blocks|key|537732|text|您可以使用集合来跟踪已处理的单词。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|537733|words+=+set()
result+=+''
for+word+in+string1.split():
++++if+word+not+in+words:
++++++++result+=+result+%2B+word+%2B+'+'
++++++++words.add(word)
print+result|code-block|syntax|javascript|537734|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can use a set to keep track of already processed words.

<pre><code>words = set()
result = ''
for word in string1.split():
 if word not in words:
 result = result + word + ' '
 words.add(word)
print result
</code></pre>

blocks|key|754275|text|def+unique_list(l):
++++ulist+=+[]
++++[ulist.append(x)+for+x+in+l+if+x+not+in+ulist]
++++return+ulist

a="calvin+klein+design+dress+calvin+klein"
a='+'.join(unique_list(a.split()))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|754276|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>def unique_list(l):
 ulist = []
 [ulist.append(x) for x in l if x not in ulist]
 return ulist

a="calvin klein design dress calvin klein"
a=' '.join(unique_list(a.split()))
</code></pre>

blocks|key|754350|text|在Python+2.7%2B中，您可以使用collections.OrderedDict来执行以下操作：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|754351|from+collections+import+OrderedDict
s+=+"calvin+klein+design+dress+calvin+klein"
print+'+'.join(OrderedDict((w,w)+for+w+in+s.split()).keys())|code-block|syntax|javascript|754352|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.python.org/dev/library/collections.html#collections.OrderedDict^0|J|N|J|N|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

In Python 2.7+, you could use <a href="http://docs.python.org/dev/library/collections.html#collections.OrderedDict" rel="noreferrer"><code>collections.OrderedDict</code></a> for this:

<pre><code>from collections import OrderedDict
s = "calvin klein design dress calvin klein"
print ' '.join(OrderedDict((w,w) for w in s.split()).keys())
</code></pre>

blocks|key|754398|text|从itertools+recipes中剪切和粘贴|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|754399|from+itertools+import+ifilterfalse

def+unique_everseen(iterable,+key=None):
++++"List+unique+elements,+preserving+order.+Remember+all+elements+ever+seen."
++++#+unique_everseen('AAAABBBCCDAABBB')+-->+A+B+C+D
++++#+unique_everseen('ABBCcAD',+str.lower)+-->+A+B+C+D
++++seen+=+set()
++++seen_add+=+seen.add
++++if+key+is+None:
++++++++for+element+in+ifilterfalse(seen.__contains__,+iterable):
++++++++++++seen_add(element)
++++++++++++yield+element
++++else:
++++++++for+element+in+iterable:
++++++++++++k+=+key(element)
++++++++++++if+k+not+in+seen:
++++++++++++++++seen_add(k)
++++++++++++++++yield+element|code-block|syntax|javascript|754400|我真的希望他们能尽快把这些食谱做成一个模块。我非常希望能够做from+itertools_recipes+import+unique_everseen，而不是每次我需要东西的时候都使用剪切粘贴。|style|CODE|754401|像这样使用：|754402|def+unique_words(string,+ignore_case=False):
++++key+=+None
++++if+ignore_case:
++++++++key+=+str.lower
++++return+"+".join(unique_everseen(string.split(),+key=key))

string2+=+unique_words(string1)|754403|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.python.org/library/itertools.html#recipes^0|1|H|0|0|0|U|19|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|13|8|@$A|14|B|15|K|L]]|9|@]|C|$]]|$1|M|3|N|5|6|7|16|8|@]|9|@]|C|$]]|$1|O|3|P|5|F|7|17|8|@]|9|@]|C|$G|H]]|$1|Q|3|-4|5|6|7|18|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]]]

Cut and paste from the <a href="http://docs.python.org/library/itertools.html#recipes" rel="noreferrer">itertools recipes</a>

<pre><code>from itertools import ifilterfalse

def unique_everseen(iterable, key=None):
 "List unique elements, preserving order. Remember all elements ever seen."
 # unique_everseen('AAAABBBCCDAABBB') --&gt; A B C D
 # unique_everseen('ABBCcAD', str.lower) --&gt; A B C D
 seen = set()
 seen_add = seen.add
 if key is None:
 for element in ifilterfalse(seen.__contains__, iterable):
 seen_add(element)
 yield element
 else:
 for element in iterable:
 k = key(element)
 if k not in seen:
 seen_add(k)
 yield element
</code></pre>

I really wish they could go ahead and make a module out of those recipes soon. I'd very much like to be able to do <code>from itertools_recipes import unique_everseen</code> instead of using cut-and-paste every time I need something.

Use like this:

<pre><code>def unique_words(string, ignore_case=False):
 key = None
 if ignore_case:
 key = str.lower
 return " ".join(unique_everseen(string.split(), key=key))

string2 = unique_words(string1)
</code></pre>

blocks|key|537893|text|string+=+'calvin+klein+design+dress+calvin+klein'

def+uniquify(string):
++++output+=+[]
++++seen+=+set()
++++for+word+in+string.split():
++++++++if+word+not+in+seen:
++++++++++++output.append(word)
++++++++++++seen.add(word)
++++return+'+'.join(output)

print+uniquify(string)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|537894|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>string = 'calvin klein design dress calvin klein'

def uniquify(string):
 output = []
 seen = set()
 for word in string.split():
 if word not in seen:
 output.append(word)
 seen.add(word)
 return ' '.join(output)

print uniquify(string)
</code></pre>

blocks|key|537931|text|string1+=+"calvin+klein+design+dress+calvin+klein"
words+=+string1.split()
print+("+".join(sorted(set(words),+key=words.index)))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|537932|这将根据原始单词列表中的单词索引对字符串中的所有(唯一)单词集进行排序。|unstyled|537933|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|J|8|@]|9|@]|A|$]]|$1|G|3|-4|5|F|7|K|8|@]|9|@]|A|$]]]|H|$]]

<pre><code>string1 = "calvin klein design dress calvin klein"
words = string1.split()
print (" ".join(sorted(set(words), key=words.index)))
</code></pre>

This sorts the set of all the (unique) words in your string by the word's index in the original list of words.

blocks|key|754490|text|有几个答案非常接近这一点，但并没有完全达到我想要的结果：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|754491|def+uniques(+your_string+):++++
++++seen+=+set()
++++return+'+'.join(+seen.add(i)+or+i+for+i+in+your_string.split()+if+i+not+in+seen+)|code-block|syntax|javascript|754492|当然，如果你想让它更干净或者更快，我们可以重构一下：|754493|def+uniques(+your_string+):++++
++++words+=+your_string.split()

++++seen+=+set()
++++seen_add+=+seen.add

++++def+add(x):
++++++++seen_add(x)++
++++++++return+x

++++return+'+'.join(+add(i)+for+i+in+words+if+i+not+in+seen+)|754494|我认为第二个版本是你在少量代码中所能获得的最好的性能。(可以使用更多的代码在一次扫描输入字符串中完成所有工作，但对于大多数工作负载来说，这应该足够了。)|754495|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Several answers are pretty close to this but haven't quite ended up where I did:

<pre><code>def uniques( your_string ): 
 seen = set()
 return ' '.join( seen.add(i) or i for i in your_string.split() if i not in seen )
</code></pre>

Of course, if you want it a tiny bit cleaner or faster, we can refactor a bit:

<pre><code>def uniques( your_string ): 
 words = your_string.split()

 seen = set()
 seen_add = seen.add

 def add(x):
 seen_add(x) 
 return x

 return ' '.join( add(i) for i in words if i not in seen )
</code></pre>

I think the second version is about as performant as you can get in a small amount of code. (More code could be used to do all the work in a single scan across the input string but for most workloads, this should be sufficient.)

blocks|key|754504|text|11和2完美地工作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|754505|++++s="the+sky+is+blue+very+blue"
++++s=s.lower()
++++slist+=+s.split()
++++print+"+".join(sorted(set(slist),+key=slist.index))|code-block|syntax|javascript|754506|和2|754507|++++s="the+sky+is+blue+very+blue"
++++s=s.lower()
++++slist+=+s.split()
++++print+"+".join(sorted(set(slist),+key=slist.index))|754508|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

11 and 2 work perfectly:

<pre><code> s="the sky is blue very blue"
 s=s.lower()
 slist = s.split()
 print " ".join(sorted(set(slist), key=slist.index))
</code></pre>

and 2

<pre><code> s="the sky is blue very blue"
 s=s.lower()
 slist = s.split()
 print " ".join(sorted(set(slist), key=slist.index))
</code></pre>

blocks|key|538031|text|问:删除字符串中的重复项|type|unstyled|depth|inlineStyleRanges|entityRanges|data|538032|+from+_collections+import+OrderedDict

++++a+=+"Gina+Gini+Gini+Protijayi"

++++aa+=+OrderedDict().fromkeys(a.split())
++++print('+'.join(aa))
+++#+output+=>+Gina+Gini+Protijayi|code-block|syntax|javascript|538033|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Question: Remove the duplicates in a string

<pre><code> from _collections import OrderedDict

 a = "Gina Gini Gini Protijayi"

 aa = OrderedDict().fromkeys(a.split())
 print(' '.join(aa))
 # output =&gt; Gina Gini Protijayi
</code></pre>

blocks|key|538111|text|您可以使用以下代码从文本文件或字符串中删除重复或重复的单词：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|538112|from+collections+import+Counter
for+lines+in+all_words:

++++line=''.join(lines.lower())
++++new_data1='+'.join(lemmatize_sentence(line))
++++new_data2+=+word_tokenize(new_data1)
++++new_data3=nltk.pos_tag(new_data2)

++++#+below+code+is+for+removal+of+repeated+words

++++for+i+in+range(0,+len(new_data3)):
++++++++new_data3[i]+=+"".join(new_data3[i])
++++UniqW+=+Counter(new_data3)
++++new_data5+=+"+".join(UniqW.keys())
++++print+(new_data5)


++++new_data.append(new_data5)


print+(new_data)|code-block|syntax|javascript|538113|根据要求提供P.S.+-Do标识。希望这能有所帮助！|538114|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

You can remove duplicate or repeated words from a text file or string using following codes - 

<pre><code>from collections import Counter
for lines in all_words:

 line=''.join(lines.lower())
 new_data1=' '.join(lemmatize_sentence(line))
 new_data2 = word_tokenize(new_data1)
 new_data3=nltk.pos_tag(new_data2)

 # below code is for removal of repeated words

 for i in range(0, len(new_data3)):
 new_data3[i] = "".join(new_data3[i])
 UniqW = Counter(new_data3)
 new_data5 = " ".join(UniqW.keys())
 print (new_data5)


 new_data.append(new_data5)


print (new_data)
</code></pre>

P.S. -Do identations as per required.
Hope this helps!!!

blocks|key|538199|text|您可以简单地通过获取与字符串相关联的集合来做到这一点，根据定义，该字符串是一个不包含重复元素的数学对象。它可以将集合中的单词重新连接到一个字符串中：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|538200|def+remove_duplicate_words(string):
++++++++x+=+string.split()
++++++++x+=+sorted(set(x),+key+=+x.index)
++++++++return+'+'.join(x)|code-block|syntax|javascript|538201|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can do that simply by getting the set associated to the string, which is a mathematical object containing no repeated elements by definition. It suffices to join the words in the set back into a string:
<pre><code>def remove_duplicate_words(string):
 x = string.split()
 x = sorted(set(x), key = x.index)
 return ' '.join(x)
</code></pre>

blocks|key|754774|text|不使用split函数(在面试中会有所帮助)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|754775|def+unique_words2(a):
++++words+=+[]
++++spaces+=+'+'
++++length+=+len(a)
++++i+=+0
++++while+i+<+length:
++++++++if+a[i]+not+in+spaces:
++++++++++++word_start+=+i
++++++++++++while+i+<+length+and+a[i]+not+in+spaces:
++++++++++++++++i+%2B=+1
++++++++++++words.append(a[word_start:i])
++++++++i+%2B=+1
++++words_stack+=+[]
++++for+val+in+words:++#
++++++++if+val+not+in+words_stack:++#+We+can+replace+these+three+lines+with+this+one+->+[words_stack.append(val)+for+val+in+words+if+val+not+in+words_stack]
++++++++++++words_stack.append(val)++#
++++print('+'.join(words_stack))++#+or+return,+your+choice


unique_words2('calvin+klein+design+dress+calvin+klein')+|code-block|syntax|javascript|754776|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Without using the split function (will help in interviews)

<pre><code>def unique_words2(a):
 words = []
 spaces = ' '
 length = len(a)
 i = 0
 while i &lt; length:
 if a[i] not in spaces:
 word_start = i
 while i &lt; length and a[i] not in spaces:
 i += 1
 words.append(a[word_start:i])
 i += 1
 words_stack = []
 for val in words: #
 if val not in words_stack: # We can replace these three lines with this one -&gt; [words_stack.append(val) for val in words if val not in words_stack]
 words_stack.append(val) #
 print(' '.join(words_stack)) # or return, your choice


unique_words2('calvin klein design dress calvin klein') 
</code></pre>

blocks|key|754833|text|使用numpy函数使导入最好有一个别名作为导入(作为np)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|754834|import+numpy+as+np|code-block|syntax|javascript|754835|然后，您可以像这样使用它，以便从数组中删除重复项|754836|no_duplicates_array+=+np.unique(your_array)|754837|对于您的情况，如果想要字符串形式的结果，可以使用|754838|no_duplicates_string+=+'+'.join(np.unique(your_string.split()))|754839|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

Use numpy function
make an import its better to have an alias for the import (as np)

<pre><code>import numpy as np
</code></pre>

and then you can bing it like this
for removing duplicates from array you can use it this way

<pre><code>no_duplicates_array = np.unique(your_array)
</code></pre>

for your case if you want result in string you can use

<pre><code>no_duplicates_string = ' '.join(np.unique(your_string.split()))
</code></pre>

blocks|key|538345|text|正在初始化列表|type|unstyled|depth|inlineStyleRanges|entityRanges|data|538346|listA+=+[+'xy-xy',+'pq-qr',+'xp-xp-xp',+'dd-ee']

print("Given+list+:+",listA)|code-block|syntax|javascript|538347|使用set()和split()|offset|length|style|CODE|538348|res+=+[set(sub.split('-'))+for+sub+in+listA]|538349|结果|538350|print("List+after+duplicate+removal+:",+res)+|538351|entityMap^0|0|0|2|5|8|7|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@$I|X|J|Y|K|L]|$I|Z|J|10|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|12|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|S|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|T|$]]

<h4>initializing list</h4>
<pre><code>listA = [ 'xy-xy', 'pq-qr', 'xp-xp-xp', 'dd-ee']

print(&quot;Given list : &quot;,listA)
</code></pre>
<h4>using <code>set()</code> and <code>split()</code></h4>
<pre><code>res = [set(sub.split('-')) for sub in listA]
</code></pre>
<h4>Result</h4>
<pre><code>print(&quot;List after duplicate removal :&quot;, res) 
</code></pre>

Following example:

<pre><code>string1 = "calvin klein design dress calvin klein"
</code></pre>

How can I remove the second two duplicates <code>"calvin"</code> and <code>"klein"</code>?

The result should look like 

<pre><code>string2 = "calvin klein design dress"
</code></pre>

only the second duplicates should be removed and the sequence of the words should not be changed!

How can I remove duplicate words in a string with Python?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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下面是示例：string1 = "calvin klein design dress calvin klein"如何删除第二个重复的"calvin"和"klein"结果应该如下所示string2 = "calvin klein design dress"只应删除第二个重复项，并且不应更改单词的顺序！

问如何使用Python删除字符串中的重复单词？
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问如何使用Python删除字符串中的重复单词？EN