以下是一个示例代码,展示了如何分块读取大文件: filename="large_file.txt"block_size=4096withopen(filename,"rb")asfile:whileTrue:block=file.read(block_size)ifnotblock:break# 处理每个块的逻辑pass 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 在上面的代码中,我们使用open函数打开文件,并使用with语句...
defread_large_file(file_path):withopen(file_path,'r')asfile:forlineinfile:yieldline file_path='large_text.txt'forlineinread_large_file(file_path):process_line(line) 1. 2. 3. 4. 5. 6. 7. 8. 上述代码中,read_large_file函数通过yield关键字返回每一行文本数据,而在for循环中逐行处理文本...
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...
There are three ways to read the contents of a text file: read(), readline(), and readlines().1、read()方法 read（）方法表示一次读取文件全部内容，该方法返回字符串。The read() method means reading the entire contents of the file at once, and the method returns a string.2. The readline...
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.iso", I just take this file as an example here. ...
要读取个大文件,文件大概是3G左右,担心read会出现内存溢出的情况,网上找了个靠谱的用法: withopen(...)asf:forlineinf: <do somethingwithline> Thewithstatement handles opening and closing the file, including if an exception is raised in the inner block. Thefor line in ftreats the file objectfas ...
The Path.read_text function opens the file in text mode, reads it, and closes the file. It is a convenience function for easy reading of text. It should not be used for large files. main.py #!/usr/bin/python from pathlib import Path path = Path('words.txt') content = path.read_...
If all went well, this should have created a file calledLondon_Sundays_2000.xlsx, and then saved our data toSheet1. Open this file up in Excel or LibreOffice, and confirm that the data is correct. Mark as Completed Share 🐍 Python Tricks 💌 ...
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...
file_reader.py with open('pi_digits.txt') as file_object: contents = file_object.read() print(contents) 在这个程序中,第一行代码做了大量的工作。我们先来看看函数open()。 要以任何方式使用文件,那怕仅仅是打印其内容,都得先打开文件,才能访问它。