Challenge to crawl 100 videos per minute. As long as the network speed is fast enough, anti crawl will not catch up with me

Keywords: Python JSON Pycharm Windows


Most of the good videos are short videos! The same interface returns different videos to users

Today, I will take you down the video recommended by the system!

Knowledge points

1. Dynamic data capture demonstration

2. json data analysis method

3. Video data saving

Environment introduction

python 3.6




General thinking of reptiles

1. Analyze the target web page to determine the url path and headers parameters to be crawled

2. Send request -- requests simulate browser to send request and get response data

3. Parse data

4. Save data - save in destination folder


1. Import tool

import requests
import time
import pprint


2. Determine the url path to crawl, and the headers parameter





# Get timestamp
    //The time stamp refers to the total number of milliseconds from 00:00:00, January 1, 1970, GMT (08:00:00, January 1, 1970, Beijing time).
    //Second timestamps, 10 bits
    //Millisecond timestamps, 13 bits
    //Microsecond timestamps, 16 bit

time_one = str(int(time.time() * 1000))
# print(time_one)
base_url = '' + time_one
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36',
    'cookie': 'BIDUPSID=ABD6DB65092EB1ECFA3DB139E3DCDE8D; PSTM=1575868363; BAIDUID=ABD6DB65092EB1ECE63825000D8C97DB:FG=1; BDUSS=U1c0hpalFvb2ZLclIwY0tHSnA2T0ZLbjV3NDcyQmhkQ2FsV2VPbmptS1U1QzllRVFBQUFBJCQAAAAAAAAAAAEAAAD9hL2nuti-~M~oMzEzNjQxOQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJRXCF6UVwheZn; BDORZ=B490B5EBF6F3CD402E515D22BCDA1598; PC_TAB_LOG=haokan_website_page; Hm_lvt_4aadd610dfd2f5972f1efee2653a2bc5=1578978739,1578979115; BAIDU_SSP_lcr=; BDRCVFR[PaHiFN6tims]=9xWipS8B-FspA7EnHc1QhPEUf; delPer=0; PSINO=6; H_PS_PSSID=; Hm_lpvt_4aadd610dfd2f5972f1efee2653a2bc5=1578982791; reptileData=%7B%22data%22%3A%22ff38fdbd98456480e9c9c7834cbfeaa39236e14520ac985b719893846080819083f656303845fdcba03de7a67af409104bd1b7bccbc028b467f251922334608c1b34b919ef391c146a5ad41b8099df302ec0d32f3a55b4271300112ff8e8f12a1cde132ecaf78f8df8d9c97ddd9abefa4d7a4d8bdd641c156c016dba346150a8%22%2C%22key_id%22%3A%2230%22%2C%22sign%22%3A%226430f36d%22%7D'


3. Send request -- requests simulate browser to send request and get response data

response = requests.get(base_url, headers=headers)
data = response.json()
# pprint.pprint(data)

4. Parse data

data_list = data['data']['response']['videos']  # --list
# print(data_list)

# Traversal list
for data in data_list:
    video_name = data['title'] + '.rmvb'  # Video file name
    video_url = data['play_url']  # Video url address
    # print(video_name, video_url)
    # print(type(video_name))

    # Send request again
    print('Downloading:', video_name)
    video_data = requests.get(video_url, headers=headers).content

5. Save data - save in destination folder

 with open('video\\' + video_name, 'wb') as f:
        print('Download complete...\n')

Run the code, the effect is as follows

OK, so the video can be downloaded slowly

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Posted by nvidia on Wed, 13 May 2020 06:56:07 -0700