Python实现高效地读写大型文件

def read_large_file_line_by_line(file_path):
    with open(file_path, 'r') as file:
        for line in file:
            # 处理每一行的数据，这里仅打印
            print(line.strip())

def read_large_file_in_chunks(file_path, chunk_size=1024):
    with open(file_path, 'r') as file:
        while True:
            data = file.read(chunk_size)
            if not data:
                break
            # 处理读取到的数据块，这里仅打印
            print(data)

import mmap

def read_large_file_with_mmap(file_path):
    with open(file_path, 'r') as file:
        with mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) as mmap_obj:
            # 处理映射的数据，这里仅打印
            print(mmap_obj.readline())

import pandas as pd

def read_large_csv_in_chunks(csv_file_path):
    chunk_size = 100000  # 每块的行数
    for chunk in pd.read_csv(csv_file_path, chunksize=chunk_size):
        # 处理数据块，这里仅打印
        print(chunk)

import numpy as np

def read_large_binary_in_chunks(binary_file_path, chunk_size=1024):
    with open(binary_file_path, 'rb') as file:
        while True:
            data = np.fromfile(file, dtype=np.float32, count=chunk_size)
            if data.size == 0:
                break
            # 处理数据块，这里仅打印
            print(data)

import itertools

def read_large_file_with_itertools(file_path, chunk_size=1024):
    with open(file_path, 'r') as file:
        for chunk in itertools.zip_longest(*[iter(file)]*chunk_size):
            chunk = [line.strip() for line in chunk if line]
            # 处理数据块，这里仅打印
            print(chunk)

import linecache

def read_large_file_with_linecache(file_path, line_number):
    line = linecache.getline(file_path, line_number)
    # 处理指定行的数据，这里仅打印
    print(line.strip())