Python快速写入大文件内容

本文将从多个方面详细阐述如何使用Python快速写入大文件内容。

一、文件写入方法

1、使用文件对象的write()方法逐行写入文件内容。

with open("large_file.txt", "w") as f:
    for i in range(1000000):
        f.write("This is line %d\n" % (i+1))

2、使用文件对象的writelines()方法一次性写入多行内容。

lines = []
for i in range(1000000):
    lines.append("This is line %d\n" % (i+1))

with open("large_file.txt", "w") as f:
    f.writelines(lines)

二、优化写入速度

1、使用缓冲区进行批量写入，例如每次写入1000行内容。

lines = []
for i in range(1000000):
    lines.append("This is line %d\n" % (i+1))

with open("large_file.txt", "w", buffering=1000) as f:
    f.writelines(lines)

2、使用多线程或多进程进行并发写入操作。

import threading

def write_lines(filename, start, end):
    lines = []
    for i in range(start, end):
        lines.append("This is line %d\n" % (i+1))

    with open(filename, "a") as f:
        f.writelines(lines)

num_threads = 4
num_lines = 1000000
chunk_size = num_lines // num_threads

threads = []
for i in range(num_threads):
    start = i * chunk_size
    end = start + chunk_size
    t = threading.Thread(target=write_lines, args=("large_file.txt", start, end))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

三、使用高性能库

1、使用numpy库进行大文件内容的写入。

import numpy as np

lines = []
for i in range(1000000):
    lines.append("This is line %d\n" % (i+1))

np.savetxt("large_file.txt", lines, fmt="%s")

2、使用pandas库进行大文件内容的写入。

import pandas as pd

lines = []
for i in range(1000000):
    lines.append(["This is line %d" % (i+1)])

df = pd.DataFrame(lines)
df.to_csv("large_file.txt", index=False, header=False)

四、使用第三方库

1、使用tqdm库增加进度条显示，提升用户体验。

from tqdm import tqdm

lines = []
for i in tqdm(range(1000000)):
    lines.append("This is line %d\n" % (i+1))

with open("large_file.txt", "w") as f:
    f.writelines(lines)

2、使用dask库进行分布式写入，利用多台机器的计算资源。

import dask.dataframe as dd

lines = []
for i in range(1000000):
    lines.append(["This is line %d" % (i+1)])

df = dd.from_pandas(pd.DataFrame(lines), npartitions=4)
df.to_csv("large_file.txt", index=False, header=False)

通过以上方法，我们可以在Python中快速写入大文件内容，并且根据实际需求选择合适的方法和库来提升写入速度和性能。