How can I make TikTok scraper scrape and add to CSV

I am building this scraper and I am trying to scrape TikTok hastags, then get the username from that hashtag, followed by scarping each username that I previously scraped. When finished want the information to be organized in a csv file. This is what I came up with but it is not working as I expected. I am a beginner and I am trying to learn a new language. What am I missing and doing wrong?

import requests
import json
import pandas as pd

# scrape hastag
url = "https://api.tikapi.io/public/hashtag?count=30&id=9261"

payload={}
headers = {
  'X-API-KEY': 'xxxxxx'
}

response = requests.request("GET", url, headers=headers, data=payload)
hashtag_response = response.text
hashtag_json = json.loads (hashtag_response)

# write data to hashtag json file
results_json = open("data.json", "w")
L = [response.text]
results_json.writelines(L)
results_json.close()

# list
influencer = []

followerCount = []
bioLink = []
signature = []


for uniqueId in hashtag_json ['uniqueId']:
   influencer.append(uniqueId)


# scrape influencer username
url = "https://api.tikapi.io/public/check?username={influencer}"

payload={}
headers = {
  'X-API-KEY': 'xxxxx'
}

influencerresponse = requests.request("GET", url, headers=headers, data=payload)
infl_response = influencerresponse.text
influencer_json = json.loads (infl_response)

# write data to influencer json file
results_json = open("infl_data.json", "w")
I = [influencerresponse.text]
results_json.writelines(I)
results_json.close()

for followerCount, bioLink in influencer_json ['followerCount','bioLink','signature']:
    followerCount.append(followerCount)
    bioLink.append(bioLink)
    signature.append(signature)

#  create csv file of results 

influencer_df = pd.DataFrame({  
    'Influencer' : influencer,
    'Follower Count' : followerCount,
    'Link' : bioLink,
    'Signature' : signature,   

    })

influencer_df.to_csv('/Users/john/Repos/TikTok/influencer.csv', index=False)

Solution

you were wrong in this part

for uniqueId in hashtag_json ['uniqueId']:
   influencer.append(uniqueId)

should be

influencer.append(hashtag_json["itemList"][0]['author']['uniqueId'])

and in this part

for followerCount, bioLink in influencer_json ['followerCount','bioLink','signature']:

should be

followerCount.append(influencer_json['userInfo']['stats']['followerCount'])
bioLink.append(influencer_json['userInfo']['user']['bioLink']['link'])
signature.append(influencer_json['userInfo']['user']['signature'])

import requests
import json
import pandas as pd

# scrape hastag
url = "https://api.tikapi.io/public/hashtag?count=30&id=9261"

payload={}
headers = {
  'X-API-KEY': 'xxxx'
}

response = requests.request("GET", url, headers=headers, data=payload)
hashtag_response = response.text
hashtag_json = json.loads(hashtag_response)

# write data to hashtag json file
results_json = open("data.json", "w")
L = [response.text]
results_json.writelines(L)
results_json.close()

# list
influencer = []
followerCount = []
bioLink = []
signature = []



influencer.append(hashtag_json["itemList"][0]['author']['uniqueId'])


# scrape influencer username
url = "https://api.tikapi.io/public/check?username={}".format(influencer[0])

payload={}
headers = {
  'X-API-KEY': 'xxxx'
}

influencerresponse = requests.request("GET", url, headers=headers, data=payload)
infl_response = influencerresponse.text
influencer_json = json.loads(infl_response)

# write data to influencer json file
results_json = open("infl_data.json", "w")
I = [influencerresponse.text]
results_json.writelines(I)
results_json.close()

followerCount.append(influencer_json['userInfo']['stats']['followerCount'])
bioLink.append(influencer_json['userInfo']['user']['bioLink']['link'])
signature.append(influencer_json['userInfo']['user']['signature'])

#  create csv file of results 
influencer_df = pd.DataFrame({  
    'Influencer' : influencer,
    'Follower Count' : followerCount,
    'Link' : bioLink,
    'Signature' : signature,  
    })

influencer_df.to_csv('/Users/john/Repos/TikTok/influencer.csv', index=False)