Python有string.find()和string.rfind()来获取字符串中子字符串的索引。
我想知道是否有像string.find_all()这样的东西可以返回所有找到的索引(不仅是从开始的第一个索引，还是从结束的第一个索引)。
例如:
string = "test test test test"
print string.find('test') # 0
print string.rfind('test') # 15
#this is the goal
print string.find_all('test') # [0,5,10,15]
要统计出现次数，请参见计算字符串中子字符串出现的次数。
当前回答
def find_index(string, let):
enumerated = [place for place, letter in enumerate(string) if letter == let]
return enumerated
例如:
find_index("hey doode find d", "d")
返回:
[4, 7, 13, 15]
其他回答
我认为最干净的解决方法是没有库和yield:
def find_all_occurrences(string, sub):
index_of_occurrences = []
current_index = 0
while True:
current_index = string.find(sub, current_index)
if current_index == -1:
return index_of_occurrences
else:
index_of_occurrences.append(current_index)
current_index += len(sub)
find_all_occurrences(string, substr)
注意:find()方法在找不到任何东西时返回-1
这是我使用re.finditer的技巧
import re
text = 'This is sample text to test if this pythonic '\
'program can serve as an indexing platform for '\
'finding words in a paragraph. It can give '\
'values as to where the word is located with the '\
'different examples as stated'
# find all occurances of the word 'as' in the above text
find_the_word = re.finditer('as', text)
for match in find_the_word:
print('start {}, end {}, search string \'{}\''.
format(match.start(), match.end(), match.group()))
如果你只是寻找一个单一的字符，这是可行的:
string = "dooobiedoobiedoobie"
match = 'o'
reduce(lambda count, char: count + 1 if char == match else count, string, 0)
# produces 7
同时,
string = "test test test test"
match = "test"
len(string.split(match)) - 1
# produces 4
我的直觉是，这两个(尤其是#2)的性能都不太好。
python的方法是:
mystring = 'Hello World, this should work!'
find_all = lambda c,s: [x for x in range(c.find(s), len(c)) if c[x] == s]
# s represents the search string
# c represents the character string
find_all(mystring,'o') # will return all positions of 'o'
[4, 7, 20, 26]
>>>
当在一份文件中寻找大量的关键词时，使用flash文本
from flashtext import KeywordProcessor
words = ['test', 'exam', 'quiz']
txt = 'this is a test'
kwp = KeywordProcessor()
kwp.add_keywords_from_list(words)
result = kwp.extract_keywords(txt, span_info=True)
在大量搜索词列表上，Flashtext比正则表达式运行得更快。
