Upload visa_D_search.py

visa_D_search.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[4]:
+
+
+import requests
+import pandas as pd
+from io import BytesIO
+from bs4 import BeautifulSoup
+
+# URL of the website to scrape
+url = "https://www.ireland.ie/en/india/newdelhi/services/visas/processing-times-and-decisions/"
+
+# Headers to mimic a browser request
+headers = {
+    "User-Agent": (
+        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+        "(KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36"
+    )
+}
+
+# Send an HTTP GET request to the website
+response = requests.get(url, headers=headers)
+
+# Check if the request was successful
+if response.status_code == 200:
+    soup = BeautifulSoup(response.content, 'html.parser')
+    
+    # Find all anchor tags
+    links = soup.find_all('a')
+    
+    # Search for the link that contains the specific text
+    file_url = None
+    for link in links:
+        link_text = link.get_text(strip=True)
+        if "Visa decisions made from 1 January 2024 to" in link_text:
+            file_url = link.get('href')
+            break
+    
+    if file_url:
+        # Make the link absolute if it's relative
+        if not file_url.startswith('http'):
+            file_url = requests.compat.urljoin(url, file_url)
+        
+        ###print(f"Found link: {file_url}")
+        
+        # Download the file into memory
+        file_response = requests.get(file_url, headers=headers)
+        
+        if file_response.status_code == 200:
+            ods_file = BytesIO(file_response.content)
+            
+            # Read the .ods file into a DataFrame
+            try:
+                df = pd.read_excel(ods_file, engine='odf')
+                
+                # Step 1: Drop unnecessary columns ("Unnamed: 0" and "Unnamed: 1")
+                df = df.drop(columns=["Unnamed: 0", "Unnamed: 1"])
+                
+                # Step 2: Find the index where data starts with "Application Number"
+                header_row_index = df[df['Unnamed: 2'] == 'Application Number'].index[0]
+                
+                # Step 3: Set new headers and skip the rows before actual data
+                df.columns = df.iloc[header_row_index]
+                df = df[header_row_index + 1:].reset_index(drop=True)
+                
+                # Step 4: Rename the columns for clarity
+                df.columns = ['Application Number', 'Decision']
+                
+                # Step 5: Drop any rows with all NaN values (optional cleanup)
+                df = df.dropna(how='all')
+                
+                # Remove the download and display part
+                
+            except Exception as e:
+                print("Error reading the .ods file:", e)
+        else:
+            print("Failed to download the file. Status code:", file_response.status_code)
+    else:
+        print("The specified link was not found.")
+else:
+    print(f"Failed to retrieve the webpage. Status code: {response.status_code}")
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+