I have a large file ( ~4G) to process in Python. I wonder whether it is OK to "read" such a large file. So I tried in the following several ways: The original large file to deal with is not "./CentOS-6.5-i386.is
Subsequent calls toreadline()will continue reading from the position where the previous read left off. This makesreadline()a suitable method for reading large files line by line without loading the entire file into memory at once. try:withopen('data.txt', 'r')as file:line=file.readline()wh...
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def read_file_line_by_line(filepath): lines = [] with open(filepath, 'r') as file: for line in file: lines.append(line.strip()) # 去除每行的换行符并添加到列表中 return lines # 使用示例 file_lines = read_file_line_by_line("large_file.txt") for line in file_lines: print(lin...
file=open('example.txt','r')line=file.readline()whileline:print(line)line=file.readline() 1. 2. 3. 4. 5. 上述代码会读取example.txt文件的每一行,并逐行打印出来,直到文件的末尾。可以根据需要对每一行进行进一步处理。 使用with语句 在处理文件时,我们通常使用with语句来自动关闭文件。with语句会在代...
We can use the file object as an iterator. The iterator will return each line one by one, which can be processed. This will not read the whole file into memory and it’s suitable to read large files in Python. Here is the code snippet to read large file in Python by treating it as...
逐行遍历文件（Iterate through the file line by line:）：法一：一次读入，分行处理 Method 1: One read-in, branch processing 法二：分行读入，逐行处理 Method 2: Read in branches and process line by line 写入文本的三种方法：write()、writelines()、seek（）Three ways to write text: write(), ...
Reading File in Chunks The read() (without argument) and readlines() methods reads the all data into memory at once. So don't use them to read large files. A better approach is to read the file in chunks using the read() or read the file line by line using the readline(), as ...
(1)<file>.read(size=-1) #从文件中读取整个文件内容,如果给出参数,读入前size长度的字符串(文本文件)或字节流(二进制文件),size=-1默认读取全部。 栗子1. #test2_1.py文件 with open("English_Study_Dict.txt",'rt') as file: a=file.readable() #判断文件是否可读取 b=file.writable() #判断文件...
read_csv( 'large.csv', chunksize=chunksize, dtype=dtype_map ) # # 然后每个chunk进行一些压缩内存的操作,比如全都转成sparse类型 # string类型比如,学历,可以转化成sparse的category变量,可以省很多内存 sdf = pd.concat( chunk.to_sparse(fill_value=0.0) for chunk in chunks ) #很稀疏有可能可以装的...