uj5u.com熱心網友回復:
實際上,資料也是從api呼叫json回應中生成的。以下是作業實體:
代碼:
代碼:
代碼:
import requests
import pandas as pd
import json
params = {
"queryid": "產品",
"anonymousId": "1073AB4A1C5F8BFD37D3302DFF7210E5",
"country": "us",
"endpoint": "/product_feed/rollup_threads/v2? filter=marketplace(US)&filter=language(en)&filter=employeePrice(true)&filter=attributeIds(5b21a62a-0503-400c-8336-3ccfbff2a684)&anchor=24&consumerChannelId=d9a5bc42-4b9c-4976-858a-f159cf99c647&count=24",
"語言"。"en",
"localizedRangeStr": "{lowestPrice} - {highestPrice}"。
}
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36'}。
data=[] 。
url= 'https://api.nike.com/cic/browse/v1'。
r = requests.get(url, params=params,headers = headers)
for item in r.json()['data']['products']。
data.append([
item['title']。
item['subtitle']。
item['colorDescription']。
item['images']['portraitURL'] 。
item['colorways'][0]['images'] ['portraitURL']。
item['colorways'][0]['images']['squareishURL']
])
cols=['Product_title','Product_subtitle','Product_color', 'Images-01','colorways-01','colorways-02']
df = pd.DataFrame(data, columns=cols)
print(df)
#df.to_csv('info.csv', index = False)
輸出:
Product_title ... colorways-02
0 Kyrie Flytrap 4 ... https://static.nike.com/a/images/t_default/33c ...
1 Nike ... https://static.nike.com/a/images/t_default/a8c...
2 Nike Sportswear Club Fleece ... https://static.nike.com/a/images/t_default/e77...
3 Nike Sportswear ... https://static.nike.com/a/images/t_default/bcc...
4 Nike Dri-FIT One ... https://static.nike.com/a/images/t_default/02b...
5 Nike React Miler 2 ... https://static.nike.com/a/images/t_default/261 ...
6 Nike Pegasus Trail 2 ... https://static.nike.com/a/images/t_default/517 ...
7 Nike TechKnit Ultra ... https://static.nike.com/a/images/t_default/d6f...
8 Nike Fast ... https://static.nike.com/a/images/t_default/c10...
9 Nike Air Max Infinity ... https://static.nike.com/a/images/t_default/d85...
10 Nike Free X Metcon 2 ... https://static.nike.com/a/images/t_default/wo6 ...
11 Nike Dri-FIT Indy Rainbow Ladder ... https://static.nike.com/a/images/t_default/085...
12 Nike One Rainbow Ladder ... https://static.nike.com/a/images/t_default/662...
13 Nike Revolution 5 ... https://static.nike.com/a/images/t_default/a9d...
14 Nike Revolution 5 ... https://static.nike.com/a/images/t_default/50B ...
15 Nike Sportswear ... https://static.nike.com/a/images/t_default/258...
16 Nike Dri-FIT Trophy ... https://static.nike.com/a/images/t_default/w1o...
17 Jordan ... https://static.nike.com/a/images/t _default/ca9...
18 Nike Dri-FIT Tempo ... https://static.nike.com/a/images/t_default/26e...
19 LeBron Soldier 14 ... https://static.nike.com/a/images/t_default/3a1 ...
20 KD Trey 5 VIII ... https://static.nike.com/a/images/t_default/i1-...
21 Nike Sportswear Windrunner Tech Pack ... https://static.nike.com/a/images/t_default/i1-...
22 USA Nike Therma Flex Showtime ... https://static.nike.com/a/images/t _default/7e1...
23 Nike Blazer Low '77 ... https://static.nike.com/a/images/t_default/b97...
[24行x6列]
uj5u.com熱心網友回復:
第一次呼叫URL
import requests
headers={"user-agent"。"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36"/span>}。
res=requests.get("https://www.nike.com/in/t/kyrie-flytrap-3-ep-basketball-shoe-0qb5vw/CD0191-103", headers=headers)
其次使用
bs4來尋找元素
from bs4 import BeautifulSoup
soup=BeautifulSoup(res.text,"html.parser")
第三:你可以手動從湯中找到只是URL,它將給你a標簽與類。在這一點上,我使用了css選擇器,在對其進行回圈后,我們 可以提取
href
main_data=soup.select("a.colorway-product-overlay"/span>)
for i in main_data:
print(i['href'] )
輸出:
https:/span>//www.nike. com/in/t/kyrie-flytrap-3-ep-basketball-shoe-0qb5vw/CD0191-104。
https://www.nike. com/in/t/kyrie-flytrap-3-ep-basketball-shoe-0qb5vw/CD0191-103。
https://www.nike. com/in/t/kyrie-flytrap-3-ep-basketball-shoe-0qb5vw/CD0191-006
https://www.nike. com/in/t/kyrie-flytrap-3-ep-basketball-shoe-0qb5vw/CD0191-100。
https://www.nike.com/in/t/kyrie-flytrap-3-ep-籃球鞋-0qb5vw/cd0191- 009
轉載請註明出處,本文鏈接:https://www.uj5u.com/houduan/331280.html
標籤:


