How Can Web Scraping Cheapest Groceries Supermarket Maximize Your Savings?

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How To Extract Food Delivery Apps UAE Data Using
Expert Web Scraping Services?
Food delivery apps are a ground-breaking way to
distribute food. This blog discusses extracting
food delivery apps' UAE data using expert web
scraping services. What is a Food Delivery App?
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Food delivery apps are an advanced way of food
distribution. Many food delivery apps are
available in the market, a standard platform
between customers and restaurants. A few
restaurant owners make food ordering apps so
customers can order food rapidly and offer fresh
food. Some top food delivery apps in UAE comprise
Careem, Deliveroo, Eat Clean, Instashop, EatEasy,
Noon Food, Make my meal, Smiles UAE,
MunchOn, Zomato, Talabat, and Uber Eats.
Some Info About Food Delivery Segment Growth
Revenue of the food delivery segment has reached
US9,207m in 2020! This income is expected to get
an annual growth of 9.5 with CAGR 2020-2024 and
reach US13,233m of market size by 2024! The most
significant segment in the marketplace is
Restaurant-to-Consumer Delivery, with a volume of
US4,934m during 2020.
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Food Data Scrape provides top food delivery app
scraping services in UAE to extract food delivery
apps like Careem, Deliveroo, Eat Clean,
Instashop, EatEasy, Noon Food, Make my meal,
Smiles UAE, MunchOn, Zomato, Talabat, and Uber
Eats. We offer food delivery app scraping service
to all consumers with on-time delivery and
precision. Our food data extraction services
assist get information like product data,
pricing, quotations, features, etc. At Food Data
Scrape, we help you scrape precise data and
provide necessary business details.
List of Data Fields
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Our initial concept involves creating a
user-friendly platform where users can add
individual grocery items to their shopping lists.
For data collection, we plan to perform
manual web scraping of grocery data from various
online sources. The scraping process will search
for each product's lowest price at supermarkets
near the user's vicinity. The extracted data will
then be organized and recorded in a Pandas
DataFrame, featuring columns such as "product,"
"price," "supermarket," and "distance. Next,
the system will filter the "price" and "distance"
columns to determine the best trade-off between
these criteria. Suppose the user enters a
complete grocery list. In that case, the scraper
will fetch the lowest prices for each product
across all supermarkets, storing the total prices
along with the respective supermarket names and
distances in another Pandas data frame. Finally,
the platform will arrange the list based on
overall cost and distance and perform another
evaluation to find the optimal compromise between
these two factors. This final output will be
presented to the platform user, allowing them to
decide on the most cost-effective and convenient
supermarket to fulfill their grocery needs. Our
project encountered several challenges and
deviations from our initial plan, which is common
in such endeavors. Our research discovered that
only a few supermarkets have internet stores or
comprehensive online offerings. Many others
primarily display their current discounts or
non-edible products, making it difficult for us
to gather the necessary grocery data. For
instance, while attempting to cooperate with
REWE, we encountered legal concerns about
"Captcha," a tool designed to differentiate
between human users and bots. It hindered our
data collection efforts on their
website. Additionally, most websites did not
provide the option to search for a specific
region's selection, which can vary from one
location to another. Using Google Maps for
location-based searches would have complicated
the project significantly and was beyond its
scope.
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Another challenge was the complexity and
messiness of specific data sources, as HTML files
varied widely in structure and organization. Thus
, we decided to step back and simplify our
approach to address these roadblocks. We created
individual scripts for each supermarket,
utilizing web scraping with Python Selenium and a
Chrome driver. It allowed us to extract
supermarket prices from their respective
websites. Our approach involved creating a
function called "choose a product," where the
user selects a product from a list, and its name
is as a variable. Then, on each supermarket's
website, we used "driver.find_element_by_id(""
).click()" to enable cookies. Subsequently, a
function named "enter_website" was developed to
input the selected product into the website's
search field. We also included steps to apply the
"price low to high" filter from the drop-down
list. We utilized XPath to locate and save the
first displayed product's name and price on the
HTML site to extract the necessary information.
We then stored this data, along with the name of
the associated store, in a CSV file. We
implemented additional code to join the scrapers
and store the information in the same CSV file to
streamline the process and avoid handling each
supermarket's script separately. Despite our
challenges, we adapted our approach. We created a
functional system that scrapes supermarket prices
for a chosen product, making grocery shopping
more cost-effective and accessible for users. To
streamline the data collection process, we
developed separate functions for each supermarket
scraper and incorporated them within a for-loop
to iterate through all of them. The code is
functional, except for some minor issues related
to identifying specific items on certain
websites. These issues are quickly addressable
with a little more time and attention.
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Now that we have gathered all the required
information, our next step is determining the
cheapest supermarket and the lowest price for all
the items on the shopping list. We have written
another script to filter the data in the newly
formed file to achieve this. The script includes
a method called "min_sum()" that groups the
supermarket items in the data frame, allowing us
to perform necessary calculations efficiently.
Additionally, we implemented the
"favorite_store()" method, where the user prompts
to specify if they have a preferred supermarket.
If they do, they must choose from the list of
scraped stores. By combining these
functionalities, we can present the final output
to the user, indicating the most cost-effective
supermarket for their shopping list and the
overall lowest price for all the items. With this
final step, our platform is now fully equipped to
assist users in finding the best deals and making
budget-conscious decisions for their grocery
shopping needs. Upon the user's response
regarding their favorite retailer, the system
provides the price total for the shopping list
and displays a menu of product names they intend
to purchase at their preferred store. Conversely,
if the user indicates they do not have a favorite
store, the code executes the "min_sum()"
method. The "min_sum()" method calculates the
price sum, store name, and product list, which
presents to the user. However, a notable
challenge we currently face is that not all
stores advertise all their items online. To
address this issue, we have implemented a
workaround. When certain items are unavailable,
we substitute them with the value "Beispiel" and
assign a standard price of 3. It allows the
system to continue functioning despite incomplete
data. Nevertheless, we recognize this is an
aspect we must address before making the web
scraper available online. Ensuring comprehensive
and accurate data representation will provide
users with the most reliable and valuable
information during their grocery shopping
experience. For further details, contact Food
Data Scrape now! You can also reach us for all
your Food Data Aggregator and Mobile Restaurant
App Scraping service requirements.
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