A project to pull all the result for Samsung Galaxy S25 Ultra in the Amazon Website with their details for the title, price, date and shipping date

#step 1 #Import Libraries import requests from bs4 import BeautifulSoup import re import datetime import csv import time

#step2

Connect to the website to extract the data

Example: Search page for Samsung Galaxy S25 Ultra

URL = "https://www.amazon.com.au/s?k=Samsung+Galaxy+S25+Ultra&ref=mr_direct_us_au_au"

headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)","Accept-Language": "en-US,en;q=0.9"}

page = requests.get(URL, headers=headers) soup = BeautifulSoup(page.text, "html.parser")

for item in items: # Title title_tag = item.find("h2", class_="a-size-base-plus a-spacing-none a-color-base a-text-normal") title = title_tag.get_text(strip=True) if title_tag else "Title not found"

    shipping_tag = item.find("span", class_="a-text-bold")
    shipping = shipping_tag.get_text(strip=True) if shipping_tag else "Shipping not found"

    # Price
    price_tag = item.find("span", class_="a-price-whole")
    if not price_tag:
        price_tag = item.find("span", class_="a-offscreen")  # fallback
    price_str = price_tag.get_text(strip=True) if price_tag else "Price not found"

    

🔹 Each search result is inside "div" with class "s-result-item"

items = soup.find_all("div", {"data-asin": True, "class": "s-result-item"})

results = []

Coversion of the price into float and sorting

price = None if price_str != "Price not found": price_clean = price_str.replace("$", "").replace(",", "").replace("AUD", "").strip() try: price = float(price_clean) except ValueError: price = None

    if price:  # only keep items with valid price
        results.append([title, price, today,shipping])

    results.sort(key=lambda x: x[1])

Define a function to pull the Title, Price, Shipping date etc.

def scrape_search_results(): # Example: Search page for Samsung Galaxy S25 Ultra URL = "https://www.amazon.com.au/s?k=Samsung+Galaxy+S25+Ultra&ref=mr_direct_us_au_au"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
    "Accept-Language": "en-US,en;q=0.9",
}

page = requests.get(URL, headers=headers)
soup = BeautifulSoup(page.text, "html.parser")

today = datetime.date.today()

# 🔹 Each search result is inside "div" with class "s-result-item"
items = soup.find_all("div", {"data-asin": True, "class": "s-result-item"})

results = []

for item in items:
    # Title
    title_tag = item.find("h2", class_="a-size-base-plus a-spacing-none a-color-base a-text-normal")
    title = title_tag.get_text(strip=True) if title_tag else "Title not found"
    #Shipping_date
    shipping_tag = item.find("span", class_="a-text-bold")
    shipping = shipping_tag.get_text(strip=True) if shipping_tag else "Shipping not found"

    # Price
    price_tag = item.find("span", class_="a-price-whole")
    if not price_tag:
        price_tag = item.find("span", class_="a-offscreen")  # fallback
    price_str = price_tag.get_text(strip=True) if price_tag else "Price not found"

    price = None
    if price_str != "Price not found":
        price_clean = price_str.replace("$", "").replace(",", "").replace("AUD", "").strip()
        try:
            price = float(price_clean)
        except ValueError:
            price = None

    if price:  # only keep items with valid price
        results.append([title, price, today,shipping])

    results.sort(key=lambda x: x[1])
    # Save to CSV
with open("Amazon_Search_Results.csv", "w", newline="", encoding="UTF8") as f:
    writer = csv.writer(f)
    writer.writerow(["Title", "Price", "Date", "Shipping"])
    writer.writerows(results)



print(f"✅ Saved {len(results)} products to Amazon_Search_Results.csv")

scrape_search_results()

Save to CSV

with open("Amazon_Search_Results.csv", "w", newline="", encoding="UTF8") as f:
    writer = csv.writer(f)
    writer.writerow(["Title", "Price", "Date", "Shipping"])
    writer.writerows(results)

#Final Function

import requests from bs4 import BeautifulSoup import re import datetime import csv import time

def scrape_search_results(): # Example: Search page for Samsung Galaxy S25 Ultra URL = "https://www.amazon.com.au/s?k=Samsung+Galaxy+S25+Ultra&ref=mr_direct_us_au_au"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
    "Accept-Language": "en-US,en;q=0.9",
}

page = requests.get(URL, headers=headers)
soup = BeautifulSoup(page.text, "html.parser")

today = datetime.date.today()

# 🔹 Each search result is inside "div" with class "s-result-item"
items = soup.find_all("div", {"data-asin": True, "class": "s-result-item"})

results = []

for item in items:
    # Title
    title_tag = item.find("h2", class_="a-size-base-plus a-spacing-none a-color-base a-text-normal")
    title = title_tag.get_text(strip=True) if title_tag else "Title not found"
    
    shipping_tag = item.find("span", class_="a-text-bold")
    shipping = shipping_tag.get_text(strip=True) if shipping_tag else "Shipping not found"

    # Price
    price_tag = item.find("span", class_="a-price-whole")
    if not price_tag:
        price_tag = item.find("span", class_="a-offscreen")  # fallback
    price_str = price_tag.get_text(strip=True) if price_tag else "Price not found"

    price = None
    if price_str != "Price not found":
        price_clean = price_str.replace("$", "").replace(",", "").replace("AUD", "").strip()
        try:
            price = float(price_clean)
        except ValueError:
            price = None

    if price:  # only keep items with valid price
        results.append([title, price, today,shipping])

    results.sort(key=lambda x: x[1])


# Save to CSV
with open("Amazon_Search_Results.csv", "w", newline="", encoding="UTF8") as f:
    writer = csv.writer(f)
    writer.writerow(["Title", "Price", "Date", "Shipping"])
    writer.writerows(results)

print(f"✅ Saved {len(results)} products to Amazon_Search_Results.csv")

scrape_search_results()

Autosearch within mentioned time(5 sec)

while(True): scrape_search_results() time.sleep(5)

Extraction of the Dataframe that saved as CSV file

import pandas as pd

df = pd.read_csv(r'C:\Users\mesag\Amazon_Search_Results.csv') print(df)