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How can I get data from this website? It seems a json structure. It possible get it with BeautifulSoup?

url: https://www.ultimatetennisstatistics.com/statsLeadersTable?current=1&rowCount=-1&sort%5Bvalue%5D=desc&searchPhrase=&category=aces&season=&fromDate=&toDate=&level=&bestOf=&surface=&indoor=&speed=&round=&result=&tournamentId=&opponent=&countryId=&minEntries=&active=true&_=1622884929848

import requests
from bs4 import BeautifulSoup
import pandas as pd

r = requests.get(url)
soup = BeautifulSoup(r.text, 'lxml')

Answers

You can construct pandas dataframe from the data directly. For example:

import requests
import pandas as pd

url = "https://www.ultimatetennisstatistics.com/statsLeadersTable?current=1&rowCount=-1&sort%5Bvalue%5D=desc&searchPhrase=&category=aces&season=&fromDate=&toDate=&level=&bestOf=&surface=&indoor=&speed=&round=&result=&tournamentId=&opponent=&countryId=&minEntries=&active=true&_=1622884929848"
data = requests.get(url).json()

df = pd.json_normalize(data["rows"])
print(df)
df.to_csv("data.csv", index=False)

Prints:

     rank  playerId                          name  value               country.name country.id country.code
0       1      3333                  Ivo Karlovic  13687                    Croatia        CRO           hr
1       2      4544                    John Isner  12806              United States        USA           us
2       3      3819                 Roger Federer  11371                Switzerland        SUI           ch
3       5      3852               Feliciano Lopez   9920                      Spain        ESP           es
4       8      5016                   Sam Querrey   8466              United States        USA           us
5       9      5670                  Milos Raonic   8130                     Canada        CAN           ca
6      13      4728                Kevin Anderson   7262               South Africa        RSA           za
7      14      5220                   Marin Cilic   7246                    Croatia        CRO           hr
8      17      4541            Jo Wilfried Tsonga   6634                     France        FRA           fr
9      18      4789                  Gael Monfils   6245                     France        FRA           fr
10     20      4920                Novak Djokovic   6069                     Serbia        SRB           rs
11     21      4526                 Stan Wawrinka   5900                Switzerland        SUI           ch

...

And saves data.csv (screenshot from LibreOffice):