Html – Why is my IF statement giving me the wrong output?

There is no <th> tags with the text 'Jimmy Butler', hence that if statement will return False, and go to your else statement. Based on your description, I am going to assume you are trying to pull stats from the team site 'https://www.basketball-reference.com/teams/MIA/2023.html‘

There are a few other things you need to fix and I’ll comment it in the code:

import requests
from bs4 import BeautifulSoup

# URL of the webpage with Jimmy Butler's statistics
url = "https://www.basketball-reference.com/teams/MIA/2023.html" # Team stats page

# Send a GET request to the webpage
response = requests.get(url)
html_content = response.content

# Create a BeautifulSoup object to parse the HTML content
soup = BeautifulSoup(html_content, "html.parser") # you stored response.content, so might as well use that variable here instead of response.content

# Find the HTML table that contains the statistics
table = soup.find("table", {"id": "per_game"})

# Extract the table headers (column names)
headers = table.find("thead").find_all("th")
column_names = [header.text for header in headers]

# Find the row that corresponds to Jimmy Butler
rows = table.find("tbody").find_all("tr")
jimmy_butler_row = None
for row in rows:
    if row.find("td").text == "Jimmy Butler":   # the text Jimmy Butler is under a td tag, not a th tag
        jimmy_butler_row = row
        break

# Check if the row for Jimmy Butler was found
if jimmy_butler_row is not None:
    # Extract the statistics for Jimmy Butler
    pts = jimmy_butler_row.find('td', {'data-stat': 'pts_per_g'}).text     # Heres how you want to grab those stats using the tags and attributes
    ast = jimmy_butler_row.find('td', {'data-stat': 'ast_per_g'}).text
    fg_percentage = jimmy_butler_row.find('td', {'data-stat': 'fg_pct'}).text
    ft_percentage = jimmy_butler_row.find('td', {'data-stat': 'ft_pct'}).text
    
    # Store the extracted statistics in a data structure
    jimmy_butler_stats = {
        "Points (PTS)": pts,
        "Assists (AST)": ast,
        "Field Goal Percentage (FG%)": fg_percentage,
        "Free Throw Percentage (FT%)": ft_percentage
    }
    
    # Print the extracted statistics
    print("Jimmy Butler's Statistics:")
    for stat_name, stat_value in jimmy_butler_stats.items():
        print(stat_name + ":", stat_value)
else:
    print("Jimmy Butler's statistics not found.")

Output:

Jimmy Butler's Statistics:
Points (PTS): 22.9
Assists (AST): 5.3
Field Goal Percentage (FG%): .539
Free Throw Percentage (FT%): .850

Lastly, tables are a great way to learn beautifulsoup and html since they are well structured. However, once you get a hang of it, consider using Pandas to pull table tags:

import pandas as pd

url = "https://www.basketball-reference.com/teams/MIA/2023.html"
df = pd.read_html(url, attrs={'id':'per_game'})

Then could just filter the df.

Output:

print(df)
[    Rk             Player  Age   G  GS    MP  ...  AST  STL  BLK  TOV   PF   PTS
0    1        Tyler Herro   23  67  67  34.9  ...  4.2  0.8  0.2  2.4  1.5  20.1
1    2        Bam Adebayo   25  75  75  34.6  ...  3.2  1.2  0.8  2.5  2.8  20.4
2    3       Jimmy Butler   33  64  64  33.4  ...  5.3  1.8  0.3  1.6  1.3  22.9
3    4         Kyle Lowry   36  55  44  31.2  ...  5.1  1.0  0.4  1.9  2.6  11.2
4    5       Caleb Martin   27  71  49  29.3  ...  1.6  1.0  0.4  1.1  2.0   9.6
5    6          Max Strus   26  80  33  28.4  ...  2.1  0.5  0.2  0.9  2.1  11.5
6    7     Victor Oladipo   30  42   2  26.3  ...  3.5  1.4  0.3  2.1  2.4  10.7
7    8       Gabe Vincent   26  68  34  25.9  ...  2.5  0.9  0.1  1.4  2.3   9.4
8    9         Kevin Love   34  21  17  20.0  ...  1.9  0.4  0.2  1.1  1.5   7.7
9   10  Haywood Highsmith   26  54  11  17.9  ...  0.8  0.7  0.3  0.8  1.5   4.4
10  11    Duncan Robinson   28  42   1  16.5  ...  1.1  0.3  0.0  0.7  1.8   6.4
11  12     Jamaree Bouyea   23   4   0  16.3  ...  1.0  1.0  0.5  1.0  1.3   3.8
12  13        Cody Zeller   30  15   2  14.5  ...  0.7  0.2  0.3  0.9  2.2   6.5
13  14   Orlando Robinson   22  31   1  13.7  ...  0.8  0.4  0.4  0.5  1.7   3.7
14  15       Nikola Jović   19  15   8  13.6  ...  0.7  0.5  0.1  0.7  1.3   5.5
15  16          Dru Smith   25   5   1  13.4  ...  1.0  0.8  0.6  0.2  2.0   2.2
16  17         Jamal Cain   23  18   0  13.3  ...  0.7  0.6  0.1  0.3  1.2   5.4
17  18     Dewayne Dedmon   33  30   0  11.7  ...  0.5  0.2  0.5  0.6  2.0   5.7
18  19      Udonis Haslem   42   7   1  10.1  ...  0.0  0.1  0.3  0.1  1.6   3.9
19  20     Omer Yurtseven   24   9   0   9.2  ...  0.2  0.2  0.2  0.4  1.8   4.4

[20 rows x 28 columns]]