Category-wise packs with monthly refresh; export as CSV, ISON, or Parquet.
Choose your region, and we’ll deliver clean, accurate store location datasets.
Launch instantly with ready-made scrapers tailored for popular platforms. Extract clean, structured data without building from scratch.
Access real-time, structured data through scalable REST APIs. Integrate seamlessly into your workflows for faster insights and automation.
Download sample datasets with product titles, price, stock, and reviews data. Explore Q4-ready insights to test, analyze, and power smarter business strategies.
Playbook to win the digital shelf. Learn how brands & retailers can track prices, monitor stock, boost visibility, and drive conversions with actionable data insights.
We deliver innovative solutions, empowering businesses to grow, adapt, and succeed globally.
Collaborating with industry leaders to provide reliable, scalable, and cutting-edge solutions.
Find clear, concise answers to all your questions about our services, solutions, and business support.
Our talented, dedicated team members bring expertise and innovation to deliver quality work.
Creating working prototypes to validate ideas and accelerate overall business innovation quickly.
Connect to explore services, request demos, or discuss opportunities for business growth.
GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.157 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.157 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
In today's data-driven landscape, extracting valuable information from websites can provide crucial insights. Creating a Python script for scraping restaurant and menu data is a powerful way to gather relevant details for analysis. This guide explores the step-by-step process of developing a Python script to scrape restaurant and menu information from three diverse locations: Indonesia, the Philippines, and Thailand.
Each country's restaurant and culinary landscape offers a rich tapestry of flavors and choices. By harnessing web scraping capabilities, we can systematically extract data from restaurant websites, gaining a comprehensive understanding of the dining options available in these distinct regions.
We'll delve into the intricacies of Python programming, utilizing libraries such as requests and BeautifulSoup to make HTTP requests and parse HTML content. The script will be designed to navigate through the web pages, locate restaurant details, and extract menu information. Through this journey, we aim to empower users to create adaptable and efficient scripts to harvest data from diverse online sources.
As we embark on this scripting adventure, envision the potential to unlock culinary insights from each location. From popular dishes to unique dining experiences, the script will serve as a bridge to connect data enthusiasts with the diverse and vibrant world of restaurants in Indonesia, the Philippines, and Thailand.
Prepare to embark on a coding journey that sharpens your Python skills and opens up a world of culinary exploration through the lens of web scraping. Let's dive into the process of crafting a dynamic Python script for culinary data extraction from these three captivating locations.
Import the required libraries, including requests for making HTTP requests and BeautifulSoup for parsing HTML content.
import requests from bs4 import BeautifulSoup
Create a function that takes a location as an argument and performs the scraping based on the website structure.
def scrape_restaurant_data(location): # Code for scraping goes here
Build URLs for each location using a base URL and the location parameter.
url = f"https://example-restaurant-website.com/{location}"
Use the requests library to fetch the HTML content of the webpage.
response = requests.get(url)
Verify that the request was successful (status code 200).
if response.status_code == 200:
Use BeautifulSoup to parse the HTML content and navigate the document's structure.
soup = BeautifulSoup(response.content, 'html.parser')
Locate and extract relevant information such as restaurant names, addresses, and menu items.
Implement error handling in case the request is unsuccessful.
Call the scraping function for each location.
Execute the script to initiate the scraping process for each specified location.
This basic flow provides a starting point for scraping restaurant and menu data. Adjust the script based on the specific structure and requirements of the website you're working with. Always respect the website's terms of service and consider implementing additional features, such as handling dynamic content or pagination, depending on the complexity of the target website.
Developing a Python script for scraping restaurant and menu data from diverse locations is a journey into the heart of culinary exploration. Actowiz Solutions, with its expertise in data solutions, stands as a critical ally in this venture. As we wrap up this guide, it's crucial to recognize the power of data-driven insights in shaping informed decisions within the culinary landscape.
Actowiz Solutions empowers businesses and enthusiasts alike to harness the potential of data, providing tailored solutions for seamless information extraction. By leveraging Actowiz's expertise, you gain access to cutting-edge tools and unlock a world of culinary intelligence. The script you've crafted becomes a gateway to a treasure trove of restaurant details, offering a competitive edge in understanding culinary preferences across Indonesia, the Philippines, and Thailand.
Take the next step with Actowiz Solutions, where innovation meets data-driven success. Explore many possibilities for enhancing your business strategies, making informed decisions, and staying ahead in an ever-evolving market. Contact Actowiz Solutions today and elevate your data journey, ensuring your culinary insights are as diverse and vibrant as the regions they represent. Seize the opportunity to transform raw data into a culinary masterpiece, and let Actowiz Solutions guide you in this dynamic realm. For more details, contact us now! You can also reach us for all your mobile app scraping, instant data scraper and web scraping service requirements.
✨ "1000+ Projects Delivered Globally"
⭐ "Rated 4.9/5 on Google & G2"
🔒 "Your data is secure with us. NDA available."
💬 "Average Response Time: Under 12 hours"
Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.
Find Insights Use AI to connect data points and uncover market changes. Meanwhile.
Move Forward Predict demand, price shifts, and future opportunities across geographies.
Industry:
Coffee / Beverage / D2C
Result
2x Faster
Smarter product targeting
“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”
Operations Manager, Beanly Coffee
✓ Competitive insights from multiple platforms
Real Estate
Real-time RERA insights for 20+ states
“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”
Data Analyst, Aditya Birla Group
✓ Boosted data acquisition speed by 3×
Organic Grocery / FMCG
Improved
competitive benchmarking
“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”
Product Manager, 24Mantra Organic
✓ Real-time SKU-level tracking
Quick Commerce
Inventory Decisions
“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”
Aarav Shah, Senior Data Analyst, Mensa Brands
✓ 28% product availability accuracy
✓ Reduced OOS by 34% in 3 weeks
3x Faster
improvement in operational efficiency
“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”
Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
Faster
Trend Detection
“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”
Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
Enhanced
stock tracking across SKUs
“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”
Growth Analyst, TheBakersDozen.in
✓ Improved rank visibility of top products
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.
✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place
Explore the top 500 trending ecommerce products in the USA for December with real-time data, pricing shifts, and marketplace insights powered by Actowiz Solutions.
A detailed case study on weekly stock-out analysis for top grocery SKUs using Actowiz Solutions’ real-time availability tracking and data intelligence.
Real-time grocery price changes across Walmart, Instacart and Target. Track top SKU drops, increases and hourly volatility with Actowiz Solutions.
A detailed research report analyzing December ecommerce deals, discounts, and price drops across USA and UAE marketplaces powered by Actowiz Solutions.
A detailed blog uncovering assortment gaps across major USA ecommerce marketplaces with real-time insights powered by Actowiz Solutions.
A complete analysis of December price drops and deal patterns across Amazon, Walmart, and Target using real-time ecommerce intelligence from Actowiz Solutions.
Discover how pack-size differences drive major price wars across 10 leading grocery platforms. A data-driven case study powered by Actowiz Solutions.
A case study exploring how the HungerStation Dataset for Restaurant and Order Data enables accurate order forecasting and improved delivery efficiency through data-driven analysis.
Enhance deep learning performance with large-scale image scraping. Build diverse, high-quality training datasets to improve AI accuracy, object detection, and model generalization.
Uncover how data-driven strategies optimize dark store locations, boosting quick commerce efficiency, reducing costs, and improving delivery speed.
A detailed December ecommerce price index report comparing category-wise pricing trends across USA & UAE markets powered by Actowiz Solutions.
Weekly research report on top-selling SKUs across USA marketplaces like Amazon, Walmart, and Target, powered by Actowiz Solutions’ real-time ecommerce intelligence.
Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.
Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.
Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations