How to Build an In-Depth Wayfair Price Tracking Tool with Python

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Hey there! Looking to score some deals on Wayfair? As you probably know, Wayfair is one of the top spots to shop for furniture, home decor, kitchenware, and more online. The catch is that Wayfair‘s prices fluctuate constantly. That gorgeous mid-century dresser you‘ve been eyeing might be 10% off today, but tomorrow it‘s back to full price.

Luckily, with a custom price tracking tool built in Python, you can stay on top of Wayfair‘s ever-changing prices. In this step-by-step guide, I‘ll walk you through how to build your own Wayfair price tracker using Python scripts for web scraping and automation.

With this tool in your arsenal, you‘ll be able to:

  • Track price history on any Wayfair product
  • Get alerts when prices drop on items you‘re watching
  • See visually how prices change over time with graphs
  • Monitor thousands of products across Wayfair‘s catalog

Let‘s dive in!

Step 1: Install the Required Python Packages

First things first – we need to install the Python packages that will power our Wayfair price tracker.

Run this code in your terminal to install the necessary dependencies:

pip install beautifulsoup4 requests pandas matplotlib smtplib

Here‘s a quick overview of what each package does:

  • Beautiful Soup – Parses HTML from websites to extract data
  • Requests – Sends HTTP requests to download webpages
  • Pandas – Provides data structures to store price history
  • Matplotlib – Plots graphs of price data over time
  • smtplib – Sends email notifications when prices change

These powerful libraries will provide the tools we need to build a fully-featured web scraper and automation workflow.

With the packages installed, let‘s start writing our code!

Step 2: Write a Function to Scrape Wayfair Product Pages

The core piece we need is a script to scrape pricing data from Wayfair product pages. Let‘s write a function called get_wayfair_price() to handle that:

import requests
from bs4 import BeautifulSoup

def get_wayfair_price(url):

  headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36" 
  }

  page = requests.get(url, headers=headers)

  soup = BeautifulSoup(page.content, "html.parser")

  title = soup.find("h1", {"data-testid": "productTitle"}).get_text()

  price = soup.find("span", {"data-testid": "primaryPrice"}).get_text()

  return {
    "title": title,
    "price": price
  }

Here‘s what it‘s doing step-by-step:

  1. Make a request to the Wayfair product URL passed to the function

  2. Pass a User-Agent header to mimic a real browser visit

  3. Use Beautiful Soup to parse the HTML of the page

  4. Extract the product title from the <h1> tag

  5. Extract the price from the appropriate <span> tag

  6. Return a dictionary with the title and price

To summarize, this function takes in a Wayfair product page URL and returns a dictionary with the product title and price scraped from that page. Perfect for our needs!

Now let‘s test it out…

Step 3: Test the Wayfair Scraper

Let‘s pass a real Wayfair product URL to our scraper function to see it in action:

product = get_wayfair_price("https://www.wayfair.com/furniture/pdp/wade-logan-bethea-tv-stand-for-tvs-up-to-65-wlgn3669.html")

print(product["title"]) 
print(product["price"])

If everything worked correctly, this should print out:

Bethea TV Stand for TVs up to 65"
$299.99

Excellent – our function successfully scraped the product title and pricing info from the page!

Now we can begin tracking changes over time.

Step 4: Use Pandas to Track Price History

To monitor prices over time, we need a way to store the historical pricing data. For that, we‘ll use Pandas DataFrames.

First let‘s import Pandas:

import pandas as pd

Then we can create an empty DataFrame with columns for the product title, date, and price:

history = pd.DataFrame(columns = ["title", "date", "price"])

This will let us log each daily price check as a new row.

Next we need a function to append new rows to this DataFrame:

from datetime import datetime

def log_price(title, price):

  today = datetime.now().strftime("%Y-%m-%d")

  new_row = {
    "title": title, 
    "date": today,
    "price": price
  }  

  global history

  history = history.append(new_row, ignore_index=True)

This takes the title and price, creates a row for today‘s date, and adds it to the DataFrame.

Let‘s test it out on a sample product:

product = get_wayfair_price("https://www.wayfair.com/furniture/pdp/wade-logan-bethea-tv-stand-for-tvs-up-to-65-wlgn3669.html")

log_price(product["title"], product["price"]) 

print(history)

It should have logged the first data point to our DataFrame!

Now we‘re saving each price check, which will allow us to analyze pricing trends over time.

Step 5: Schedule the Script to Run Daily

So far we‘ve built the core functions for retrieving Wayfair pricing data and saving the history. Now let‘s turn this into an automated script that can run daily to collect updates.

First, we‘ll define a list of products to track:

# List of Wayfair product URLs to track pricing for 
products = [
  "https://www.wayfair.com/furniture/pdp/wade-logan-bethea-tv-stand-for-tvs-up-to-65-wlgn3669.html",
  "https://www.wayfair.com/furniture/pdp/orren-ellis-nantucket-74-tv-stand-w000324750.html"
]

Then we‘ll write a check_prices() function that loops through each one:

def check_prices():

  print(f"Checking prices at {datetime.now()}")

  for url in products:

    product = get_wayfair_price(url)  

    log_price(product["title"], product["price"])

  print("Done!")

This retrieves the latest price for each product and logs it to our DataFrame.

Finally, we‘ll call the function:

if __name__ == "__main__":

  check_prices()

When we run this script, it will populate the DataFrame with the current prices for tracking.

Now, to have it run automatically on a schedule, we can add it to Cron or a Windows scheduled task. I‘d recommend running it once daily to catch any overnight price changes.

And that‘s it – your Wayfair price tracker will now collect daily snapshots to analyze over time! 📈

Next let‘s add some functionality to alert you of any price drops…

Step 6: Get Email Alerts for Price Drops

The whole point of tracking Wayfair pricing is to snag a good deal when it drops. Let‘s make our program send an email alert whenever it detects a price decrease.

First, configure your email credentials:

# Email details to send alerts from 
from_email = "[email protected]"
to_email = "[email protected]"
password = "my_email_password"

Replace with your actual email and password.

Next, we‘ll import smtplib and write a function for sending emails:

import smtplib

def send_email(subject, body):

  message = f"Subject: {subject}\n\n{body}"

  with smtplib.SMTP("smtp.gmail.com", 587) as server:
    server.ehlo()
    server.starttls()
    server.ehlo()
    server.login(from_email, password)
    server.sendmail(from_email, to_email, message)

This handles connecting to the SMTP server and sending the message.

Now we can check for a price drop and send an alert:

import pandas as pd

yesterday = history.iloc[-2]
today = history.iloc[-1]

if today["price"] < yesterday["price"]:

  title = today["title"]
  old_price = yesterday["price"]
  new_price = today["price"]

  diff = old_price - new_price

  body = f"{title} dropped from {old_price} to {new_price}, saving you ${diff}!"

  send_email("Price Drop Alert", body)

This logic checks the current price versus previous day, calculates the price difference, and sends an email if the price went down.

Now you‘ll get notified immediately when prices fall on products you‘re tracking – no more missing out on deals!

Step 7: Visualize Price History with Matplotlib

While the email alerts are great, it‘s also helpful to visualize the pricing trends over time. For that, we can use Matplotlib to generate graphs.

First, we‘ll filter down to just one product‘s history:

import matplotlib.pyplot as plt

one_product = history[history["title"] == "Bethea TV Stand for TVs up to 65"]

Then we can plot price on the y-axis and date on the x-axis:

plt.plot(one_product["date"], one_product["price"])

plt.xlabel("Date")
plt.ylabel("Price")
plt.title("Bethea TV Stand Price History")

plt.show()

This outputs an interactive line graph tracking that product‘s price over time:

Bethea TV Stand Price History Graph

With graphs like this, you can easily spot pricing patterns and trends for products you‘re watching.

Step 8: Scale Up to Track Thousands of Products

Right now our price tracker works well for a handful of products. But what about scaling up to track pricing across Wayfair‘s entire catalog?

Here are a few approaches:

Use proxies to scrape at scale

If you start sending thousands of requests per day directly from your IP, Wayfair will likely block you. The solution is using residential proxies – IP addresses from real homes across the world.

Popular proxy services like BrightData provide thousands of fresh IPs that you can rotate through to mimic real users. Just pass the proxy IPs into your Python requests and Wayfair will be none the wiser!

Automate search to build a product database

Manually finding product URLs to track doesn‘t scale. You can automate search queries to build a database of Wayfair products to monitor.

For example, scrape and extract results from their product catalogs and category pages. This lets you grow to a huge scale.

Run on a server for performance

Eventually your home computer may not be able to keep up with running large numbers of daily checks. Consider running your scraper on a cloud server like AWS EC2 for more computing power and reliability.

Expand to other sites

In addition to Wayfair, there are deals hiding across furniture sites like Amazon, Target, IKEA and more. With a few tweaks, you can adapt your scraper to work across multiple sources.

With these scaling approaches, you can realistically monitor tens of thousands of products across the web and be the first to know when prices change!

Recap: How Your Wayfair Price Tracker Works

Let‘s quickly recap how the key pieces of our Wayfair price tracking tool work together:

  • The web scraping function extracts pricing data from Wayfair product pages

  • We save price history in a Pandas DataFrame to track changes

  • The script runs on a schedule to check for daily price updates

  • When prices drop, it sends email alerts so you can act fast

  • We can view price trends in visual graphs with Matplotlib

  • To scale up, we‘ll use proxies and automate searching for products

So in summary, you‘ve built a flexible Wayfair price tracker in Python that:

  • Monitors as many products as you want

  • Alerts you to deals in real time

  • Provides insights into pricing patterns over time

  • Can scale up to catalog-wide tracking across sites

With your new tool, you‘ll effortlessly stay on top of Wayfair‘s ever-changing prices and score the best deals!

Frequently Asked Questions

Here are answers to some common questions around building a custom Wayfair price tracker:

What are some alternative ways to track Wayfair prices?

A few other options besides a custom Python scraper include:

  • Browser extensions like Honey that alert you to price drops in your cart
  • Third party services like camelcamelcamel to track Amazon listings
  • Wayfair‘s price drop alerts – but only work for individual products

The main advantage of building your own scraper is complete flexibility and control over what data you collect. For example, getting historical price data.

Is web scraping Wayfair against their terms of service?

Generally speaking, scraping public sites like Wayfair is legally allowed with some common sense limitations:

  • Respect robots.txt rules and any restricted portions of the site
  • Scrape at a reasonable pace to avoid overloading servers
  • Don‘t share scraped pricing data with third parties
  • Use for personal or non-commercial purposes

It‘s smart to review Wayfair‘s terms closely. But you‘ll find many examples online of people building personal scrapers to find deals.

What are the risks of getting blocked while scraping Wayfair?

If you send requests too quickly or at large scale, Wayfair may detect the activity and IP ban you. A few best practices:

  • Use proxies and rotate them frequently
  • Add random delays into your scraper to mimic human behavior
  • Limit requests to a reasonable number per day
  • Check for CAPTCHAs and handle them
  • Follow Wayfair‘s robots.txt rules

By taking precautions, you can avoid disruptions while building your price tracker.

How can I expand this to track competitor pricing?

While we focused on Wayfair here, you can adapt the scraper to extract pricing data from other major furniture/home goods sites like:

  • Amazon
  • Target
  • IKEA
  • Walmart
  • Overstock
  • Home Depot

The core logic remains the same – just tweak the HTML parsing to handle each site‘s unique layouts. This lets you track prices across competitors in one place!

Let me know if you have any other questions! I‘m happy to help explain any part of the process in more detail.

Final Thoughts

Building a custom web scraper with Python is an incredibly useful skill – not just for getting Wayfair deals but for many other automation needs.

I hope this guide provided a solid foundation for how to:

  • Extract and parse data from sites with Python and Beautiful Soup
  • Store and analyze information over time with Pandas
  • Build automated workflows that run on a schedule
  • Send programmatic notifications like email alerts
  • Create visualizations and graphs with Matplotlib

These concepts open up a whole world of possibilities to collect and harness data for your own purposes.

Let me know how your Wayfair price tracker works out! I‘m always happy to help troubleshoot any issues or provide guidance on expanding the tool.

Happy (discount) shopping!

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