How to Build a Price Tracker With Python

Building a price tracker is a great way to learn Python automation. It’s a particularly convenient project for beginners as the resulting script can be used right away. 

This article explains how to build a scalable price tracker for immediate deployment to various eCommerce sites.

For your convenience, we also prepared this tutorial in a video format:

The CSV file can be read and converted to a dictionary object using Pandas. Let’s wrap this up in a simple function:

def get_urls(csv_file):
    df = pd.read_csv(csv_file)
    return df

The function will return a Pandas’ DataFrame object that contains three columns — product, URL, and alert_price (see the image above).

Scraping the prices

The initial step is to loop over the target URLs.

Note that the get_urls() returns a DataFrame object. 

To run a loop, first use the to_dict() method of Pandas. When the to_dict method is called with the parameter as records, it converts the DataFrame into a list of dictionaries. 

Run a loop over each dictionary as follows:

def process_products(df):
    for product in df.to_dict("records"):
        # product["url"] is the URL

We’ll revisit this method after writing two additional functions. The first function is to get the HTML and the second function is to extract the price from it.

To get the HTML from response for each URL, run the following function:

def get_response(url):
    response = requests.get(url)
    return response.text

Next, create a BeautifulSoup object according to the response and locate the price element using a CSS selector. Use the Price-parser library to extract the price as a float for comparison with the alert price. If you want to better understand how the Price-parser library works, head over to our GitHub repository for examples.

The following function will extract the price from the given HTML, returning it as a float:

def get_price(html):
    soup = BeautifulSoup(html, "lxml")
    el = soup.select_one(".price_color")
    price = Price.fromstring(el.text)
    return price.amount_float

Note that the CSS selector used in this example is specific to the scraping target. If you are working with any other site, this is the only place where you would have to change the code.

We’re using BeautifulSoup to locate an element containing the price via CSS selectors. The element is stored in the el variable. The text attribute of the el tag, el.text, contains the price and currency symbol. Price-parser parses this string to extract the price as a float value.

There is more than one product URL in the DataFrame object. Let’s loop over all the rows and update the DataFrame with new information.

The easiest approach is to convert each row into a dictionary. This way, you can read the URL, call the get_price() function, and update the required fields.

We’ll add two new keys — the extracted price (price) and a boolean value (alert), which filters rows for sending an email.

The process_products() function can now be extended to demonstrate the aforementioned sequence:

def process_products(df):
    updated_products = []
    for product in df.to_dict("records"):
        html = get_response(product["url"])
        product["price"] = get_price(html)
        product["alert"] = product["price"] < product["alert_price"]
        updated_products.append(product)
    return pd.DataFrame(updated_products)

This function will return a new DataFrame object containing the product URL and a name read from the CSV. Additionally, it includes the price and alert flag used to send an email on a price drop.

Saving the output

The final DataFrame containing the updated product data can be saved as CSV using a simple call to the to_csv() function.

Additionally, we’ll check the SAVE_TO_CSV flag as follows:

if SAVE_TO_CSV:
    df_updated.to_csv(PRICES_CSV, mode="a")

You’ll notice that the mode is set to "a", which stands for “append” to ensure new data is appended if the CSV file is present.

Sending email alerts

Optionally, you can send an email alert on price drop based on the alert flag. First, create a function that filters the data frame and returns email’s subject and body:

def get_mail(df):
    subject = "Price Drop Alert"
    body = df[df["alert"]].to_string()
    subject_and_message = f"Subject:{subject}\n\n{body}"
    return subject_and_message

Now, using smtplib, create another function that sends alert emails:

def send_mail(df):
    message_text = get_mail(df)
    with smtplib.SMTP("smtp.server.address", 587) as smtp:
        smtp.starttls()
        smtp.login(mail_user, mail_pass)
        smtp.sendmail(mail_user, mail_to, message_text)

This code snippet assumes that you’ll set the variables mail_user, mail_pass, and mail_to.

Putting everything together, this is the main function:

def main():
    df = get_urls(PRODUCT_URL_CSV)
    df_updated = process_products(df)
    if SAVE_TO_CSV:
        df_updated.to_csv(PRICES_CSV, index=False, mode="a")
    if SEND_MAIL:
        send_mail(df_updated)

Add the lines shown below to execute this function and run the entire code:

if __name__ == "__main__":
    main()

If you wish to run this automatically at certain intervals, use cronjob on macOS/Linux or Task Scheduler on Windows. 

Alternatively, you can also deploy this price monitoring script on any cloud service environment.

Conclusion

The Python prices tracking script showcased in this article can fetch product prices and other product data from any number of product URLs. The functionality of this price monitor can be expanded further according to your preferences. For example, instead of saving to a CSV, you can save the information to a database. 

If you’re running a price monitoring script at scale, you’re likely to require reliable Residential Proxies. You can also check our complete solutions for price monitoring: Amazon Product Data API, Target Scraper API, Walmart Product API, Lowe's Scraper API, and Etsy Scraper API.

For complete code samples, see our GitHub repository. Check our blog for more tutorials on web scraping with Python, PHP, and Ruby, or take a look at a more detailed guide on how to avoid blocks when web scraping.