diff --git a/petit_fute_scraping.py b/petit_fute_scraping.py
index 2b771a6..9907280 100644
--- a/petit_fute_scraping.py
+++ b/petit_fute_scraping.py
@@ -7,7 +7,8 @@ from icecream import ic
 
 
 def get_names(soup):
-    return [_.text.strip() for _ in soup.find_all('h3')]
+    # ic(soup)
+    return [_.text.strip() for _ in soup.find_all('h2') if 'manger à' not in _.text][:-1]
 
 
 def get_number_of_pages(soup):
@@ -42,13 +43,16 @@ def get_addresses(soup):
     urls = get_url(soup)
     addresses = []
     for url in urls:
-        ic()
+        # ic()
         page = requests.get(url)
         soup2 = BeautifulSoup(page.text, features='html.parser')
 
         all_span = [_.text for _ in soup2.find_all('span')]
-        address = (('' + all_span[all_span.index('Paris,')-2] + all_span[all_span.index('Paris,')-1]).
-                   replace(',', ' ').replace('  ', ' '))
+        try:
+            address = (('' + all_span[all_span.index('Paris,')-2] + all_span[all_span.index('Paris,')-1]).
+                       replace(',', ' ').replace('  ', ' '))
+        except ValueError:
+            address = ''
 
         # Deal with word before the street number
         if not address.split(' ')[0].isdigit():
@@ -68,30 +72,45 @@ def get_addresses(soup):
         # Deal with abbreviation
         address = address.replace('Av.', 'avenue').replace('bis ', '')
 
-        ic(address)
+        # ic(address)
         addresses.append(address)
     return addresses
 
 
 def scrap(soup):
+    ic()
     df = pd.DataFrame(columns=['Name', 'Date', 'Address', 'Price',])
     df['Name'] = get_names(soup)
+    # ic(len(get_names(soup)))
     df['Date'] = datetime.date.today()
-    df['Address'] = get_addresses(soup)
-    df['Price'] = get_prices(soup)
+    try:
+        df['Address'] = get_addresses(soup)
+        # ic(len(get_addresses(soup)))
+    except ValueError:
+        ic(get_names(soup))
+        ic(get_addresses(soup))
+    try:
+        df['Price'] = get_prices(soup)
+        # ic(len(get_prices(soup)))
+    except ValueError:
+        ic(get_names(soup))
+        ic(get_prices(soup))
 
     for name in df['Name']:
         newpath = '/Users/oliviermeyer/Desktop/CREST/Web Scraping/HTML/Petit Fute/' + str(datetime.date.today())
         if not os.path.exists(newpath):
             os.makedirs(newpath)
 
-        html_path = ('/Users/oliviermeyer/Desktop/CREST/Web Scraping/HTML/Le Fooding/' +
-                     str(datetime.date.today()) + '/' + name.replace('/', '-') + '.html')
-
-        with open(html_path, 'wt', encoding='utf-8') as html_file:
-            for line in soup.prettify():
-                html_file.write(line)
+        try:
+            html_path = ('/Users/oliviermeyer/Desktop/CREST/Web Scraping/HTML/Petit Fute/' +
+                         str(datetime.date.today()) + '/' + name.replace('/', '-') + '.html')
 
+            with open(html_path, 'wt', encoding='utf-8') as html_file:
+                for line in soup.prettify():
+                    html_file.write(line)
+        except AttributeError:
+            ic()
+    # print(df)
     return df
 
 
@@ -104,15 +123,22 @@ def complete_scraping():
 
     number_of_pages = get_number_of_pages(soup)
     for i in range(number_of_pages+1):
-        try:
-            new_url = url + '?page=' + str(i)
-            new_page = requests.get(new_url)
-            new_soup = BeautifulSoup(new_page.text, features='html.parser')
-            temporary_df = scrap(new_soup)
-            df = pd.concat([df, temporary_df], ignore_index=True)
-        except ValueError:
-            pass
+        # try:
+        new_url = url + '?page=' + str(i)
+        new_page = requests.get(new_url)
+        new_soup = BeautifulSoup(new_page.text, features='html.parser')
+        temporary_df = scrap(new_soup)
+        df = pd.concat([df, temporary_df], ignore_index=True)
+        # print(df.to_string())
+        # except ValueError:
+        #     pass
 
     df = df.dropna(subset='Address')
-    return df
 
+    prices = df['Price']
+    for i in range(len(prices)):
+        if not pd.isna(prices[i]):
+            if prices[i].replace('€', '') != '':
+                prices[i] = prices[i].split(' ')[0]
+
+    return df