CODEWITHSUNDEEP
pythonweb-scrapingbeautifulsoup
hojlund123
hojlund123

Reputation: 15

Splitting html from scraped data (Python+BeautifulSoup4)

I have run into a problem with scraping the text inside a tag without getting all the html data aswell. Here is my python code. The text I want to scrape is not inside a span class, and stands by itself in the tag. Here is an example of where the text is placed. 

<a href="/counterstrike/rankings/team-details/32537">
  <span class="ranking">49</span>
  <span class="flag flag-pl" data-tooltip="" tabindex="1" title="Poland></span>
  TEXT-I-WANT-TO-SCRAPE
  <span class="elo">1103</span>
</a>

If I use the ".text.encode('utf8').lstrip().rstrip()" function, I still get the data like this:

print(textt) '49\n \n\n\n TEXT-I-WANT-TO-SCRAPE \n \n 1103'

My question is how do I only get the text inside the tag?

It is no problem to scrape both elo and ranking because they are contained inside spans with specific classes. 

def get_matches():
matches = get_parsed_page("https://www.gosugamers.net/counterstrike/rankings")
rankings = matches.find("ul", {"class": "ranking-list"})
matchdays = rankings.find_all("li")

for match in matchdays:
    matchDetails = match.find_all("a")

    for getMatch in matchDetails:
        elo = match.find("span", {"class": "elo"}).text.encode('utf8').lstrip().rstrip()
        ranking = match.find("span", {"class": "ranking"}).text.encode('utf8').lstrip().rstrip()
        textt = match.find("a").text.encode('utf8').lstrip().rstrip()

        print(ranking,elo,textt)

Best regards

Upvotes: 1

Views: 104

Answers (1)

KunduK
KunduK

Reputation: 33384

Use next_element to get the text of next element of a tag.Try below code.Used regular expression to find particular href to scrape.

from bs4 import BeautifulSoup
import requests
import re
data=requests.get("https://www.gosugamers.net/counterstrike/rankings").text
soup=BeautifulSoup(data,'html.parser')
for a in soup.find_all('a',href=re.compile("/counterstrike/rankings/team-details")):
    ranking=a.find('span' , class_='ranking').text.replace('\n','').strip()
    name=a.find('span', class_='ranking').next_element.next_element.next_element.next_element.replace('\n','').strip()
    elo=a.find('span',class_='elo').text.replace('\n','').strip()
    print(ranking,name,elo)

Output:

1 Astralis 1505
2 Team Liquid 1469
3 ENCE eSports 1402
4 Vitality 1365
5 AVANGAR 1326
6 Natus Vincere 1298
7 Ninjas in Pyjamas 1294
8 fnatic 1292
9 MiBR 1269
10 FURIA 1264
11 mousesports 1258
12 Renegades 1252
13 NRG eSports 1248
14 ORDER 1240
15 Grayhound Gaming 1237
16 Valiance 1235
17 Windigo 1228
18 FaZe Clan 1222
19 North 1220
20 G2 Esports 1213
21 OpTic Gaming 1201
22 MVP PK 1196
23 Heroic 1183
24 Chiefs eSports Club 1177
25 3DMAX.CS 1173
26 HellRaisers 1168
27 Rogue 1167
28 BIG 1165
29 forZe 1165
30 Ghost Gaming 1159
31 Swole Patrol 1154
32 TyLoo 1151
33 Red Reserve 1142
34 Isurus Gaming 1142
35 Team Kinguin 1136
36 Tainted Minds 1135
37 Movistar Riders 1134
38 NoChance 1134
39 DETONA Gaming 1132
40 Space Soldiers 1120
41 Bravado Gaming 1117
42 BPro Gaming 1116
43 Cloud9 1116
44 GamerLegion 1113
45 CyberZen 1111
46 Epsilon 1111
47 CLG Red 1107
48 Luminosity Gaming 1107
49 devils.one 1103
50 Sprout 1096

Upvotes: 1

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