I have a python list:
[ (2,2),(2,3),(1,4),(2,2), etc...]
What I need is some kind of function that reduces it to its unique components... w
Using a set
will remove duplicates, and you create a list
from it afterwards:
>>> list(set([ (2,2),(2,3),(1,4),(2,2) ]))
[(2, 3), (1, 4), (2, 2)]
set()
will remove all duplicates, and you can then put it back to a list:
unique = list(set(mylist))
Using set()
, however, will kill your ordering. If the order matters, you can use a list comprehension that checks if the value already exists earlier in the list:
unique = [v for i,v in enumerate(mylist) if v not in mylist[:i]]
That solution is a little slow, however, so you can do it like this:
unique = []
for tup in mylist:
if tup not in unique:
unique.append(tup)
you could simply do
y = np.unique(x, axis=0)
z = []
for i in y:
z.append(tuple(i))
The reason is that a list of tuples is interpreted by numpy as a 2D array. By setting axis=0, you'd be asking numpy not to flatten the array and return unique rows.
If order does not matter
If the order of the result is not critical, you can convert your list to a set (because tuples are hashable) and convert the set back to a list:
>>> l = [(2,2),(2,3),(1,4),(2,2)]
>>> list(set(l))
[(2, 3), (1, 4), (2, 2)]
If order matters
(UPDATE)
As of CPython 3.6 (or any Python 3.7 version) regular dictionaries remember their insertion order, so you can simply issue.
>>> l = [(2,2),(2,3),(1,4),(2,2)]
>>> list(dict.fromkeys(l))
[(2, 2), (2, 3), (1, 4)]
(OLD ANSWER)
If the order is important, the canonical way to filter the duplicates is this:
>>> seen = set()
>>> result = []
>>> for item in l:
... if item not in seen:
... seen.add(item)
... result.append(item)
...
>>> result
[(2, 2), (2, 3), (1, 4)]
Finally, a little slower and a bit more hackish, you can abuse an OrderedDict
as an ordered set:
>>> from collections import OrderedDict
>>> OrderedDict.fromkeys(l).keys() # or list(OrderedDict.fromkeys(l)) if using a version where keys() does not return a list
[(2, 2), (2, 3), (1, 4)]