diff --git a/mastodon-get-posts.org b/mastodon-get-posts.org
index 56456e8..615ed57 100644
--- a/mastodon-get-posts.org
+++ b/mastodon-get-posts.org
@@ -1,63 +1,87 @@
-* Import 
+* Initialize
 #+begin_src python
 import json  # to parse data
 import requests # to get data
-import pandas as pd # work with data
-from mastodon import Mastodon # to get the user id
-from datetime import date # to get the current date
-import subprocess # for getting access token from pass
-from bs4 import BeautifulSoup # to more easily read the html output
-#+end_src
+
+# get user id
+instance = "https://social.edu.nl"
+username = "mishavelthuis"
+id = json.loads(requests.get(f"{instance}/api/v1/accounts/lookup?acct={username}").text)['id']
+
+# get current date
+current_date = date.today()
+
+# Create filename for data output
+#current_dir="/".join(inspect.getfile(inspect.currentframe()).split("/")[:-1])
+download_dir=os.path.expanduser("~/Downloads")
+file_name_save=f'{download_dir}/mydata_{current_date}_{username}.csv'
+#+end_src 
 * Get/refresh data
 - I used [[https://jrashford.com/2023/02/13/how-to-scrape-mastodon-timelines-using-python-and-pandas/][this]] setup.
 - Only have to be refreshed (run) every now and then
 #+begin_src python
-# Get user id
-instance = "https://social.edu.nl"
-username = "mishavelthuis"
-id = json.loads(requests.get("https://social.edu.nl/api/v1/accounts/lookup?acct=mishavelthuis").text)['id']
+import json  # to parse data
+import requests # to get data
+import pandas as pd # work with data
+from datetime import date # to get the current date
+import subprocess # for getting access token from pass
+import os # to remove file
 
-URL = f'{instance}/api/v1/accounts/{id}/statuses'
+# To not append to existing file
+os.remove(file_name_save)
+
+url = f'{instance}/api/v1/accounts/{id}/statuses'
 params = {
     'limit': 40
 }
 
 results = []
+num_done = 0
 
 while True:
-    r = requests.get(URL, params=params)
-    toots = json.loads(r.text)
+    print(f'{num_done} statuses downloaded')
+    try:
+        r = requests.get(url, params=params)
+        toots = json.loads(r.text)
+    except:
+        print("request didn't work")
 
     if len(toots) == 0:
         break
     
-    results.extend(toots)
-    
-    max_id = toots[-1]['id']
-    params['max_id'] = max_id
-    
-df = pd.DataFrame(results)
+    try:
+        max_id = toots[-1]['id']
+        params['max_id'] = max_id
+    except Exception as error:
+        print("An error occurred with max_id:", error) 
 
-current_date = date.today()
-current_dir="/".join(inspect.getfile(inspect.currentframe()).split("/")[:-1])
-file_name_save=f'{current_dir}/mydata_{current_date}_{username}.csv'
-df.to_csv(file_name_save, index=False)
+    num_done=num_done+40
 
+    try:
+        df = pd.DataFrame(toots)
+        df.to_csv(file_name_save, mode='a', index=False)
+    except Exception as error:
+        print("An error occurred with df:", error) 
+        num_done=num_done-40
 #+end_src
-
 * Use/search data
 - You don't have to load all data for every search.
 #+begin_src python
+import pandas as pd # work with data
+from bs4 import BeautifulSoup # to more easily read the html output
+
 df=pd.read_csv(file_name_save)
 
 query="test"
 
 # Search for words
-for i in df['content']:
-    if isinstance(i,str):
-      if query in i:
-          soup = BeautifulSoup(i, 'html.parser')
+for index, i in df.iterrows():   
+    if isinstance(i['content'],str):
+      if query in i['content']:
+          soup = BeautifulSoup(i['content'], 'html.parser')
           readable_text = soup.get_text(separator=' ', strip=True)
+          print(i['url'])
+          print(i['created_at'])
           print(readable_text)
           print("----")
 #+end_src