Can anyone suggest a good solution to remove duplicates from nested lists if wanting to evaluate duplicates based on first element of each nested list?
The main list looks like this:
L = [['14', '65', 76], ['2', '5', 6], ['7', '12', 33], ['14', '22', 46]]
If there is another list with the same element at first position [k][0]
that had already occurred, then I'd like to remove that list and get this result:
L = [['14', '65', 76], ['2', '5', 6], ['7', '12', 33]]
Can you suggest an algorithm to achieve this goal?
Do you care about preserving order / which duplicate is removed? If not, then:
dict((x[0], x) for x in L).values()
will do it. If you want to preserve order, and want to keep the first one you find then:
def unique_items(L):
found = set()
for item in L:
if item[0] not in found:
yield item
found.add(item[0])
print list(unique_items(L))
use a dict instead like so:
L = {'14': ['65', 76], '2': ['5', 6], '7': ['12', 33]}
L['14'] = ['22', 46]
if you are receiving the first list from some external source, convert it like so:
L = [['14', '65', 76], ['2', '5', 6], ['7', '12', 33], ['14', '22', 46]]
L_dict = dict((x[0], x[1:]) for x in L)
i am not sure what you meant by "another list", so i assume you are saying those lists inside L
a=[]
L = [['14', '65', 76], ['2', '5', 6], ['7', '12', 33], ['14', '22', 46],['7','a','b']]
for item in L:
if not item[0] in a:
a.append(item[0])
print item
If the order does not matter, code below
print [ [k] + v for (k, v) in dict( [ [a[0], a[1:]] for a in reversed(L) ] ).items() ]
gives
[['2', '5', '6'], ['14', '65', '76'], ['7', '12', '33']]
Use Pandas :
import pandas as pd
L = [['14', '65', 76], ['2', '5', 6], ['7', '12', 33], ['14', '22', 46],['7','a','b']]
df = pd.DataFrame(L)
df = df.drop_duplicates()
L_no_duplicates = df.values.tolist()
If you want to drop duplicates in specific columns only use instead:
df = df.drop_duplicates([1,2])
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