Add Web_To_Excel Python Code

Author: OsSyLab

Web_to_Excel_Data_Automation/Readme.md

@@ -1,65 +1,67 @@
-# Web to Excel Data Automation
-
-This Python project demonstrates how to extract a numeric value from a webpage using `requests` and `BeautifulSoup`, process it, and then write the result to a specific cell in an Excel file using `openpyxl`.
-
-It is ideal for automating tasks like tracking online financial or statistical data and recording it directly into Excel spreadsheets.
-
----
-
-## 📌 Features
-
-- Fetches numerical data from a webpage.
-- Parses HTML tables and extracts values.
-- Converts annual values to monthly averages (customizable).
-- Writes the result into a specified Excel cell.
-
----
-
-## 🚀 Requirements
-
-Install required Python packages:
-
-```bash
-pip install requests beautifulsoup4 openpyxl
-📂 File Structure
-bash
-
-project-folder/
-├── web_to_excel.py      # Main script file
-└── README.md            # Documentation
-🧩 How to Use
-Update the script:
-
-In the web_to_excel.py file, locate this line:
-
-python
-
-url = "ENTER_THE_WEBPAGE_URL_TO_SCRAPE"
-Replace it with the actual URL of the website you want to scrape data from.
-
-Customize HTML parsing logic
-If the structure of the table is different, update the parsing logic accordingly.
-
-Set your Excel file path and sheet name:
-
-python
-
-file_path = r"PATH_TO_YOUR_EXCEL_FILE.xlsx"
-sheet_name = "YourSheetName"
-Run the script:
-
-bash
-
-python web_to_excel.py
-⚠️ Notes
-Make sure the Excel file is closed before running the script.
-
-If the webpage structure changes, the script may need to be updated.
-
-This example assumes the target data is inside the first HTML table.
-
-📄 License
-MIT License
-You are free to use, modify, and distribute this code with attribution.
-
-© 2025 Data Solutions Lab. by Osman Uluhan – All rights reserved.
+# Web to Excel Data Automation
+
+This Python project demonstrates how to extract a numeric value from a webpage using `requests` and `BeautifulSoup`, process it, and then write the result to a specific cell in an Excel file using `openpyxl`.
+
+It is ideal for automating tasks like tracking online financial or statistical data and recording it directly into Excel spreadsheets.
+
+---
+
+## 📌 Features
+
+- Fetches numerical data from a webpage.
+- Parses HTML tables and extracts values.
+- Converts annual values to monthly averages (customizable).
+- Writes the result into a specified Excel cell.
+
+---
+
+## 🚀 Requirements
+
+Install required Python packages:
+
+```bash
+pip install requests beautifulsoup4 openpyxl
+📂 File Structure
+bash
+Kodu kopyala
+project-folder/
+├── web_to_excel.py      # Main script file
+└── README.md            # Documentation
+🧩 How to Use
+Update the script:
+
+In the web_to_excel.py file, go to the following line:
+
+python
+Kodu kopyala
+url = "ENTER_THE_WEBPAGE_URL_TO_SCRAPE"
+Replace it with the actual URL of the website you want to scrape data from.
+
+Customize HTML parsing logic
+If the structure of the table is different, you may need to update how the table is parsed using BeautifulSoup.
+
+Set the correct Excel path and sheet name:
+
+Update the following section with your file path and sheet name:
+
+python
+Kodu kopyala
+file_path = r"PATH_TO_YOUR_EXCEL_FILE.xlsx"
+sheet_name = "YourSheetName"
+Run the script:
+
+bash
+Kodu kopyala
+python web_to_excel.py
+⚠️ Notes
+Ensure the Excel file is closed before running the script.
+
+This script assumes that the target data is in the first HTML table of the page.
+
+The numeric format uses commas (,) for decimals. If your data uses dots (.), you may adjust the conversion logic.
+
+📄 License
+MIT License
+You are free to use, modify, and distribute this code with attribution.
+
+© 2025 Data Solutions Lab. by Osman Uluhan – All rights reserved.

Web_to_Excel_Data_Automation/Web_To_Excel.py

@@ -0,0 +1,47 @@
+import requests
+from bs4 import BeautifulSoup
+from openpyxl import load_workbook
+
+# 1. Fetch data from a webpage and convert the annual value to a monthly average
+def fetch_annual_value():
+    url = "ENTER_THE_WEBPAGE_URL_TO_SCRAPE"  # Replace with the actual data source URL
+    headers = {
+        "User-Agent": "Mozilla/5.0"
+    }
+    resp = requests.get(url, headers=headers)
+    resp.raise_for_status()
+    soup = BeautifulSoup(resp.text, "html.parser")
+
+    # Get the first table on the page (assuming it contains the target data)
+    table = soup.find("table")
+    if table is None:
+        raise Exception("Table not found on the page.")
+
+    # Loop through rows to find the first valid annual data
+    for tr in table.find_all("tr"):
+        tds = tr.find_all("td")
+        if len(tds) >= 2:
+            date_text = tds[0].text.strip()
+            annual_value_text = tds[1].text.strip()
+            try:
+                # Convert comma to dot, cast to float, divide by 12, round to 2 decimals
+                value = float(annual_value_text.replace(",", "."))
+                monthly_average = round(value / 12, 2)
+                return monthly_average
+            except ValueError:
+                raise Exception(f"Could not convert '{annual_value_text}' to float.")
+
+    raise Exception("Annual value not found in the table.")
+
+# 2. Write the value to a specific cell in an Excel file
+def write_to_excel(value):
+    file_path = r"C:\Users\Osman ULUHAN\Desktop\Borsa\Algoritmik Trading\250930 Spot & Viop Canlı Algo Takip Tablosu.xlsx"
+    sheet_name = "Analiz"
+
+    wb = load_workbook(file_path)
+    if sheet_name not in wb.sheetnames:
+        raise Exception(f"Sheet '{sheet_name}' not found. Available sheets: {wb.sheetnames}")
+
+    ws = wb[sheet_name]
+    ws["C25"] = value
+    wb.save(file_path)