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，其中存储了您有兴趣在文本中找到的单词。您的映射器函数可以针对文本的每一行遍历此单词列表，如果它出现在该行中，它会返回一个值，例如 ['word', lineNum] ，该值存储在结果列表中。映射器本质上是一个 for 循环的包装器。然后，您可以通过编写一个 reducer 函数来获取您的结果列表并“减少”它，在这种情况下，您可以获取结果列表，它应该看起来像 [['word1', 1]...['word1', n] ...] 变成一个看起来像 {'word1': [1, 2, 5], 'word3': [7], ...} 的对象。
这种方法是有利的，因为您在对每个项目执行通用操作的同时抽象了迭代列表的过程，并且如果您的分析需求发生变化（就像他们经常做的那样），您只需要更改映射器/归约函数而无需触及其余部分编码。此外，这种方法是高度可并行化的，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，我根据你的问题实现了一个 map/reduce 版本，而不使用内置库。由于您没有指定数据的存储方式，因此我对此做了几个假设，即感兴趣的单词列表将以逗号分隔的文件形式提供。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，拆分任何非字母数字字符）。当然，这可能不适合您的需求，因此您可以将其更改为对您正在查看的文件有意义的任何内容。
我试图远离深奥的 python 特性（没有 lambdas！），所以希望实现是清晰的。最后一点，我使用循环来遍历文本文件的行，并使用 map 函数来遍历感兴趣的单词列表。您可以改用嵌套映射函数，但我想跟踪循环索引（因为您关心行号）。如果您真的想嵌套映射函数，您可以在读取文件时将数组行存储为行和行号的元组，或者您可以修改映射函数以返回索引，您可以选择。
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]))