. Ever wish to duplicate a generator? Chain two generators? Group values in a nested list with a one-liner? Map / Zip without creating another list?
Then just import itertools .
An example? Let’s see the possible orders of arrival for a four-horse race:
>>> horses = [1, 2, 3, 4] >>> races = itertools.permutations(horses) >>> print(races) >>> print(list(itertools.permutations(horses))) [(1, 2, 3, 4), (1, 2, 4, 3), (1, 3, 2, 4), (1, 3, 4, 2), (1, 4, 2, 3), (1, 4, 3, 2), (2, 1, 3, 4), (2, 1, 4, 3), (2, 3, 1, 4), (2, 3, 4, 1), (2, 4, 1, 3), (2, 4, 3, 1), (3, 1, 2, 4), (3, 1, 4, 2), (3, 2, 1, 4), (3, 2, 4, 1), (3, 4, 1, 2), (3, 4, 2, 1), (4, 1, 2, 3), (4, 1, 3, 2), (4, 2, 1, 3), (4, 2, 3, 1), (4, 3, 1, 2), (4, 3, 2, 1)] Understanding the inner mechanisms of iteration
Iteration is a process implying iterables (implementing the __iter__() method) and iterators (implementing the __next__() method). Iterables are any objects you can get an iterator from. Iterators are objects that let you iterate on iterables.
There is more about it in this article about how for loops work.
Python – How to import a module given its name as string
With Python older than 2.7/3.1, that’s pretty much how you do it.
For newer versions, see importlib.import_module for Python 2 and Python 3.
You can use exec if you want to as well.
Or using __import__ you can import a list of modules by doing this:
>>> moduleNames = ['sys', 'os', 're', 'unittest'] >>> moduleNames ['sys', 'os', 're', 'unittest'] >>> modules = map(__import__, moduleNames) SyntaxError: keyword argument repeated
It turns out that repeated keyword arguments (kwargs) were illegal in python 2.4, ignored in python 2.5 and illegal again in python 2.6. This means that if some have crept into your codebase, you’ve now got a handful of syntax errors!
def foo(**kwargs): for k, v in kwargs.iteritems(): print "%s: %s" % (k, v) foo(a = 1, b = 2, a = 3) And here’s what happens when you run it under 2.4, 2.5 and 2.6:
nick@pluto:~/tmp$ python2.4 test.py File "test.py", line 5 foo(a = 1, b = 2, a = 3) SyntaxError: duplicate keyword argument nick@pluto:~/tmp$ python2.5 test.py a: 3 b: 2 nick@pluto:~/tmp$ python2.6 test.py File "test.py", line 5 foo(a = 1, b = 2, a = 3) SyntaxError: keyword argument repeated How could this creep in? Most likely through revision control mergers. Function calls with lots of kwargs are often laid out like:
foo( kwarg_with_a_long_name=1, another_self_documenting_flag=False, ) Alice adds a kwarg for whatever=”bar”, at the start on the kwargs list, before kwarg_with_a_long_name. So does Bob, but Bob adds it at the end. The revision control system will happily merge these two changes without conflicts as:
foo( whatever="bar", kwarg_with_a_long_name=1, another_self_documenting_flag=False, whatever="bar", ) And under python 2.5, neither Alice or Bob are likely to notice … after all, foo() is being called with kwarg whatever=”bar” just as they intended.
But when the underlying system is upgraded to python 2.6, a mysterious “SyntaxError: keyword argument repeated” appears, and no-one can quite work out who to blame!
All content Copyright © 2009-2023 Nick Moore unless otherwise noted