我不知道我是否以正确的方式提出这个问题，但我想搜索日志文件并查找数组中的每个单词。此时，我已经要求用户将有问题的文件拖到终端中，然后用输入构建一个数组。程序应该打印出一个单词的每一行。
一旦我开始工作，我会格式化，有一个计数器，或者对我在文件中找到的内容做一点总结等。
这是我到目前为止所得到的，只有当我运行它时，它实际上找不到任何单词。我一直在查看重复使用示例，但我认为可能会因为我的想法而过于复杂：
def wordsToFind():
needsWords = True
searchArray = []
print "Add words to search ('done') to save/continue."
while needsWords == True:
word = raw_input("Enter a search word: ")
if word.lower() == "done":
needsWords = False
break
else:
searchArray.append(word)
print word + " added"
return searchArray
def getFile():
file_to_read = raw_input("Drag file here:").strip()
return file_to_read
def main():
filePath = getFile()
searchArray = wordsToFind()
print "Words searched for: ", searchArray
searchCount = []
with open(filePath, "r") as inFile:
for line in inFile:
for item in searchArray:
if item in line:
print item
main()
显然，我们非常欢迎任何优化或建议更好的python编码的建议，我只知道我所知道的，并感谢所有的帮助！
答案 0 :(得分：2)
这正是map-reduce旨在解决的问题。如果您不熟悉，map-reduce是一个简单的两步过程。假设您有一个列表，用于存储您有兴趣在文本中找到的单词。您的映射器函数可以遍历此单词列表，对于文本的每一行，如果它出现在行中，它将返回一个值，例如，['word'，lineNum]，它存储在结果列表中。映射器本质上是for循环的包装器。然后你可以通过编写一个reducer函数来获取你的结果列表并“减少”它，在这种情况下，它可以取结果列表，它应该看起来像[['word1'，1] ... ['word1'，n] ...]到一个看起来像{'word1'的对象：[1,2,5]，'word3'：[7]，...}。
这种方法很有用，因为您在对每个项目执行常见操作时抽象迭代列表的过程，并且如果您的分析需要更改（通常如此），您只需要更改映射器/缩减功能而不需要触摸其余的代码。此外，这种方法可以高度并行化，如果它成为一个问题（只要问谷歌！）。
Python 3.x有内置的map / reduce方法，如map（）和reduce（）;在python文档中查找它们。所以你可以看到它们是如何工作的，我在不使用内置库的情况下根据你的问题实现了map / reduce版本。由于您未指定数据的存储方式，因此我对其进行了一些假设，即感兴趣的单词列表将以逗号分隔文件的形式给出。为了读取文本文件，我使用readlines（）来获取行数组，并使用正则表达式模式将行拆分为单词（即，拆分任何不是字母数字的字符）。当然，这可能不适合您的需求，因此您可以将其更改为您正在查看的文件。
我试图远离深奥的python功能（没有lambdas！），所以希望实现很清楚。最后一点，我使用循环迭代文本文件的行，并使用map函数迭代感兴趣的单词列表。您可以使用嵌套的地图函数，但我想跟踪循环索引（因为您关心行号）。如果你真的想要嵌套地图函数，你可以在读取文件时将数组行存储为行和行号的元组，或者你可以修改map函数以返回你选择的索引。
我希望这有帮助！
#!usr/bin/env/ python
#Regexp library
import re
#Map
#This function returns a new array containing
#the elements after that have been modified by whatever function we passed in.
def mapper(function, sequence):
#List to store the results of the map operation
result = []
#Iterate over each item in sequence, append the values to the results list
#after they have been modified by the "function" supplied as an argument in the
#mapper function call.
for item in sequence:
result.append(function(item))
return result
#Reduce
#The purpose of the reduce function is to go through an array, and combine the items
#according to a specified function - this specified function should combine an element
#with a base value
def reducer(function, sequence, base_value):
#Need to get an base value to serve as the starting point for the construction of
#the result
#I will assume one is given, but in most cases you should include extra validation
#here to either ensure one is given, or some sensible default is chosen
#Initialize our accumulative value object with the base value
accum_value = base_value
#Iterate through the sequence items, applying the "function" provided, and
#storing the results in the accum_value object
for item in sequence:
accum_value = function(item, accum_value)
return accum_value
#With these functions it should be sufficient to address your problem, what remains
#is simply to get the data from the text files, and keep track of the lines in
#which words appear
if __name__ == 'main':
word_list_file = 'FILEPATH GOES HERE'
#Read in a file containing the words that will be searched in the text file
#(assumes words are given as a comma separated list)
infile = open(word_list_file, 'rt') #Open file
content = infile.read() #read the whole file as a single string
word_list = content.split(',') #split the string into an array of words
infile.close()
target_text_file = 'FILEPATH GOES HERE'
#Read in the text to analyze
infile = open(target_text_file, 'rt') #Open file
target_text_lines = infile.readlines() #Read the whole file as an array of lines
infile.close()
#With the data loaded, the overall strategy will be to loop over the text lines, and
#we will use the map function to loop over the the word_list and see if they are in
#the current text file line
#First, define the my_mapper function that will process your data, and will be passed to
#the map function
def my_mapper(item):
#Split the current sentence into words
#Will split on any non alpha-numeric character. This strategy can be revised
#to find matches to a regular expression pattern based on the words in the
#words list. Either way, make sure you choose a sensible strategy to do this.
current_line_words = re.split(r'\W+', target_text_lines[k])
#lowercase the words
current_line_words = [word.lower() for word in current_line_words]
#Check if the current item (word) is in the current_line_words list, and if so,
#return the word and the line number
if item in current_line_words:
return [item, k+1] #Return k+1 because k begins at 0, but I assume line
#counting begins with 1?
else:
return [] #Technically, this does not need to be added, it can simply
#return None by default, but that requires manually handling iterator
#objects so the loop doesn't crash when seeing the None values,
#and I am being lazy :D
#With the mapper function established, we can proceed to loop over the text lines of the
#array, and use our map function to process the lines against the list of words.
#This array will store the results of the map operation
map_output = []
#Loop over text file lines, use mapper to find which words are in which lines, store
#in map_output list. This is the exciting stuff!
for k in range(len(target_text_lines)):
map_output.extend(mapper(my_mapper, word_list))
#At this point, we should have a list of lists containing the words and the lines they
#appeared in, and it should look like, [['word1', 1] ... ['word25': 5] ... [] ...]
#As you can see, the post-map array will have an entry for each word that appeared in
#each line, and if a particular word did not appear in a particular line, there will be a
#empty list instead.
#Now all that remains is to summarize our data, and that is what the reduce function is
#for. We will iterate over the map_output list, and collect the words and which lines
#they appear at in an object that will have the format { 'word': [n1, n2, ...] },where
#n1, n2, ... are the lines the word appears in. As in the case for the mapper
#function, the output of the reduce function can be modified in the my_reducer function
#you supply to it. If you'd rather it return something else (like say, word count), this
#is the function to modify.
def my_reducer(item, accum_value):
#First, verify item is not empty
if item != []:
#If the element already exists in the output object, append the current line
#value to it, if not, add it to the object and create a set holding the current
#line value
#Check this word/line combination isn't already stored in the output dict
if (item[0] in accum_value) and (item[1] not in accum_value[item[0]]):
accum_value[item[0]].append(item[1])
else:
accum_value[item[0]] = [item[1]]
return accum_value
#Now we can call the reduce function, save it's output, print it to screen, and we're
#done!
#(Note that for base value we are just passing in an empty object, {})
reduce_results = reducer(my_reducer, map_output, {})
#Print results to screen
for result in reduce_results:
print('word: {}, lines: {}'.format(result, reduce_results[result]))
答案 1 :(得分：1)
你可以这样做：
a = ['foo', 'bar', 'cox', 'less', 'more']
b = ['foo', 'cox', 'complex', 'list']
c = list(set(a).intersection(set(b)))
这样c将是：
['cox', 'foo']
实现此目的的另一种方法是使用python comprehension：
c = [x for x in a if x in b]
我不测试哪种是最方式的，但我认为是使用套装......