#FoodDataScrape

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How to Scrape Restaurants and Menus Data from Uber Eats?

Uber Eats is an online food delivery platform and ordering app based in the USA. This app allows customers to order, track, and search for their desired food items. It helps in ordering food as per your choice from a wide range of restaurants. Uber Eats spreads over 6,000 cities, with 66 million users in 2020. By 2020, there were nearly 6,00,000 Uber Eats restaurants.

However, information is available on Uber Eats. If your business is also in food delivery and wants to grow further, extracting data from Uber Eats is extremely important. In such a situation, Uber Eats Data scraping services comes into play.

By extracting restaurant listing data and food details from Uber Eats, you can easily avail restaurant data, menu data, delivery charges, discounts, competitive pricing data, menu categories, descriptions, reviews, ratings, etc. You can also read the blog about the importance of web scraping Uber Eats food delivery data

Lists of the significant data fields scraped from Uber Eats are:

Restaurants names

Restaurants addresses

Number of restaurants

Restaurants reviews

Multi-cuisines

Customers reviews

Payment methods

Restaurants menus

Types of products

Food price

Food description

Let’s first understand how to use Uber Eats restaurants and menu data.

Listed below are some of the ways that you can use scraped Uber Eats data to enhance your business strategies:

Restaurant data: Using the restaurant data, you can track the availability of the open restaurants in the locality and analyze their brand presence using the name, type, images, etc. You can also scrape website for restaurant menus from Uber Eats.

Discounts/Price Data: Beat the competitor in pricing with attractive discounts and offers. Deal with the price strategy to ensure that your offering is competitive.

Ratings & Reviews: Analyze the quality gaps in every location and adopt your brand strategy associated with ratings and reviews.

Opening Times: Discover which chains and services offer early breakfast or night-light deliveries by knowing the areas where competition is high.

Scraping of Restaurants and Menus Data from Uber Eats

Get detail insights into how to scrape restaurants and menus data from Uber Eats. Here we will find all restaurants on Uber Eats in Burlington. We are using the Python BeautifulSoup4 library to scrape food delivery data from Uber Eats. Because this library is versatile, super lightweight, and performs quickly with limited use of animation and Javascript.

Install using the pip library and then run.

pip install beautifulsoup4

Then, import it into your program using the:

from bs4 import BeautifulSoup

pip install beautifulsoup4

Import the following at the top of your program:

Now, we have all the libraries. So, for scraping restaurants, we will refer;

Retrieve the webpage contents using the following code lines.

The above lines instruct the program where to look, request the specific webpage while mimicking a user using Mozilla 5.0, open such a page, and then finally parse the page using BeautifulSoup4. Now, we are all set to extract our desired data.

Here, we are interested in scraping Uber Eats restaurant data in Burlington that are available on Uber Eats. Start with the data that you want to scrape from Uber Eats. For this, right-click on the name of any restaurant and then hit Inspect. The source code will pop up, enabling you to see the tags of each element.

In this case, after right-clicking on Taco Bell (777 Guelph Line) and hitting Inspect, the line we get is:

< h3 class="h3 c4 c5 ai">Taco Bell (777 Guelph Line)< /h3 >

It indicates that Uber Eats uses the < h3 > tag to analyze all the names of the restaurants on the page. So, we will find every < h3 > tag on the page to avail the restaurant names. We will perform this using the following snippet code:

This simple Python loop iterates via webpage content that the BeautifulSoup library has parsed. Using the ‘findAll’ method, we can list each element in our ‘soup’ object containing < h3 > tag. We will print the object x’s text field within the ‘for’ loop. It will give the following

output:

Finally, we have a complete list of the Burlington restaurants and menu data on Uber Eats.

Finally, we have a complete list of the Burlington restaurants and menu data on Uber Eats. By scraping restaurant and menu data from Uber Eats, you can easily collect relevant information for your business needs. For more information, contact Food Data Scrape now! You can also reach us for all your food data scraping service and mobile app data scraping service requirements. Know more : https://www.fooddatascrape.com/how-to-scrape-restaurants-and-menus-data-from-uber-eats.php

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Food Aggregator Scraping – Extract Food Aggregator Data

Food Aggregator Scraping of Food Data Scrape assists you in extracting food data from various food aggregator sites like Swiggy, DoorDash, Zomato, Postmates, Eat Street, Delivery.com, etc.

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Food Price Dashboard | Food Data Collections

Discover the essence of culinary trends with our Food Price Dashboard. Unleash rich insights from diverse food data collections for informed analysis.

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Are you in the food industry looking to get more business? Foodspark helps you scrape food delivery data to help you find more business in the food industry.

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Scrape Food & Grocery Delivery API Data

Scrape APIs for all the available food and grocery delivery apps at Food Data Scrape! Easily scrape food & grocery delivery api data using our Food & Grocery Scraper. Know more : https://www.fooddatascrape.com/scrape-food-grocery-delivery-api-data.php

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Scrape Listing Data Of Michelin-Star Restaurants In Singapore

In this blog, we will practice web data scraping by scraping names, addresses, cuisine types, and star ratings of Michelin-Star restaurants in Singapore to know cuisine-type distribution and geolocations.

Know more : https://www.fooddatascrape.com/scrape-listing-data-of-michelin-star-restaurants-in-singapore.php

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How To Scrape TikTok Indonesia Food Recipe Data For Using Data Extraction, Exploration And Data Visualization?

During the Covid-19 pandemic, we have seen changes in people’s story updates and Instagram posts. From posts like hangouts, parties, and travel, it’s shifting to the home activities about gardening, cooking, and Netflix binge-watching!

We have seen similar dishes which people try and post on social media. It began with dalgona coffee in March-April 2020 with Korean garlic cheese bun. We have seen people sharing different recipes on TikTok, having demo recipe videos.

If you want to explore TikTok posts using some hashtags, which people use for exploring recipes at home. Our objective is very simple :

Extract a few top TikTok posts using the hashtags

Scrape captions, views, and likes to check for all interesting trends —playing with the data.

Collecting TikTok Data

Here, we have used an API from Food Data Scrape. The complete code of TikTok scraping code can be found here.

1. Collect Data from Food Data Scrape Endpoints

Here we are using Python HTTP request comments, calling Food Data Scrape endpoints using a hashtag query needed. We have pre-defined a count of posts to get captured like 1000 posts(from maximum 2000 request or posts)

2. Parsing response data

From the given script, we would get responses to JSON data.

Then, we are parsing data in data frame format for columns that we need: user_name, video URL, caption, comments, plays, like counts, and shares.

3. Scrape hashtags and mentions from captions

Here, the code is given to extract data from food recipe with mentions and hashtags from the string.

And that’s it! You have some datasets to play with!

Data Backbiting Time!

Timeseries trends of posts

Plays, shares, and likes distribution across different accounts

Content of common recipes

Beginning from a few time-series analysis, a trend is here of posts over time. Just look at the timeseries charts given below to get a few insights:

Video posts are in uptrends since March 2020, topping in May 2020 (Indonesia Ramadhan season). In the last 2 months, total videos getting posted are stable of ~10 posts per day.

The length of posted videos also has an uptrend. This was ~40 seconds during April 2020 and reached ~60 seconds during August 2020.

Afternoon time: 3 to 7 pm looks to be peak hours when people post cooking tutorials. Looking for afternoon snacks or dinner, maybe?

How about posting trends across different accounts? Stimulatingly, for top 15 users having maximum posts, we observe a different spreading of shares and likes. Accounts including ‘fahmimiasmr’, ‘2beha10ribu’, and ‘venithyacalistaa’ have higher likes distribution, getting more than 1mio likes. Instead, ‘cookingwithhel’ is a winner for circulation of shares. One of the posts has even reached 70k shares.

The largest challenge here is to scrape dish names from given posts’ captions because in the TikTok posts you can just type anything without any organized fields. Similarly, the videos could be edited to exhibit the dish names rather than using captions. Here, we used many data cleansing procedures like removing special characters and numbers, filtering word noise (common words and stop words on posts), and scraping dish names from well-known trigrams and bigrams in a dataset.

A few word clouds of food are in bigrams, trigrams, and unigram. You might need to translate that as it’s within Bahasa Indonesia, however the components are mostly associated to snacks and desserts— oreo, martabak, cake, chocolate, milo, cheese stick, pie, pudding, etc. It’s easily understandable that the peak hours of the posts are during the afternoon as all these are ideal afternoon snacks!

And the most popular dishes include:

There are different recipes about the dishes here and some posts referring the similar dishes. Summarizing rapidly, here are all viral food recipes of TikTok Indonesia:

Desserts : dessert box, brownies, cake, milk pie, smoothies bowl

Snacks : potato hotdog, rolled egg, fried tofu, coffee bread, mochi

Savory dishes : Korean fried rice, meatballs, chicken katsu, grilled chicken,

Many of them look to be snacks and desserts opposed to other side dishes to get consumed with rice.

Some additional viz for making a more extravagant wordcloud — We are using pylab and PIL to get the image color as a background of a word cloud.

Conclusion

This concludes our discovery for important food recipes from TikTok Indonesia. Though there are boundless possibilities to extract data online, we still have to be aware of the proper stands about it. Just remember that you extract data from food recipe from only publicly accessible data and not in the destructive manner of the server accounts.

For more information about TikTok Indonesia data scraping, contact Food Data Scrape!

Know more : https://www.fooddatascrape.com/how-to-scrape-tiktok-indonesia-food-recipe-data-for-using-data-extraction-exploration-and-data-visualization.php

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Uber Eats Restaurant Data Extraction - Scrape Uber Eats Restaurant Data Use Uber Eats Restaurant data extraction services in the USA, Germany, India, UAE, Spain, Singapore, Canada, Philippines, and China to Scrape restaurant data, including locations, mentions, menus, reviews, etc., with no problem.

Know more : https://www.fooddatascrape.com/uber-eats-restaurant-data-scraping.php

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How Can Food Delivery Intelligence and Food Data Scraping Help If You are Working in the Food & Beverage Industry?

The technological development based on Artificial Intelligence and Big Data has made the global food industry dive deeper into applications, allowing them to leverage consumer and market information to develop innovative products. The food industry has grown significantly toward using data-driven food and beverage innovation methods in this new era. However, traditional methods like surveys and focusing on groups are old-age methods to collect information about consumer interests and trends. All these methods had several limitations that could have been more effective in using the data.

Food Data Scrape takes food delivery data intelligence to the next level by scraping data from more than five million restaurants, four million traffic and recipes, and 73 billion social media interactions. When paired with AI, this data gets intentionally built for the food and beverage industry to provide users with powerful tools today.

Why Is Traditional Method Losing Its Charm?

While surveys and focus groups are essential, their importance is limited to a certain extent. You can't blindly rely on it. The major drawback of surveys and focus group is that it is wholly based on self-reported data. It means that people won't be truthful about their eating preferences, as they never want to be judgmental or judged while telling the surveyors.

On the contrary, food delivery data scraping services has the potential to provide unlimited advantages over surveys and focus groups. These data-driven methods can provide a larger, more representative sample for analysis. The only reason for accurate data is that the data is available from multiple sources, including social media, point-of-sale, and even BNA data. All these sources can provide a better and more accurate picture of consumer preferences and trends.

The Data-Driven Method is Far More Than Just Data

The added advantage of data-driven methods is that they can quickly unwrap the impossible insights through surveys and focus groups. Consumer tastes, demands, and priorities are constantly evolving and spoiled with celebrity comments and culture on health trends and global politics; it is impossible to extract exact data through surveys and focus groups. They are not only slow but outdated. Food Data Scrape offers a real-time tool for food delivery data analysis.

Data helps predict future trends. Motoring the patterns over time makes it easy to know which flavors and products will become popular. It will allow the food and beverage industry to stay ahead of the competition and launch products 3ahead of their competitors.

Give Consumers a Touch of Personalization

With technological advancement, the food and beverage industries also include product personalization. However, personalization is not possible with traditional methods of surveys and focus groups. The data creates personalized products to meet individual tastes and dietary restrictions. Analyzing the trending data makes it easy to know which products attract more – like gluten-free and suitable for gut health. It is an essential factor for those who want to stay health-conscious. The consumer insights incorporation will help service organizations better than before.

Thus, we concluded that data-driven market insights could offer several advantages over traditional methods. Investing in this method is a smart option for food and beverage industries looking to innovate and stay ahead of the competition. It not only helps in identifying trends and predicting future changes but can also personalize products as per specific market segments.

For more information, contact Food Data Scrape now! You can also reach us for all your Grocery Data Intelligence and Restaurant Data Intelligence requirements.

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