#Zomato Data Scraper Tool

[iwebscrapingblogs]

Zomato API - Zomato Scraper - Zomato Review API

In the realm of food and restaurant discovery, Zomato stands as a significant player. For developers and data enthusiasts, the platform offers several APIs that provide access to its extensive database of restaurants, reviews, and user-generated content. In this blog, we delve into the Zomato API ecosystem, focusing on three key components: the Zomato API, Zomato Scraper, and Zomato Review API. Understanding these tools can unlock a wealth of opportunities for creating innovative applications and gaining insights into dining trends.

The Zomato API

Overview

The Zomato API is a powerful tool that allows developers to access Zomato’s vast collection of restaurant data. Whether you’re building a restaurant recommendation app, a food delivery service, or conducting market research, this API provides a plethora of endpoints that can meet your needs.

Key Features

Restaurant Search and Details:

Retrieve information about restaurants, including name, location, cuisine, and average cost.

Search for restaurants based on various criteria like location, cuisine type, and budget.

Location Data:

Access details about specific locations including cities, and neighborhoods, and their popularity.

Use geo-coordinates to find restaurants nearby.

Cuisine and Establishment Types:

Get a list of available cuisines in a specified location.

Discover different types of establishments such as cafes, bars, and fine dining options.

User Reviews:

Fetch reviews and ratings for restaurants.

Access user-generated content that provides insights into customer experiences.

How to Use

To get started with the Zomato API:

Sign Up: Register on the Zomato Developers portal to get an API key.

Documentation: Review the API documentation to understand the available endpoints and how to use them.

Integration: Use your API key to authenticate requests and integrate the data into your application.

Zomato Scraper

Overview

While the official Zomato API offers extensive access to data, some information might not be available through the API. In such cases, a Zomato Scraper can be a valuable tool. Web scraping involves extracting data directly from web pages, providing a way to collect information not exposed by the API.

Key Uses

Custom Data Extraction:

Extract details that might not be available through the API, such as additional reviews or specific dish information.

Data for Analysis:

Collect large amounts of data for sentiment analysis, market research, or machine learning models.

Monitoring Changes:

Track changes in restaurant details, menu items, and pricing over time.

Ethical Considerations

Using a Zomato Scraper requires careful consideration of Zomato’s terms of service and legal guidelines. It's crucial to:

Respect Robots.txt: Check and comply with Zomato’s robots.txt file to ensure you are not violating their scraping policies.

Avoid Overloading: Implement rate limits to avoid overloading Zomato’s servers.

Use Responsibly: Ensure that the scraped data is used ethically and for legitimate purposes.

Zomato Review API

Overview

For applications that focus on user-generated content and feedback, the Zomato Review API is an invaluable resource. This API specifically targets reviews and ratings provided by users, offering detailed insights into customer satisfaction and dining experiences.

Key Features

Review Data:

Access detailed reviews including the user’s comments, ratings, and review date.

Filter reviews by date, rating, or relevance.

User Information:

Obtain information about the reviewers, such as their user profile and review history.

Analyze patterns in reviews from specific users or demographics.

Sentiment Analysis:

Use review data to perform sentiment analysis, gauging public opinion and trends.

Identify key themes and sentiments in user feedback.

How to Use

To leverage the Zomato Review API:

Obtain Access: Similar to the Zomato API, secure access by registering and obtaining an API key.

Explore Endpoints: Use the provided endpoints to fetch reviews and associated data.

Integrate and Analyze: Integrate the review data into your system and use it for various analysis and insights.

[mobileapp14]

How to Scrape Zomato Delivery Apps Data: A Comprehensive Guide

How to Scrape Zomato Delivery Apps Data: A Comprehensive Guide

Dec 26, 2023

Introduction

In the burgeoning world of food delivery, platforms such as the Zomato Food Delivery App have become paramount. These apps not only simplify the ordering process but also offer a treasure trove of data for businesses and researchers. However, diving into Zomato's data pool requires adept techniques and ethical considerations. Using tools like the Zomato App Scraper can aid in this endeavor, ensuring accurate Food Delivery Apps Scraping. One of the prized datasets within is the ability to Extract Restaurant Menu Data, offering insights into culinary trends and consumer preferences. Navigating this extraction process responsibly is crucial, balancing the desire for information with respect for user privacy and platform guidelines.

Understanding The Landscape

Before delving into the nuances of Zomato Food Delivery App Scraping, it's paramount to comprehend the expansive ecosystem of Zomato. This renowned platform encompasses a vast repository of information, ranging from intricate restaurant particulars and comprehensive menu listings to competitive pricing, user feedback through reviews, and punctual delivery timelines. Such a diverse dataset isn't merely about food—it's a goldmine for businesses aiming for in-depth market analysis, establishing benchmarks against competitors, and formulating astute strategic blueprints. Leveraging tools like the Zomato App Scraper is pivotal for professionals keen on Food Delivery Apps Scraping. Especially noteworthy is the capacity to Extract Restaurant Menu Data, which provides a window into evolving culinary preferences and potential market gaps. As we navigate the realm of data extraction, it's crucial to approach this task with precision, ensuring the integrity of the data while adhering to ethical standards and platform policies.

Preliminary Research & Planning

Preliminary Research and planning are pivotal in ensuring a successful scraping endeavor, especially when dealing with a multifaceted platform like Zomato.

Platform Analysis

Zomato's presence across the iOS and Android ecosystems necessitates a comprehensive understanding of each platform's distinct features and intricacies. For instance, while the user interface might remain consistent, backend data structures, API endpoints, or data presentation could vary between iOS and Android. Recognizing these variances is crucial. Those familiar with app development nuances can attest that each platform has its unique way of handling data, permissions, and security protocols. Thus, tailoring the Zomato App Scraping method to suit the specificities of iOS versus Android can optimize efficiency and accuracy.

Data Identification

Once the platform nuances are understood, the next step is meticulous Data Identification. This involves pinpointing precise data elements that align with your research objectives or business needs. Whether you're keen on extracting granular details like restaurant ratings, the intricacies of delivery fees, or delving into user-specific preferences and feedback, clarity in defining these data points ensures that the scraping process remains targeted and yields relevant results. This focused approach not only streamlines the extraction process but also enhances the quality and relevance of the acquired data.

Tools & Technologies

In data extraction, employing the right tools and technologies can significantly influence the efficiency and accuracy of the scraping process. Here's a closer look at some pivotal tools tailored for specific scraping needs:

Mobile App Scraping

Regarding Mobile App Scraping, specialized frameworks and tools have become indispensable. Frameworks like Appium stand out, offering a robust platform-agnostic solution. Appium allows testers and developers to automate interactions with mobile apps across both iOS and Android platforms, making it apt for scraping Zomato's diverse user base. Complementing this, tools like Charles Proxy provide a powerful way to inspect and intercept app traffic. By setting up Charles Proxy correctly, one can gain insights into the app's backend requests, responses, and data flows, facilitating a more structured approach to data extraction.

Mobile App Scraping Libraries

Many mobile app scraping libraries come to the forefront for those focusing on Zomato's app interface. With its rich data manipulation ecosystem, Python offers gems like BeautifulSoup and Scrapy. BeautifulSoup simplifies parsing HTML and XML documents, enabling users to extract specific data elements effortlessly. On the other hand, Scrapy is a comprehensive app crawling framework, empowering users to scale their scraping operations efficiently, making it an excellent choice for projects requiring extensive data extraction from platforms like Zomato.

Ethical & Legal Considerations

Ethical and legal considerations are paramount in the realm of mobile app scraping, particularly from platforms like Zomato. Ensuring compliance not only upholds the integrity of the scraping process but also safeguards against potential repercussions.

Terms of Service

A thorough understanding and adherence to Zomato's Terms of Service and scraping policies is the foundational pillar of any scraping endeavor. These guidelines delineate the permissible actions concerning data access, usage, and redistribution. Ignoring or circumventing these terms can lead to legal complications, including potential bans or legal actions. Hence, it's imperative to review these terms meticulously and ensure that the scraping activities align with the platform's stipulations.

Rate Limiting & Access Restrictions

Beyond ethical concerns, there are practical challenges, primarily around rate limiting and access constraints. Platforms like Zomato employ rate-limiting mechanisms to prevent overwhelming their servers and maintain a consistent user experience. To navigate these limitations, scraping endeavors should integrate strategic measures. Implementing request throttling ensures that the scraping requests are spaced out, preventing a barrage of simultaneous requests that could trigger rate-limiting responses. Furthermore, employing IP rotation—switching between IP addresses—adds an extra layer of anonymity and reduces the risk of being flagged for suspicious activity. By proactively addressing these challenges, one can ensure a smoother, more sustainable scraping operation that respects both the platform and its users.

Script Development & Automation

In the intricate process of scraping data, especially from dynamic platforms like Zomato, meticulous script development and automation are indispensable.

Targeted Scraping

To extract meaningful insights, it's pivotal to adopt a targeted approach. One can ensure precise and relevant data extraction by crafting scripts that focus on specific API endpoints or distinct mobile app elements. This specificity minimizes unnecessary data retrieval, optimizing both time and resources.

Error Handling

In any automated process, unforeseen challenges can arise, jeopardizing the data's integrity. Therefore, robust error-handling mechanisms are crucial. Scripts should be designed to detect anomalies or disruptions promptly. Additionally, integrating comprehensive logging capabilities allows for real-time tracking of scraping activities. Such a proactive approach enhances the scraping operation's reliability and facilitates timely interventions, ensuring that the extracted data remains accurate and actionable.

Data Extraction & Storage

Efficient data extraction and storage methodologies form the backbone of any successful scraping initiative, ensuring the harvested information remains accessible, organized, and secure.

Structured Data

Organizing the extracted data in structured formats is paramount for subsequent analysis and interpretation. Formats like JSON (JavaScript Object Notation) or CSV (Comma Separated Values) provide a standardized structure, facilitating seamless integration with various analytical tools. Such structured data streamlines the analysis process and enhances the clarity and reliability of insights derived.

Database Storage

Once data is extracted, its storage demands careful consideration. Opting for secure, scalable database solutions is essential. By prioritizing data integrity and accessibility, businesses can ensure that the harvested information remains consistent, protected from unauthorized access, and readily available for future use. Leveraging robust database management systems (DBMS) further fortifies the storage infrastructure, guaranteeing optimal performance and reliability.

Continuous Monitoring & Maintenance

The landscape of mobile app scraping is dynamic, requiring vigilant oversight and adaptability to maintain efficacy and compliance.

Proactive Monitoring

Continuous surveillance of scraping operations is essential. Proactive monitoring activities can swiftly identify anomalies, disruptions, or potential bottlenecks. Such vigilance allows for timely interventions, ensuring the scraping process remains uninterrupted and data integrity is preserved. Regular reviews also provide insights into performance metrics, facilitating continuous optimization of the scraping strategy.

Adaptability

The digital ecosystem, including platforms like Zomato, undergoes frequent updates and modifications. To ensure sustained effectiveness, it's imperative to remain updated on any changes to the app's structure, policies, or security protocols. By staying abreast of these developments, scraping methodologies can be promptly adjusted or refined, ensuring they align with the platform's current configuration and regulatory requirements. Embracing adaptability ensures longevity and relevance in the rapidly evolving mobile app scraping domain.

Conclusion

Navigating the intricacies of Zomato Delivery Apps offers a gateway to unparalleled insights. Yet, as with any endeavor, integrity, and adherence to ethical standards remain paramount. At Mobile App Scraping, we emphasize responsible data extraction, ensuring our clients harness the potential of Zomato data ethically and effectively. Our suite of tools and expertise ensures data gathering and the derivation of actionable insights pivotal for success in the dynamic food delivery arena.

Elevate your strategic decisions with Mobile App Scraping. Let's embark on a journey of informed choices and innovation. Dive deeper, drive better. Join Mobile App Scraping today!

know more: https://www.mobileappscraping.com/scrape-zomato-delivery-apps-data.php

[foodspark-scraper]

How To Scrape Data From Zomato Food Delivery Website?

Introduction

If you want to get your hands on some of the most essential pieces of info from Zomato's app, you can hop on web scraping. Zomato has numerous lumps of data about restaurants, menus, and reviews. Web scraping comes to your resume for collecting this info for various reasons like studying the market, checking competitors, or making your apps.

But before you step into the market, remember that Zomato restaurant data scraping should be fair and follow the rules. Keep an eye on Zomato's terms and rules before you begin your web scraping journey. You can easily use various coding languages like Python and tools to scrape better. You can seamlessly make a scraping plan once you are well-versed in how a website is built and its parts work in HTML. But know that websites change, so your plan might also require updates.

In this blog, we will examine how you can scrape data from the Zomato food delivery website. So, without any further adieu, let's dig deep!

Which Data To Scrape From Zomato Food Delivery Website?

When it comes down to scraping data from various food delivery websites, the list can be a long one. Some of the most common pieces of information that web scrapers consider include:

Restaurant's ID

Restaurant's Name

Address

State

City

Country Code

Postal Code

Cost

Aggregate Ratings

Highlights

Email Id

Cuisines

Latitude

Longitude

Opening Hours

Once this information is gathered, it can be processed and organized in a structured format.

Why Scrape Data From Food Delivering Websites?

Web scraping, a powerful method of collecting information from websites, is critical for gaining valuable insights. While your initial points highlighted the significance of web scraping services in the food delivery sector, there are additional dimensions to explore, each offering unique benefits to businesses.

Enhanced Understanding of the Market

Web scraping is used for more than just pricing information. It serves as a portal for conducting in-depth market research. Businesses can learn a lot about their competitors' pricing strategies, as well as their menu offerings, promotional activities, and delivery options.

Adaptive Pricing Strategies

Real-time monitoring of price changes made by competitors is possible with web scraping. Companies can improve their pricing strategies by gathering information on how competitors adjust their prices in response to variables like shifting demand or seasonal trends. By doing this, they can maintain their profitability while remaining competitive.

Tailored Local Advantage

Web scraping helps businesses understand their competitors' local performance in a world where local preferences matter. Insights are gained by extracting data on specific delivery areas, customer preferences, and regional menu variations, which fuel more precise and impactful marketing campaigns.

Streamlined Operations

Through efficient data scraping services, businesses can extensively improve their operations by gathering competitor information such as contact information, operating hours, and delivery routes. Reduced delivery times and higher customer satisfaction may result from this optimization, based on rival companies' data.

Harvesting Customer Sentiments

Customer reviews are critical in the digital age. Web scraping enables businesses to collect and analyze customer feedback from various platforms.

This aggregate sentiment data can be subjected to sentiment analysis, revealing information about customer preferences, pain points, and trends. With this information, businesses can improve their offerings to meet the needs of their customers better.

Tailored Marketing Approaches

Web scraping expands beyond competitor insights. Businesses can decode individual customer behavior and preferences by aggregating data from food delivery platforms. This data can create personalized marketing campaigns, suggest menu items based on past orders, and foster stronger customer-brand connections.

Forging Strategic Alliances

Extraction of food delivery data also opens the door to future collaborations. Businesses may identify popular restaurants, understand their operational capabilities, and form partnerships. This symbiotic strategy can benefit both parties and result in mutual growth.

Web scraping services as a driver for well-informed decision-making, innovation, and operational excellence for food delivery data and goes beyond simple data collection.

The benefits of online scraping are wide-ranging and include improving pricing tactics, comprehending client sentiment, and streamlining processes. Those who harness the potential of web scraping as the food delivery sector develops will survive and prosper in this cutthroat environment.

Zomato Restaurant Data Scraping - A Brief Overview

Using specific techniques to simplify web content retrieval, particularly from sites like Zomato, is required. The Python' queries' package comes in handy here, removing the need for manual URL manipulation and streamlining HTTP/1.1 queries. It supports the addition of data such as form data and headers. 'BeautifulSoup' is another Python package for parsing complicated HTML and XML and facilitating data extraction.

The target URL must be specified when requesting Zomato's data, primarily for restaurant listings. A loop locates particular HTML div tags with the needed information ('col-s-8 col-l-1by3' class). Iteration extracts data from each restaurant separately, creating an exhaustive list.

The script stores various restaurant data in a 't' variable, including addresses, names, cuisines, pricing, and reviews. HTML 'tr' tags enclose these attributes. Accessing Zomato content is more accessible by leveraging tools such as Requests and BeautifulSoup. This automated method speeds up restaurant data extraction by eliminating the need for manual URL manipulation.

What To Do With The Extracted Food Delivery Data?

A wide range of stakeholders in the food industry and beyond can benefit from the knowledge and opportunities gleaned from mined food delivery data. Here are some ways how you can use the fetched food delivery data from Zomato's website:

Restaurant Information

You can discover new nearby eateries and monitor their popularity by examining details like restaurant names, categories, menus, and images.

Pricing and Discounts Insights

By analyzing data related to deals and discounts, you can undercut the prices of your rivals. After that, you may focus on your pricing strategy to ensure that each offer is fair.

Evaluating Ratings & Reviews

Every multi-location firm may quickly assess the service quality gaps in each location and choose your branding strategy thanks to data connected to ratings and reviews.

Understanding Opening Hours

Determine whether chains and services offer early breakfast or late-night delivery options by studying places where competition has limited operating hours and taking advantage of the market.

Enhanced Marketing Approaches

Utilizing data insights about reasonable pricing and delivery charges, you can collaborate with micro-influencers to optimize your marketing campaigns.

Wrapping Up

Making websites and apps has gotten way better. There are no fixed rules for how today's apps or websites should be. Every business has its reasons for getting info from the web. So, there's no one-size-fits-all way to pick a web scraping solution.

Foodspark is an excellent option to hop on if you opt for Zomato restaurant data scraping. It's one of the top services for web scraping that help students, small businesses, and analysts get essential information from popular websites without making a hole in your pocket.

Stay in contact!

[fooddatascrape]

Use Zomato 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.

[fastractorblog]

Benefits Of Data Extraction Tool For Web Services Business

When you start your business, it will go smoothly. Sometimes, or at the initial stage, you will need the data to maintain your potential clients and make them in the loop. Yes, at this stage, you will require a data extraction tool to fetch the data of your potential customers. Suppose you have a business whose transaction has been done on the Facebook platform to deal with and track all the customers. Thus, you will require a facebook data scraper to fetch the potential customers to maintain the data of those clients and bind them into a loop.

Before doing anything, you should know what data extraction is and how the tool work? It helps you make your data fetching smooth and maintain your data appropriately. For instance, you have to fetch the data of India from facebook, then you should choose the tradeindia data extractor for getting only India's facebook data. 

Benefits of data extraction tools 

Scalability:

The data extraction software is essential for helping the company collect data at any scale. Without this kind of tool, users will manually parse through sources to collect this information. How much data an organization consumes, and its ability to leverage collected data by the manual process. If you want to use automated extraction, the business needs to increase the amount of data deployed for the particular use case.

Efficiency:

If you use the data extraction tools that support the efficiency, especially when considering the time included in the collecting the data. Data extraction software using AI, RPA, and ML options considerably determine the collecting of relevant data. Companies that leverage data extraction tools substantially decrease the time for the data-driven process. 

Business process management:

Data extraction software leveraging RPA or the various aspects of the AI can do more things instead of identifying and collecting the relevant data. These options are useful for inserting the data into downstream processes. For example, specific RPA options can extract the data of your customers, like addresses, emails, numbers, and initiate the process. At the same time, that data/information is placed in the appropriate fields on the insurance forms. The other example is, like I had mentioned above, a facebook data scraper. 

Control:

Data extraction tools are the key to identifying which data is essential and then collecting the data from disparate sources. Companies understand the functionality that migrates data from any sources into the target services, reduces reliance on data silos and develops meaningful interaction with data. Suppose you have to find the data of the entire India, then the company has to use the tradeindia data extractor.

Accuracy:

Using data extraction provides a more advanced preparation process that leads you to help and manage complex data streams. Basically, it is the capability combined with the removal of human error and user bias that results in increased accuracy and the quality of data. 

Usability:

It is the last benefit, but the most obvious relies on the data extraction tools for the user's ease. These tools give the business users an intuitive user interface and provide a visual view of the data process and rules. 

Wrapping up...

These are the benefits that would help your business data fetching. Suppose you have a business that needs the customers' data or the history of the customer. Suppose you have a zomato business, and you need to collect the data so you will need the data extractor.

[scrapingintelligence-blog]

Need Zomato Data Scraper Tool. The company we offer the services of Scrape Zomato Restaurant Data, Best Zomato Restaurant Data Scraping in USA, Spain, Australia at affordable prices.

Extracts important data from Zomato Restaurant ID, Restaurant Name, Address, City, State, Postal Code, Country Code, Aggregate Ratings, Cost, Cuisines, Email ID, Highlights, Latitude, Longitude, Menu, Opening Hours, Order Menu, Phone, Price Range, Review, Votes, and Website

All Search Criteria same as the type of Zomato account for deep search results

Highly supports Basic, Premium & Recruiter accounts

The choice to extract data from all best restaurants, cafes of the search result or only the best restaurants, cafes Listing with published email addresses

Ready-to-use Zomato Data Extraction Tool to get started instantly

Know More:��  Zomato Data Scraping Services

[webscreenscraping]

How Web Scraping Of Zomato Can Be Done By BeautifulSoup Library In Python?

Introduction

Web scraping, also known as data scraping, is a kind of data extraction used to gather information from different websites. The software of web scraping uses a web browser or HTTP to access these websites. The software user performs web scraping manually but web scraping is generally known for automated procedures done by bots or by a web crawler. This is a type of process where specific data from the websites and the internet are copied and stored into a local dataset or spreadsheet to retrieve the data later.

Here, we will use Zomato data scraper to gather information on the best restaurants in Bengaluru, India. HTML website pages will be used in accessing and reading the information.

Scraping the Website Content

The web address is typed in the browser and the HTTP request is made to visit the webpage. If a request is successfully completed, the web page will be displayed by the browser otherwise or it will show an error. The same kind of request is made for accessing a Zomato web page.

Some of the tools that are available with us help us use Python to access a web page.

import requests from bs4 import BeautifulSoup

Let us understand the uses of libraries before using them as well as functions in accessing a web page.

Making a Request

It is created for humans who are dependent on the language. It eliminates the need of adding query strings manually to the URLs or encrypting the post data. The Requests allow you to use Python in sending requests of HTTP/1.1. You can use simple Python libraries to add material like headers, multipart files, form data, and arguments. Similarly, Python's response data can be retrieved.

BeautifulSoup (BS4)

BeautifulSoup4 is a package of Python for data extraction from XML and HTML files. It integrates with your preferred parser to offer navigation, search, and modification of a parse tree. This is normal for programmers to save hours or even days of effort.

After knowing the tools, we shall now try to access the web page of Zomato.

The data of the best hotels on Zomato has now been put in the variable. However, it is not in the readable format for everyone except computer scientists. Let's see the uses of scraped data.

Here, we are looking for the name of restaurant, address of a restaurant, and the category of cuisine. To start looking for all these characteristics, we need to locate the HTML elements that contain this data.

By looking at the BeautifulSoup material mentioned above, or by using a review on your Web Browser called Chrome to check which tag holds the gathering of the best restaurants, as well as additional tags with more information.

top_rest = soup.find_all("div",attrs={"class": "bb0 collections-grid col-l-16"}) list_tr = top_rest[0].find_all("div",attrs={"class": "col-s-8 col-l-1by3"})

The preceding code will look for any div HTML tags with the class="col-s-8 col-l-1by3" and return data for collecting lists of hotels. We need to use a loop for accessing the list items, i.e., a restaurant information at a time, for extracting additional information using loop.

list_rest =[] for tr in list_tr:    dataframe ={}    dataframe["rest_name"] = (tr.find("div",attrs={"class": "res_title zblack bold nowrap"})).text.replace('\n', ' ')    dataframe["rest_address"] = (tr.find("div",attrs={"class": "nowrap grey-text fontsize5 ttupper"})).text.replace('\n', ' ')    dataframe["cuisine_type"] = (tr.find("div",attrs={"class":"nowrap grey-text"})).text.replace('\n', ' ')    list_rest.append(dataframe) list_rest

The tr variable in the preceding code holds various details about the hotel, such as its name, cuisine, address, prices, reviews, and menu. Each piece of information is saved in its particular tag, which can be identified by looking at the tr called each item’s data.

Before looking for tags in the HTML, we should take a look at how the restaurant's menu appears on the website.

You can see in the above images that the data required to get scraped is shown in several formats. Returning to HTML content, we have discovered that data is kept within the div tag in the modules defining the kind of formats or fonts used.

The dataframe is developed for collecting necessary information. We go through each detail of data one after another and save it in diverse DataFrame columns. Because HTML data utilizes ‘n’ to split data that cannot be saved in a DataFrame, we will have to employ a few String functions. As a result, we can substitute ‘n’ with “to prevent any issues with space.

Results obtained from the above-mentioned code would be like-

Saving Data in a Readable Format

Presume the situation where you need to deliver data to a person who is not familiar with Python. They will not understand any information. The dataframe data will be saved in a readable format like CSV.

import pandas df = pandas.DataFrame(list_rest) df.to_csv("zomato_res.csv",index=False)

The code above will generate the Zomato res CSV file.

Conclusion

In this blog, we have learned to make Requests for accessing a web page from Python and BeautifulSoup4 for extracting HTML data from the available content. Then, the data was formatted in a dataframe and saved in a CSV format.

Looking for Web Scraping Service to scrape Zomato data? Contact Web screen Scraping now! Request for a quote!

[scrapingintelligence-blog]

Need Zomato Data Scraper Tool. The company we offer the services of Scrape Zomato Restaurant Data, Best Zomato Restaurant Data Scraping in the USA, Spain, Australia at affordable prices. 

Visit Us:  http://www.websitescraper.com/zomato-scraper/