Python Otomasyon Çözümleri

# E-ticaret sitesi fiyat takibi
import requests
from bs4 import BeautifulSoup
import pandas as pd
from datetime import datetime

class PriceScraper:
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0...'
        })
    
    def scrape_product(self, url):
        response = self.session.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        
        price = soup.select_one('.price').text
        title = soup.select_one('.title').text
        
        return {
            'title': title.strip(),
            'price': self.clean_price(price),
            'date': datetime.now(),
            'url': url
        }
    
    def clean_price(self, price_text):
        return float(price_text.replace('₺','')
                    .replace(',','').strip())

# Kullanım
scraper = PriceScraper()
data = scraper.scrape_product(product_url)
print(f"Ürün: {data['title']}")
print(f"Fiyat: {data['price']}₺")

# Fiyat analizi ve görselleştirme
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

def analyze_price_data(csv_file):
    # Veri yükleme
    df = pd.read_csv(csv_file)
    df['date'] = pd.to_datetime(df['date'])
    
    # Temel istatistikler
    stats = {
        'min_price': df['price'].min(),
        'max_price': df['price'].max(),
        'avg_price': df['price'].mean(),
        'price_change': df['price'].pct_change().fillna(0)
    }
    
    # Trend analizi
    df['moving_avg'] = df['price'].rolling(7).mean()
    
    # Fiyat uyarıları
    threshold = stats['avg_price'] * 0.9
    alerts = df[df['price'] <= threshold]
    
    if not alerts.empty:
        send_price_alert(alerts.iloc[-1])
    
    return stats, df

def send_price_alert(product):
    # E-posta otomasyon
    message = f"""
    🔥 Fiyat Düşüşü Uyarısı!
    
    Ürün: {product['title']}
    Güncel Fiyat: {product['price']}₺
    İndirim: %{calculate_discount()}
    """
    send_email(message)

# Analiz çalıştırma
stats, data = analyze_price_data('prices.csv')