file = open('demo.txt', 'w')
file.write('Hello World')
file.close()  # 必須顯式關閉

with open('demo.txt', 'w') as f:
    f.write('Auto-closed file')  # 退出代碼塊自動關閉

with open('example.txt', 'r') as f:
    full_content = f.read()
readline()：逐行讀取，返回單行字符串
python
with open('example.txt', 'r') as f:
    first_line = f.readline()
readlines()：返回包含所有行的列表
python
with open('example.txt', 'r') as f:
    lines_list = f.readlines()

with open('output.txt', 'w') as f:
    f.write('Line 1\nLine 2')  # 需自行添加換行符
writelines()：寫入字符串列表（不自動換行）
python
lines = ['Line 1\n', 'Line 2\n']
with open('output.txt', 'w') as f:
    f.writelines(lines)  # 需確保列表元素含換行符

with open('example.txt', 'r') as f:
    print(f.tell())  # 初始位置0
    f.read(5)
    print(f.tell())  # 讀取5字符后位置5

# 復制圖片文件
with open('image.jpg', 'rb') as src:
    binary_data = src.read()
with open('copy.jpg', 'wb') as dst:
    dst.write(binary_data)

try:
    with open('missing.txt', 'r') as f:
        content = f.read()
except FileNotFoundError:
    print("文件不存在！")
except PermissionError:
    print("無讀取權限！")

# 提取包含"ERROR"的日志條目
with open('app.log', 'r') as f:
    errors = [line for line in f if 'ERROR' in line]
    for error in errors:
        print(error.strip())

import pandas as pd
 
# 讀取CSV文件
df = pd.read_csv('sales.csv')
# 刪除缺失值
df.dropna(inplace=True)
# 保存清洗結果
df.to_csv('cleaned_sales.csv', index=False)

block_size = 1024 * 1024  # 1MB塊大小
with open('large_file.bin', 'rb') as f:
    while True:
        chunk = f.read(block_size)
        if not chunk:
            break
        # 處理當前數據塊

def read_in_chunks(file_path, chunk_size):
    with open(file_path, 'r') as f:
        while True:
            data = f.read(chunk_size)
            if not data:
                break
            yield data
 
for chunk in read_in_chunks('huge.log', 4096):
    process(chunk)  # 自定義處理函數

import json
 
# 讀取配置
with open('config.json', 'r') as f:
    config = json.load(f)
# 修改配置
config['debug'] = True
# 寫回文件
with open('config.json', 'w') as f:
    json.dump(config, f, indent=4)

import yaml
 
with open('settings.yaml', 'r') as f:
    settings = yaml.safe_load(f)
# 修改參數
settings['max_connections'] = 100
with open('settings.yaml', 'w') as f:
    yaml.dump(settings, f)

# 行緩沖模式（文本模式）
with open('realtime.log', 'w', buffering=1) as f:
    f.write('Log entry\n')  # 立即刷新緩沖區(qū)
 
# 自定義緩沖區(qū)大?。ǘM制模式）
with open('data.bin', 'wb', buffering=8192) as f:
    f.write(b'X'*16384)  # 每次寫入8KB

import mmap
 
with open('huge_file.bin', 'r+b') as f:
    mm = mmap.mmap(f.fileno(), 0)
    # 像操作字符串一樣處理文件
    mm.find(b'pattern')
    mm.close()  # 修改自動同步到磁盤

# 指定正確編碼（如GBK文件）
with open('chinese.txt', 'r', encoding='gbk') as f:
    content = f.read()
 
# 忽略無法解碼的字符
with open('corrupted.txt', 'r', errors='ignore') as f:
    content = f.read()

import fcntl  # Linux/Unix系統(tǒng)
 
with open('critical.dat', 'r') as f:
    fcntl.flock(f, fcntl.LOCK_SH)  # 共享鎖
    # 讀取操作
    fcntl.flock(f, fcntl.LOCK_UN)  # 釋放鎖

from pathlib import Path
 
# 跨平臺路徑操作
file_path = Path('documents') / 'report.txt'
# 擴展名處理
if file_path.suffix == '.tmp':
    file_path.rename(file_path.with_suffix('.bak'))

import aiofiles
async with aiofiles.open('data.txt', 'r') as f:
    content = await f.read()