Python读大数据txt
如果直接对大文件对象调用 read() 方法，会导致不可预测的内存占用。好的方法是利用固定长度的缓冲区来不断读取文件内容。即通过yield。
在用Python读一个两个多G的txt文本时，天真的直接用readlines方法，结果一运行内存就崩了。
还好同事点拨了下，用yield方法，测试了下果然毫无压力。咎其原因，原来是readlines是把文本内容全部放于内存中，而yield则是类似于生成器。
代码如下：
def open_txt(file_name):
with open(file_name,'r+') as f:
while True:
line = f.readline()
if not line:
return
yield line.strip()
调用实例：
for text in open_txt('aa.txt'):
print text
例二：
目标 txt 文件大概有6G，想取出前面1000条数据保存于一个新的 txt 文件中做余下的操作，虽然不知道这样做有没有必要但还是先小数据量测试一下吧。参考这个帖子：我想把一个list列表保存到一个Txt文档，该怎么保存 ，自己写了一个简单的小程序。
====================================================
import datetime
import pickle
start = datetime.datetime.now()
print "start--%s" % (start)
fileHandle = open ( 'train.txt' )
file2 = open('s_train.txt','w')
i = 1
while ( i < 10000 ):
a = fileHandle.readline()
file2.write(''.join(a))
i = i + 1
fileHandle.close()
file2.close()
print "done--%s" % ( datetime.datetime.now() - start)
if __name__ == '__main__':
pass
====================================================
pickle 这个库大家说的很多，官网看看，后面可以好好学习一下。
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