你可以使用keras.utils。Get_file来做它:

from tensorflow import keras

path_to_downloaded_file = keras.utils.get_file(
    fname="file name",
    origin="https://www.linktofile.com/link/to/file",
    extract=True,
    archive_format="zip",  # downloaded file format
    cache_dir="/",  # cache and extract in current directory
)

我想从网页上下载所有的文件。我尝试了wget，但它失败了，所以我决定使用Python路由，我找到了这个线程。

读完之后，我做了一个小的命令行应用程序，soupget，扩展了PabloG和Stan的优秀答案，并添加了一些有用的选项。

它使用BeatifulSoup收集页面的所有url，然后下载具有所需扩展名的url。最后，它可以同时下载多个文件。

下面就是:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import (division, absolute_import, print_function, unicode_literals)
import sys, os, argparse
from bs4 import BeautifulSoup

# --- insert Stan's script here ---
# if sys.version_info >= (3,): 
#...
#...
# def download_file(url, dest=None): 
#...
#...

# --- new stuff ---
def collect_all_url(page_url, extensions):
    """
    Recovers all links in page_url checking for all the desired extensions
    """
    conn = urllib2.urlopen(page_url)
    html = conn.read()
    soup = BeautifulSoup(html, 'lxml')
    links = soup.find_all('a')

    results = []    
    for tag in links:
        link = tag.get('href', None)
        if link is not None: 
            for e in extensions:
                if e in link:
                    # Fallback for badly defined links
                    # checks for missing scheme or netloc
                    if bool(urlparse.urlparse(link).scheme) and bool(urlparse.urlparse(link).netloc):
                        results.append(link)
                    else:
                        new_url=urlparse.urljoin(page_url,link)                        
                        results.append(new_url)
    return results

if __name__ == "__main__":  # Only run if this file is called directly
    # Command line arguments
    parser = argparse.ArgumentParser(
        description='Download all files from a webpage.')
    parser.add_argument(
        '-u', '--url', 
        help='Page url to request')
    parser.add_argument(
        '-e', '--ext', 
        nargs='+',
        help='Extension(s) to find')    
    parser.add_argument(
        '-d', '--dest', 
        default=None,
        help='Destination where to save the files')
    parser.add_argument(
        '-p', '--par', 
        action='store_true', default=False, 
        help="Turns on parallel download")
    args = parser.parse_args()

    # Recover files to download
    all_links = collect_all_url(args.url, args.ext)

    # Download
    if not args.par:
        for l in all_links:
            try:
                filename = download_file(l, args.dest)
                print(l)
            except Exception as e:
                print("Error while downloading: {}".format(e))
    else:
        from multiprocessing.pool import ThreadPool
        results = ThreadPool(10).imap_unordered(
            lambda x: download_file(x, args.dest), all_links)
        for p in results:
            print(p)

它的用法示例如下:

python3 soupget.py -p -e <list of extensions> -d <destination_folder> -u <target_webpage>

还有一个实际的例子，如果你想看到它的作用:

python3 soupget.py -p -e .xlsx .pdf .csv -u https://healthdata.gov/dataset/chemicals-cosmetics

你可以使用keras.utils。Get_file来做它:

from tensorflow import keras

path_to_downloaded_file = keras.utils.get_file(
    fname="file name",
    origin="https://www.linktofile.com/link/to/file",
    extract=True,
    archive_format="zip",  # downloaded file format
    cache_dir="/",  # cache and extract in current directory
)

还有一个，使用urlretrieve:

import urllib.request
urllib.request.urlretrieve("http://www.example.com/songs/mp3.mp3", "mp3.mp3")

(对于Python 2使用import urllib和urllib.urlretrieve)

import os,requests
def download(url):
    get_response = requests.get(url,stream=True)
    file_name  = url.split("/")[-1]
    with open(file_name, 'wb') as f:
        for chunk in get_response.iter_content(chunk_size=1024):
            if chunk: # filter out keep-alive new chunks
                f.write(chunk)


download("https://example.com/example.jpg")

延迟回答，但对于python>=3.6，您可以使用:

import dload
dload.save(url)

安装dload:

pip3 install dload