How to Extract Data from Zomato API?

Zomato Data Scraping API, Scrape Zomato Data API , Extract Zomato Data API, How-to-Extract-Data-from-Zomato-API

Introduction

In the world of culinary delights, Zomato stands tall as one of the most popular platforms, offering a treasure trove of information about restaurants across various cities. With its rich and extensive API, we can extract valuable data on citywide restaurants listed on Zomato. In this blog, we will explore the process of accessing the Zomato API, extracting restaurant data for multiple cities, and creating a comprehensive CSV file that organizes this data efficiently.

Prerequisites

Before diving into the data extraction process, make sure you have the following:

A valid Zomato API key: To access Zomato’s API, you need an API key, which you can obtain by signing up on their developer platform.

Python Environment: Ensure you have Python installed on your system and the necessary libraries, such as requests and pandas.

Step 1: Accessing the Zomato API

To get started, import the required libraries in your Python script:

Accessing-the-Zomato-API

Next, set up your Zomato API key:

api_key = "YOUR_ZOMATO_API_KEY"

Step 2: Extracting Citywise Restaurant Data

Now, let’s create a function to fetch the restaurant data for a specific city:

Extracting-Citywise-Restaurant-Data

The get_restaurants() function inputs the city’s name and returns a list of restaurants in JSON format.

Step 3: Looping Through Multiple Cities

To create a comprehensive dataset, we can loop through a list of cities and extract restaurant data for each city:

Looping-Through-Multiple-Cities

In this function, the city is a list of city names you want to extract data. The function returns a list of restaurant details for all the cities combined.

Step 4: Saving the Data to a CSV File

Finally, we can use pandas to convert the extracted data into a CSV file:

Saving-the-Data-to-a-CSV-File

The save_to_csv() function takes the restaurant data and the desired file name as input and saves the data to a CSV file.

Step 5: Putting It All Together

Now that we have all the necessary functions let’s run the entire process:

Putting-It-All-Together

In this example, we have chosen five cities for illustration. You can customize the cities_list to include any cities of your choice.

Conclusion

Congratulations! You have successfully extracted restaurant data from the Zomato API for multiple cities and created a comprehensive CSV file. With this CSV dataset, you can perform further analyses, visualize trends, or even build exciting applications based on citywide restaurant information.

Exploring the vast world of gastronomy through the Zomato API opens up endless possibilities for restaurant enthusiasts, data analysts, and developers alike. Enjoy discovering new culinary wonders and happy data exploration!

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sources >> https://www.actowizsolutions.com/extract-data-from-zomato-api.php

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