老派方法:

fh = open(file_name, 'rt')
line = fh.readline()
while line:
    # do stuff with line
    line = fh.readline()
fh.close()

老派方法:

fh = open(file_name, 'rt')
line = fh.readline()
while line:
    # do stuff with line
    line = fh.readline()
fh.close()

我不敢相信这能像@john-la-rooy的回答看起来那么简单。因此，我使用逐行读写重新创建了cp命令。这是疯狂的快。

#!/usr/bin/env python3.6

import sys

with open(sys.argv[2], 'w') as outfile:
    with open(sys.argv[1]) as infile:
        for line in infile:
            outfile.write(line)

下面是加载任何大小的文本文件而不会导致内存问题的代码。 它支持千兆字节大小的文件

https://gist.github.com/iyvinjose/e6c1cb2821abd5f01fd1b9065cbc759d

下载文件data_loading_utils.py并将其导入到代码中

使用

import data_loading_utils.py.py
file_name = 'file_name.ext'
CHUNK_SIZE = 1000000


def process_lines(data, eof, file_name):

    # check if end of file reached
    if not eof:
         # process data, data is one single line of the file

    else:
         # end of file reached

data_loading_utils.read_lines_from_file_as_data_chunks(file_name, chunk_size=CHUNK_SIZE, callback=self.process_lines)

Process_lines方法是回调函数。它将对所有行调用，参数数据每次表示文件的一行。

您可以根据您的机器硬件配置来配置变量CHUNK_SIZE。

如果你在文件中没有换行符，你可以这样做:

with open('large_text.txt') as f:
  while True:
    c = f.read(1024)
    if not c:
      break
    print(c,end='')

我意识到这个问题在很久以前就已经回答过了，但是这里有一种并行的方法，而不会杀死您的内存开销(如果您试图将每一行放入池中，就会出现这种情况)。显然，将readJSON_line2函数替换为一些合理的函数——这只是为了说明这一点!

加速将取决于文件大小和你对每一行所做的事情-但最坏的情况是，对于一个小文件，只是用JSON阅读器读取它，我看到下面设置的性能与ST相似。

希望对大家有用:

def readJSON_line2(linesIn):
  #Function for reading a chunk of json lines
   '''
   Note, this function is nonsensical. A user would never use the approach suggested 
   for reading in a JSON file, 
   its role is to evaluate the MT approach for full line by line processing to both 
   increase speed and reduce memory overhead
   '''
   import json

   linesRtn = []
   for lineIn in linesIn:

       if lineIn.strip() != 0:
           lineRtn = json.loads(lineIn)
       else:
           lineRtn = ""
        
       linesRtn.append(lineRtn)

   return linesRtn




# -------------------------------------------------------------------
if __name__ == "__main__":
   import multiprocessing as mp

   path1 = "C:\\user\\Documents\\"
   file1 = "someBigJson.json"

   nBuffer = 20*nCPUs  # How many chunks are queued up (so cpus aren't waiting on processes spawning)
   nChunk = 1000 # How many lines are in each chunk
   #Both of the above will require balancing speed against memory overhead

   iJob = 0  #Tracker for SMP jobs submitted into pool
   iiJob = 0  #Tracker for SMP jobs extracted back out of pool

   jobs = []  #SMP job holder
   MTres3 = []  #Final result holder
   chunk = []  
   iBuffer = 0 # Buffer line count
   with open(path1+file1) as f:
      for line in f:
            
          #Send to the chunk
          if len(chunk) < nChunk:
              chunk.append(line)
          else:
              #Chunk full
              #Don't forget to add the current line to chunk
              chunk.append(line)
                
              #Then add the chunk to the buffer (submit to SMP pool)                  
              jobs.append(pool.apply_async(readJSON_line2, args=(chunk,)))
              iJob +=1
              iBuffer +=1
              #Clear the chunk for the next batch of entries
              chunk = []
                            
          #Buffer is full, any more chunks submitted would cause undue memory overhead
          #(Partially) empty the buffer
          if iBuffer >= nBuffer:
              temp1 = jobs[iiJob].get()
              for rtnLine1 in temp1:
                  MTres3.append(rtnLine1)
              iBuffer -=1
              iiJob+=1
            
      #Submit the last chunk if it exists (as it would not have been submitted to SMP buffer)
      if chunk:
          jobs.append(pool.apply_async(readJSON_line2, args=(chunk,)))
          iJob +=1
          iBuffer +=1

      #And gather up the last of the buffer, including the final chunk
      while iiJob < iJob:
          temp1 = jobs[iiJob].get()
          for rtnLine1 in temp1:
              MTres3.append(rtnLine1)
          iiJob+=1

   #Cleanup
   del chunk, jobs, temp1
   pool.close()