How do I create a column that tells me the the count of how many times a number appears?

Question

from bs4 import BeautifulSoup
import requests
import pandas as pd
import ast

s = requests.Session()

page=1
traits = []

#Get URL and extract content
class Scraper():

    while page != 10:
        content = s.get('https://bullsontheblock.com/api/tokens/{}'.format(page))
        soup = BeautifulSoup(content.text, 'html.parser')
        page = page + 1
    
        traits = ast.literal_eval(soup.text)['attributes']

        df = pd.DataFrame(traits).set_index('value').to_numpy()
        trait_count = len(df)
    
        print(trait_count)

Whenever I use the above code I get integers separated by line like this:

9
8
8
8
6
9
8
8
7

How do I create a column that tells me the the count of how many times a number appears so it looks something like this instead:

9 - 2
8 - 5
7 - 1
6 - 1

Basically the code above pulls the count of how many traits are in a list, but I have multiple lists so I would like to pull the count of how many times a list with a certain number of traits appear so it can look like the above. How do I go about this?

Answer 1

Just in case anyone else ever has the same problem I'm having: @garlic_rat's answer was spot on but I was still having trouble with trying to get it into a dataframe but I solved my problem by using the following code:

# Import libraries
from bs4 import BeautifulSoup
import requests
import pandas as pd
import time
import ast

start_time = time.time()
s = requests.Session()


#Get URL and extract content
page=1
traits = []
traits_e = []

while page != 10:
    content = s.get('https://bullsontheblock.com/api/tokens/{}'.format(page))
    soup = BeautifulSoup(content.text, 'html.parser')
    page = page + 1
    
    traits = ast.literal_eval(soup.text)['attributes']

    df = pd.DataFrame(traits).set_index('value').to_numpy()
    trait_count = len(df)
    traits_e.append(trait_count)
    
a = pd.DataFrame({'Trait Count': traits_e})
value_counts1 = a['Trait Count'].value_counts()
l1 = [f"{key} - {value_counts1[key]}" for key in value_counts1.keys()]
traits = l1

dfx = pd.DataFrame(
    traits, columns=['Trait Count'])

print(dfx)
print("--- %s seconds ---" % (time.time() - start_time))

By doing this I was able to receive the same data as @garlic_rat's answer but this also allowed me to put it into a dataframe column.

Answer 2

As mentioned in comments and @PyxlDavon's answer, you likely want to use a dictionary:

from bs4 import BeautifulSoup
import requests
import pandas as pd
import ast

s = requests.Session()

page=1
traits = []
traits_d = {}

#Get URL and extract content
class Scraper():
    
    while page != 10:
        content = s.get('https://bullsontheblock.com/api/tokens/{}'.format(page))
        soup = BeautifulSoup(content.text, 'html.parser')
        page = page + 1

        traits = ast.literal_eval(soup.text)['attributes']

        df = pd.DataFrame(traits).set_index('value').to_numpy()
        trait_count = len(df)

        if trait_count in traits_d:
            traits_d[trait_count] += 1
        else:
            traits_d[trait_count] = 1

traits_d contains the trait and number of times seen:

traits_d
{9: 2, 8: 5, 6: 1, 7: 1}

To print the keys out as you show, loop through the sorted keys:

       
for key in sorted(traits_d.keys(), reverse=True):
    print(key, '-', traits_d[key])

9 - 2
8 - 5
7 - 1
6 - 1

Answer 3

Add each numbers to a string, then count the frequency of each number

nums = "988869887"
fre = {}

for num in nums:
    if num in fre:
        fre[int(num)] += 1
    else:
        fre[int(num)] = 1

print(fre)

Output {9: 2, 8: 5, 6: 1, 7: 1}