Pandas - Add items to dataframe - python-3.x

Question

I am trying to add row items to the dataframe, and I am not able to update the dataframe.
What i tried until now is commented out as it doesn't do what I need.
I simply want to download the json file and store it to a dataframe with those given columns. Seems I am not able to extract the child components fron JSON file and store them to a brand new dataframe.
Please find bellow my code:
import requests, json, urllib
import pandas as pd
url = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
data = pd.read_json(url)
headers = []
df = pd.DataFrame()
for key, item in data['vulnerabilities'].items():
for k in item.keys():
headers.append(k)
col = list(set(headers))
new_df = pd.DataFrame(columns=col)
for item in data['vulnerabilities'].items():
print(item[1])
# new_df['product'] = item[1]['product']
# new_df['vendorProject'] = item[1]['vendorProject']
# new_df['dueDate'] = item[1]['dueDate']
# new_df['shortDescription'] = item[1]['shortDescription']
# new_df['dateAdded'] = item[1]['dateAdded']
# new_df['vulnerabilityName'] = item[1]['vulnerabilityName']
# new_df['cveID'] = item[1]['cveID']
# new_df.append(item[1], ignore_index = True)
new_df
At the end my df is still blank.

Answer 1

The nested JSON data can be directly converted to a flattened dataframe using pd.json_normalize(). The headers are extracted from the JSON itself.
new_df = pd.DataFrame(pd.json_normalize(data['vulnerabilities']))
UPDATE: Unnested the vulnerabilities column specifically.
Output:

Answer 2

It worked with this:
import requests, json, urllib
import pandas as pd
url = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
data = pd.read_json(url)
headers = []
df = pd.DataFrame()
for key, item in data['vulnerabilities'].items():
for k in item.keys():
headers.append(k)
col = list(set(headers))
new_df = pd.DataFrame(columns=col)
for item in data['vulnerabilities'].items():
new_df.loc[len(new_df.index)] = item[1] <===THIS
new_df.head()