Web scraping is the process of extracting data from a website and using it for a variety of purposes. It can be a useful tool for businesses looking to gather information on their competitors, for individuals looking to track changes on a website, or for researchers looking to gather data for a study.

There are many different ways to scrape a website, but one of the most popular methods is using an API. An API, or application programming interface, is a set of rules and protocols that allow different software programs to communicate with each other.

SERP stands for Search Engine Results Page. It is the page that is displayed to a user after they enter a query into a search engine, such as Google, Bing, or Yahoo. The SERP contains a list of results, also known as "search results," that are relevant to the user's query. These results can include web pages, images, videos, and other types of content. The SERP also includes other elements such as sponsored ads, local packs, featured snippets, and more.

SERPs are constantly changing and evolving as search engines continue to improve their algorithms and add new features. The layout, format, and content of SERPs can vary depending on the search engine, the query, and the location of the user. Understanding the SERP and what appears on it is crucial for SEO and SEM professionals, as it helps them to understand how their website is performing in search engine results and what they can do to improve their visibility and performance.

When it comes to scraping Google's search engine results pages (SERPs), there are several APIs available, both free and paid. In this article, we'll take a look at the top 7 Google SERP APIs and compare their pros and cons.

The first option we are going to explore is WebScrapingAPI as it brings the best value for money compared to others in the list.

WebScrapingAPI can be used by developers to scrape the web and has a lot of great features such as residential proxies, JavaScript rendering, CSS extracting, CAPTCHA bypassing, IP rotation, and evasions which gets them rid of the stress of being banned. It is very easy to use and all you need to do is to send your API KEY and a search query in an API call, just like this: https://serpapi.webscrapingapi.com/v1?engine=google&api_key=<YOUR-API-KEY>&q=Steve%20Jobs and that’s it, you are now ready to receive your results back. On top of that, this service offers a lot of dedicated scrapers for Google or Amazon, making it a great option for businesses or individuals who want to affordably scrape data from these websites and use it for different purposes.

When it comes to pricing, WebScrapingAPI has something for everybody with pricing starting from as little as 49$ per month for 100.000 API calls to an Enterprise plan for those who want to scrape the web at a large scale and need more than 10.000.000 API calls. The cost per request may vary depending on whether you use rendering JavaScript or residential proxies from 1 to 25 API credits.

This service also offers you the chance to try all products and features for free for 7 days with no credit card required, thus enabling you to find out how closely it satisfies your needs before you purchase a subscription. In case you encounter any issue with the product, you can easily get in touch with their technical support and someone will get back to you very soon, usually within a couple of hours.

Pros: By far best value for money from this list. This API is feature-rich, has great customer care and it is suitable for every budget.

Cons: Does not provide the response in many different formats such as CSV as some other options from the list, but you can easily convert the data using an JSON to CSV parser.

The second option we are going to explore is Apify which provides a great Google Scraping service.

The API allows users to extract data from Google search results pages in real time and offers a variety of options for customizing the scrape, such as the ability to search for specific keywords, retrieve data on specific SERP elements, and scrape results from multiple countries and languages. Additionally, the Apify platform offers an actor library of pre-made scraping and automation tasks, which can save time and effort for users who need to perform repetitive scraping tasks. The platform also offers a cloud storage for the scraped data which can be easily exported to different formats and a feature called Autoscaled Pool of Headless Browsers that allows to run multiple concurrent scraping tasks. Apify's Google scraping API is a valuable tool for businesses, researchers, and individuals looking to gather data on search trends, track changes in SERPs, and gather data on competitors.

When it comes to pricing, Apify has a pay-as-you-go pricing model, which means that users are charged based on the number of page loads and the amount of data scraped. The exact pricing can vary depending on the specific scraping needs of the user. It's worth mentioning that Apify offers a free trial with a certain amount of free page loads and storage, so you can test the platform and see if it meets your scraping needs before committing to a paid plan.

Pros: Offers a lot of scrapers and flexibility, thus being suitable for a lot of different scraping needs.

Cons: Price and monitoring systems can become confusing, especially at a greater scale.

Another option we are going to explore is SerpAPI which is a service that allows users to scrape Google's search engine results pages (SERPs) in real time. 

SERP API is a powerful web scraping tool specifically designed to scrape Google's search engine results pages (SERPs) in real-time. One of the key features of SERP API is its ability to scrape SERPs from multiple countries and languages, which allows users to gather data on search trends in different regions. Additionally, SERP API offers an extensive list of endpoints, which allows users to retrieve specific types of data from SERPs, such as organic search results, paid search results, featured snippets, local packs, images, videos, etc.

Another great feature of the service is its ability to handle CAPTCHAs, which can be a common issue when scraping Google SERPs. This allows users to continue scraping even when faced with CAPTCHAs, without having to manually solve them.

SerpAPI also offers a user-friendly dashboard that allows users to monitor the usage and track the progress of the scrapes and also offers support for users who need help getting started or troubleshooting issues.

This API stands out by being extremely feature-rich having scrapers for very many different engines such as: 

While most of the engines offer high quality data fastly and provide great documentation which is very easy to follow, at the time of writing this article there are a few engines that do not work as intended, but their team is currently working on fixing these issues, so you can expect that to improve soon. 

When it comes to pricing, it starts from $50 for 50,000 Google searches per month and can get expensive at a larger scale so it is definitely not as cheap as some of the other options from this list.

Pros: It is the option with most features from this list with great documentation making them very easy to use. This service offers a beautiful playground for you to use and test if the product satisfies your needs before committing to a paid subscription.

Cons: The pricing is quite expensive and some engines are currently unreliable.

The fourth option we are going to talk about is SerpWow which provides an easy-to-use Google Search API.

This service is powerful and it enables the developer to retrieve SERP data in real time, which can be used to build SEO tools, track keyword rankings, and analyze search engine performance. SerpWow uses a wide variety of proxies, thus getting the users rid of the worry of being banned and giving the data back in either JSON, CSV, or HTML. On the other hand, at the time of writing this article, they are not as feature-rich as other options from this list and provide scrapers only for a few Google engines. 

When it comes to pricing, it starts from $45 for 5000 Google searches, and as you may already have noticed it is very expensive compared to other options. SerpWow is intended more for Big Data purposes, having the most plans designed for Big Data from this list. In this case, the prices are starting from 500$ per month for 100.000 searches and get as expensive as 6300$ per month for 3.000.000 searches.

Pros: This service offers an easy to use Google Scraping API which returns the data back in various formats.

Cons: It is very expensive and not as feature-rich as other options.

The next option we are going to talk about is OxyLabs which is a professional scraping company that can help businesses and individuals to extract data and insights from the search engines to improve their online performance.

OxyLabs is a very well-known company in the scraping world and they developed a SERP solution that is high quality and reliability that uses proxy rotation in order to avoid getting banned. This service offers a fast SERP API and they provide in-depth tutorials by providing many code samples showing how to integrate their API into your product. 

But, same as the last option on the list it is very expensive with plans starting from 99$ per month for 29.000 requests (limited to 15 results per request) and getting as expensive as 10.000$ per month for more than 10.000.000 searches (no search limits per results).  

Pros: This service offers a fast, reliable and scalable Google SERP solution.

The sixth option we are going to explore is SerpStack, a product developed by Apilayer, a software company based in London. 

While this product was designed first for internal needs, it has grown to become one of the most reliable products in the marketplace. The Serpstack API allows automated retrieval of SERP data from search engines by utilizing a proxy network. With solid documentation, they make scraping Google an easy and straightforward process for developers.

When it comes to pricing it is more affordable than other options from the list, starting from 30$ for 5.000 searches and getting as expensive as 200$ per month for 50.000 searches. It is worth mentioning that if you need scraping at a larger scale this service also offers a custom Enterprise plan designed for such needs. In order to learn more about you need to get in touch with their sales team. 

Pros: This service offers a cheap and reliable Google SERP solution.

Cons: It is not as feature-rich as other candidates.

Lastly, we are going to talk about the SERP API offered by DataForSEO, a provider that offers a variety of tools and services that allow businesses and individuals to gather data on search engine rankings, keywords, backlinks, and other important SEO metrics.

The SERP API from DataForSEO is very flexible and suitable for those who are not sure how many requests are going to consume in a month, having a pay-as-you-go policy. This API is great and lets you scrape Google Search Engine and receive back high-quality data in a reasonable amount of time.

Having a pay-as-you-go payment system makes the price quite confusing and expensive for those who need Google scraping at a larger scale. It is more suitable for those who do not need as many API calls and do not have a constant number of calls per month.

Pros: Flexible and great for digital marketers or SEO consultants.

Cons: Gets expensive when scraping at scale and it is not as feature-rich as other options from the list.

In conclusion, I hope this article gives you an overview of the Google SERP available services in the marketplace. The ability to scrape Google's search engine results pages (SERPs) can be a valuable tool for businesses, researchers, and individuals looking to gather data on search trends, track changes in SERPs, and gather data on competitors. 

There are several options available for scraping Google SERPs and it is extremely important to consider the specific needs of your business or project and to carefully evaluate the features and limitations of each service.

Top 7 Best Google SERP APIs (Free & Paid)

Web scraping is a powerful tool that allows you to extract data from websites and use it for a variety of purposes. It can be used to gather data for business intelligence, track changes on websites, or build your own web applications. In this article, we will be focusing on how to scrape JavaScript tables using Python.

JavaScript tables are a common way to display tabular data on the web, and they can be found on a wide range of websites. Scraping these tables can be challenging because the data is often embedded in the page's source code as a JavaScript object, rather than in a standard HTML table. However, with the right tools and techniques, it is possible to extract this data using Python.

We will begin by setting up the necessary tools and installing any required libraries. Then, we will walk through the process of using Python and a web driver to load the webpage and extract the data from the table. Finally, we will discuss some ways to improve the efficiency and reliability of the scraping process, and why it may be better to use a professional scraper for more complex tasks.

Before we can start scraping JavaScript tables with Python, there are 2 prerequisites that we need to take care of:

Python: This tutorial assumes that you have Python installed on your machine. If you don't have Python installed, you can download it from the official website and follow the instructions for your operating system.

A web driver: In order to load and interact with webpages using Python, we will need to use a web driver. There are several options available, such as ChromeDriver, FirefoxDriver, and SafariDriver. For this tutorial, we will be using ChromeDriver.

Once you have Python and a web driver installed, you will need to install the following libraries:

Selenium: Selenium is a library that allows you to control a web browser through Python. We will use it to load and interact with the webpage containing the table. When it comes to JavaScript tables it is important to use a library like Selenium instead of Python requests because you can wait until a JavaScript generated element appears on the page in case it is not present when the webpage is loaded. 

Pandas: Pandas is a library that provides easy-to-use data structures and data analysis tools for Python. We will use it to store and manipulate the data that we extract from the table.

To install these libraries, open a terminal or command prompt and use the pip command to install them:

That's it! You are now ready to start scraping JavaScript tables with Python. In the next section, we will walk through the process step by step.

Now that we have all of the necessary tools installed, it's time to start scraping JavaScript tables with Python. The process involves the following steps

Load the webpage containing the table using Selenium and a web driver.

Extract the data from the table using Selenium and Python.

Store and manipulate the data using Pandas.

Let's walk through each of these steps in more detail:

The first thing we need to do is load the webpage containing the table that we want to scrape. We can do this using Selenium and a web driver.

First, let's import the necessary libraries:

from selenium import webdriver

from selenium.webdriver.chrome.service import Service

from selenium.webdriver.common.by import By

import time

Next, we'll create an instance of the web driver and use it to load the webpage:

# Replace "path/to/chromedriver" with the path to your ChromeDriver executable

driver = webdriver.Chrome(service=Service('path/to/chromedriver'))

# Load the webpage

driver.get('https://html.com/tags/table/')

It's important to note that the webpage must be fully loaded before extracting the data from the table. You may need to use the driver.implicitly_wait() function to wait for the page to load, or use the driver.find_element(By.*, ‘’) function to wait for a specific element on the page to be loaded.

Once the webpage is loaded, we can use Selenium to extract the data from the table. There are several ways to do this, but one method is to use the driver.find_elements(By.CSS_SELECTOR, ‘td’) function to locate the cells in the table and extract the text from each cell.

Here is an example of how to extract the data from a simple table with two columns:

# Find all of the rows in the table

rows = driver.find_elements(By.CSS_SELECTOR, 'table tr')

# For each row, find the cells and extract the text

for row in rows:

    try:

        cells = row.find_elements(By.CSS_SELECTOR, 'td') or row.find_elements(By.CSS_SELECTOR, 'th')

    except:

        continue

    for cel in cells:

        print(cel.text, end= " ")

    print()

driver.quit()

Remember that you may need to use a different CSS selector depending on the table's structure and the elements it contains. You can use the developer tools in your web browser to inspect the page and find the appropriate selector.

Once you've extracted the data from the table, you can store it in a Pandas data frame and manipulate it as needed. Here's an example of how to do this:

from selenium import webdriver

from selenium.webdriver.chrome.service import Service

from selenium.webdriver.common.by import By

import time

import pandas as pd

df = pd.DataFrame()

driver = webdriver.Chrome(service=Service('/path/to/chromedriver'))

# Use the webdriver to load a webpage

driver.get('https://html.com/tags/table/')

# When scraping JavaScript generated content it is important to wait a few seconds

time.sleep(4)

table = driver.find_element(By.CSS_SELECTOR, 'table')

# For each row, find the cells and extract the text

df = pd.read_html(table.get_attribute('outerHTML'))

print(df)

driver.close()

While the steps described above will allow you to scrape JavaScript tables using Python, there are a few ways to improve the process's efficiency and reliability.

One way to improve efficiency is to use a headless browser, which is a browser that runs in the background without a GUI. This can be faster than running a full browser, and it is less resource-intensive. To use a headless browser with Selenium, you can use the --headless flag when creating the web driver instance.

Another way to improve efficiency is to use a service that provides rotating IP addresses, such as a proxy server. This can help you avoid being detected as a scraper and blocked by the website, as the IP address of the request will appear to change with each request. WebScrapingAPI is a service that offers the possibility to scrape a website using a proxy server. You can learn more about how to use proxies for web scraping, feel free to check our docs. 

To use a proxy server with Selenium, I strongly recommend you to use selenium-wire since it is more straight-forward than plain Selenium when it comes to connecting to a proxy server. Just like any other Python package you can simply install it by running the following command:

Then you can use the following coding sample in order to use a proxy server with Selenium:

from seleniumwire import webdriver

from selenium.webdriver.chrome.service import Service

import time

# Create a webdriver instance with the desired proxy server and authentication details

API_KEY = '<YOUR-API-KEY-HERE>'

options = {

    'proxy': {

        'http': f'http://webscrapingapi:{API_KEY}@proxy.webscrapingapi.com:80',

        'https': f'https://webscrapingapi:{API_KEY}@proxy.webscrapingapi.com:80',

        'no_proxy': 'localhost,127.0.0.1'

    }

}

driver = webdriver.Chrome(service=Service('/path/to/chromedriver'), seleniumwire_options=options)

# Use the webdriver to load a webpage

driver.get('http://httpbin.org/ip')

# When scraping JavaScript generated content it is important to wait a few seconds

time.sleep(5)

# Do something with the page, such as extract data or take a screenshot

# ...

# Close the webdriver

driver.quit()

While these techniques can be useful for improving the efficiency and reliability of your web scraping, it is important to note that they are beyond the scope of this article. For more complex scraping tasks, it may be more efficient and reliable to use a professional scraper, such as WebScrapingAPI. This tool provides additional features, such as IP rotation and CAPTCHA bypass, that can make the scraping process way easier and more reliable.

In the next section, we will summarize the steps for scraping JavaScript tables with Python and discuss the benefits of using a professional scraper for more complex tasks.

In conclusion, scraping JavaScript tables with Python is a powerful way to extract data from websites and use it for a variety of purposes. Whether you are using your own code or a professional scraper, this technique can be a valuable tool for gathering data and gaining insights.

Find out how to Scrape JavaScript Tables with Python

Web scraping is a technique for extracting data from websites and can be a powerful tool for gathering information from the internet. This article will look at how to scrape HTML tables with Golang, a popular programming language known for its simplicity, concurrency support, and robust standard library.

HTML tables are a type of element in HTML (Hypertext Markup Language) that is used to represent tabular data on a web page. An HTML table consists of rows and columns of cells containing text, images, or other HTML elements. HTML tables are created using the table element, and are structured using the ‘<tr>’ (table row),‘<td>’ (table cell), ‘<th>’ (table header), ‘<caption>’, ‘<col>’, ‘<colgroup>’, ‘<tbody>’ (table body), ‘<thead>’ (table head) and ‘<tfoot>’ (table foot) elements. Now let’s go through each one and get into more detail: 

table element: Defines the start and end of an HTML table.

tr (table row) element: Defines a row in an HTML table.

td (table cell) element: Defines a cell in an HTML table.

th (table header) element: Defines a header cell in an HTML table. Header cells are displayed in bold and centered by default and are used to label the rows or columns of the table.

caption element: Defines a caption or title for an HTML table. The caption is typically displayed above or below the table.

col and colgroup elements: Define the properties of the columns in an HTML table, such as the width or alignment.

tbody, thead, and tfoot elements: Define the body, head, and foot sections of an HTML table, respectively. These elements can be used to group rows and apply styles or attributes to a specific section of the table.

For a better understanding of this concept, let’s see what an HTML table looks like:

At first look, it seems like a normal table and we can not see the structure with the above described elements. It does not mean they are not present, it means the browser already parsed that for us. In order to be able to see the HTML structure, you need to go one step deeper and use dev tools. You can do that by right-clicking on the page, clicking on inspect, clicking on select element tool, and click on the element ( table in this case ) you want to see the HTML structure for. After following these steps, you should see something like this: 

HTML tables are commonly used to present data in a structured, tabular format, such as for tabulating results or displaying the contents of a database. They can be found on a wide variety of websites and are an important element to consider when scraping data from the web.

Before we start scraping, we need to set up our Golang environment and install the necessary dependencies. Make sure you have Golang installed and configured on your system, and then create a new project directory and initialize a `go.mod` file:

$ mkdir scraping-project

$ cd scraping-project

$ go mod init <NAME-OF-YOUR-PROJECT>

$ touch main.go

Next, we need to install a library for making HTTP requests and parsing HTML. There are several options available, but for this article we will use the `net/http` package from the standard library and the golang.org/x/net/html package for parsing HTML. These packages can be installed by running the following command:

$ go get -u net/http golang.org/x/net/html

Now that our environment is set up, we are ready to start building our HTML table scraper using Golang.

Now that we have our environment set up, we can start building a scraper to extract data from an HTML table. The first step is to send an HTTP request to the web page that contains the HTML table we want to scrape. We can use the `http.Get` function from the `net/http` package to send a GET request and retrieve the HTML content:

package main

import (

	"fmt"

	"io/ioutil"

	"log"

	"net/http"

)

func main() {

	resp, err := http.Get("https://www.w3schools.com/html/html_tables.asp")

	if err != nil {

		log.Fatal(err)

	}

	defer resp.Body.Close()

	// Read the response body and convert it to a string

	body, err := ioutil.ReadAll(resp.Body)

	if err != nil {

		log.Fatal(err)

	}

	html := string(body)

	fmt.Println(html)

}

Next, we can use the `goquery.NewDocumentFromReader` function from the goquery package to parse the HTML content and extract the data we need. As any other Golang package you need to install it first as following: 

And then add the following code which will parse the HTML of the page: 

doc, err := goquery.NewDocumentFromReader(resp.Body)

if err != nil {

    log.Fatal(err)

}

Now that we have a parser and element extractor for our HTML, we can take advantage of the Goquery package `doc.Find()` functionality which enables us to find the specific elements we are looking for, in this case, a table. We can use it as follows:

doc.Find("table").Each(func(i int, sel * goquery.Selection) {

    // For sake of simplicity taking the first table of the page

    if i == 0 {

        // Looping through headers

        headers: = sel.Find("th").Each(func(_ int, sel * goquery.Selection) {

            if sel != nil {

                fmt.Print(sel.Text())

                fmt.Print(" ")

            }

        })

        fmt.Println()

        // Looping through cells

        sel.Find("td").Each(func(index int, sel * goquery.Selection) {

            if sel != nil {

                fmt.Print(sel.Text())

                fmt.Print(" ")

            }

            // Printing columns nicely

            if (index + 1) % headers.Size() == 0 {

                fmt.Println()

            }

        })

    }

})

That’s it, you are able now to scrape the table using Golang and you should be able to see it on the screen like this: 

As you may notice the structure can be quite confusing and hard to read. The good news is that you can do better than that and display the data nicely in a tabular format which is easy to read. This is a perfect job for tablewriter package which you can install as following: 

$ go get github.com/olekukonko/tablewriter

Now we need to do a few adjustments to our code before passing our information to a tablewriter such as defining the table headers, structs and storing them into an array. It should look something like this:

package main

import (

	"log"

	"net/http"

	"os"

	"github.com/PuerkitoBio/goquery"

	"github.com/olekukonko/tablewriter"

)

type Company struct {

	Company string

	Contact string

	Country string

}

func main() {

	resp, err := http.Get("https://www.w3schools.com/html/html_tables.asp")

	if err != nil {

		log.Fatal(err)

	}

	defer resp.Body.Close()

	// Read the response body and convert it to a string

	doc, err := goquery.NewDocumentFromReader(resp.Body)

	if err != nil {

		log.Fatal(err)

	}

	var companies []Company

	doc.Find("table").Each(func(i int, sel *goquery.Selection) {

		if i == 0 {

			e := Company{}

			sel.Find("td").Each(func(index int, sel *goquery.Selection) {

				if index%3 == 0 {

					e.Company = sel.Text()

				}

				if index%3 == 1 {

					e.Contact = sel.Text()

				}

				if index%3 == 2 {

					e.Country = sel.Text()

				}

                        // Add the element to our array

				if index != 0 && (index+1)%3 == 0 {

					companies = append(companies, e)

				}

			})

		}

	})

	table := tablewriter.NewWriter(os.Stdout)

	// Setting our headers

	table.SetHeader([]string{"Company", "Contact", "Country"})

	for _, Company := range companies {

		s := []string{

			Company.Company,

			Company.Contact,

			Company.Country,

		}

		table.Append(s)

	}

	table.Render()

}

You should be able now to see the data displayed in this format: 

At this point, you managed to build a scraper in Golang that scrapes a web page, and stores and displays the data nicely. You can also modify the code to scrape a table from another website. Please keep in mind that not all the websites on the internet are this easy to scrape data from. Many of them implemented high level protection measures designed to prevent scraping such as CAPTCHA and blocking the IP addresses, but luckily there are 3rd party services such as WebScrapingAPI which offer IP Rotation and CAPTCHA bypass enabling you to scrape those targets. 

While the technique we have described so far is sufficient for simple HTML tables, there are several ways in which it can be improved.

One potential issue is that the HTML table structure may not be consistent across all web pages. For example, the table may have a different number of columns or the data may be nested in different HTML elements. To handle these cases, you can use more advanced techniques such as CSS selectors or XPath expressions to locate the data you want to extract.

Another issue is that web pages often use AJAX or other client-side technologies to load additional data into the page after it has loaded in the browser. This means that the HTML table you are scraping may not contain all of the data you need. To scrape these types of pages, you may need to use a tool such as a headless browser, which can execute JavaScript and render the page just like a regular web browser. A good alternative to that is to use our scraper which can return the data after JavaScript is rendered on the page. You can learn more about this by checking our docs.  

Finally, it's important to consider the performance and scalability of your scraper. If you are scraping large tables or multiple pages, you may need to use techniques such as concurrency or rate limiting to ensure that your scraper can handle the load.

I hope you found this resource as a good starting point for scraping HTML tables with Golang. We have walked through the process of scraping data from an HTML table using the Go programming language. We looked at how to retrieve the HTML content of a web page, print it on the screen and display it in a tabular readable human eye format. We also discussed some of the challenges that you may encounter when scraping HTML tables, including inconsistent table structures, client-side data loading, and performance and scalability issues.

While it is possible to build your own scraper using the techniques described in this article, it is often more efficient and reliable to use a professional scraping service. These services have the infrastructure, expertise, and security measures in place to handle large volumes of data and complex scraping tasks, and can often provide data in a more structured and convenient format such as CSV or JSON.

In summary, scraping HTML tables can be a useful way to extract data from the web, but it is important to carefully consider the trade-offs between building your own scraper and using a professional service.

Find out how to scrape HTML tables with Golang

Google Maps is one of the most widely used mapping and navigation services in the world, providing users with an easy way to find and explore places, businesses, and points of interest. One of the key features of Google Maps is the ability to search for places and view detailed information about them, such as their location, reviews, photos, and more.

Scraping this data from Google Maps can be useful for businesses to track and analyze the performance of their locations, for researchers to study patterns in consumer behavior and for individuals to find and explore new places.

The purpose of this article is to provide a step-by-step guide on how to scrape Google Maps Reviews with our API using Node.js. We will cover everything from setting up the development environment to extracting relevant data and discussing potential issues. By the end of this article, you will have the knowledge and tools you need to scrape Google Maps place results on your own.

Why should you use a professional scraper instead of building yours?

Using a professional scraper can be a better option than creating your own for several reasons. Firstly, professional scrapers are built to handle a wide variety of scraping tasks and are optimized for performance, reliability, and scalability. They are designed to handle large amounts of data and can handle various types of websites and web technologies. This means that professional scrapers can often extract data faster and more accurately than a custom-built scraper.

Additionally, professional scrapers often come with built-in features such as CAPTCHA solving, IP rotation, and error handling, which can make the scraping process more efficient and less prone to errors. They also offer support and documentation which can be helpful when you face any issues.

Another important aspect is that professional scrapers providers are compliant with the scraping policies of the websites they scrape and they can provide legal use of the data, which is important to keep in mind when scraping data.

Finally, in our particular case, in order to scrape Google Maps Reviews, for best results, you need to pass a data_id parameter to your Google URL. This parameter usually looks something like this : 0x87c0ef253b04093f:0xafdfd6dc1d3a2b4e. I know this may sound intimidating at first as you may have no idea how to get the data_id property for a specific place and you are right, because Google hides this information and it is not visible on the page when you are searching for a place in Google Maps. But, fortunately, using a professional scraper like ours takes care of that by finding this data for you. We will talk in the later sections about how to get the data_id and how to scrape Google Maps reviews using our API.

Google Maps reviews are the ratings and comments left by users on Google Maps about a specific place. These reviews include information such as the user's name, the date the review was left, the rating given, and the review text.

Scraping Google Maps reviews can be useful for businesses who want to track and analyze the performance of their locations, researchers who want to study patterns in consumer behavior, and individuals who want to find and explore new places. By extracting the reviews data, businesses can identify the strengths and weaknesses of their locations, and make improvements accordingly. Researchers can study the sentiment of the reviews and find patterns in consumer behavior. Individuals can also use this information to make decisions about where to go and what to do.

Before beginning to scrape Google Maps reviews, it's important to have the necessary tools in place. The primary requirement is Node.js, a JavaScript runtime that enables the execution of JavaScript on the server-side, which can be downloaded from their official website. Additionally, an API KEY is required, which can be obtained by creating an account here and activating the SERP service.

After setting up Node.js and obtaining an API KEY, the next step is to create a Node.js script file. This can be done by running the following command:

And now paste the following line in your file:

If you see the message "Hello World!" displayed on the terminal, it indicates that Node.js has been successfully installed and you are ready to proceed to the final step. This final step is to obtain the Place ID of the place you are interested in scraping reviews. This is where our API comes in handy, it's easy to use and doesn't require any additional libraries to be installed.

Firstly, in a js file you need to import the Node.js `https` built in module in order to be able to send requests to our API. This can be done as following: 

Secondly, you need to specify your API key a search term and the coordinates of the place you are interested in: 

const API_KEY = "<YOUR-API-KEY-HERE>" // You can get by creating an account - https://app.webscrapingapi.com/register

const query = "Waldo%20Pizza"

const coords = "@38.99313451901278,-94.59368586441806"

Tip: This is how you get the coordinates for a place on Google Maps:

The next step is to include the obtained Place ID in an options object, to let our API know which location's reviews you want to scrape:

const options = {

  "method": "GET",

  "hostname": "serpapi.webscrapingapi.com",

  "port": null,

  "path": `/v1?engine=google_maps&api_key=${API_KEY}&type=search&q=${query}&ll=${coords}`,

  "headers": {}

};

Next, you now need to set up a call to our API with all this information:

Lastly, you can execute the script you have just created and wait for the results to be returned:

And you should get back the data_id property printed on the screen:

$ ​​0x87c0ef253b04093f:0xafdfd6dc1d3a2b4es

That concludes the setup process, with the data_id property, now you have all the necessary information to create a scraper for Google Maps reviews using our API using Node.js. 

With the environment set up, you are ready to begin scraping Google Maps Reviews with our API. To proceed, you need to set up the data parameter as previously mentioned. With all the necessary information available, you can set up the data_id parameter as follows:

const data_id = "0x87c0ef253b04093f:0xafdfd6dc1d3a2b4e" // the data_id we retrieved earlier

Now, the only thing left to do is to modify the options object, thus telling our API that you would like to scrape reviews from Google Maps: 

const options = {

  "method": "GET",

  "hostname": "serpapi.webscrapingapi.com",

  "port": null,

  "path": `/v1?engine=google_maps_reviews&api_key=${API_KEY}&data_id=${data_id}`, // there is no need in having a query anymore, data_id is enough to identify a place

  "headers": {}

};

And this is everything you need to do. Your script should now look like this: 

After executing this script, you should receive a response that appears similar to this: 

reviews: [

    {

      link: 'https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSUMyem9pOEdBEAE!2m1!1s0x0:0xafdfd6dc1d3a2b4e!3m1!1s2@1:CIHM0ogKEICAgIC2zoi8GA%7CCgwI1vuBkwYQiKeWyQE%7C?hl=en-US',

      date: '8 months ago',

      rating: 5,

      snippet: 'Wow, if you have dietary restrictions this is absolutely the place to go! Both for the variety of restrictions they cater to as well as the taste of the dishes.The good: great tasting food. Very conscious of dietary restrictions which include multiple types of vegan cheeses as well as gluten free. Decent drink selection.The meh: service is nice but a touch slow. Maybe understaffed? Prices are average for pizzas.The bad: noneFeatures: Did not see any masks on anyone inside. Unsure of cleaning practices so I cannot speak to that.Dine in: Yes\n' +

        'Takeout: Yes\n' +

        'Curbside pickup: YesWow, if you have dietary restrictions this is absolutely the place to go! Both for the variety of restrictions they cater to as well as the taste of the dishes. ...More',

      likes: 3,

      user: [Object],

      images: [Array]

    },

    {

      link: 'https://www.google.com/maps/reviews/data=!4m8!14m7!1m6!2m5!1sChZDSUhNMG9nS0VJQ0FnSURXOUxHSUl3EAE!2m1!1s0x0:0xafdfd6dc1d3a2b4e!3m1!1s2@1:CIHM0ogKEICAgIDW9LGIIw%7CCgwI3OnIkQYQwLGL1gM%7C?hl=en-US',

      date: '9 months ago',

      rating: 5,

      snippet: "We love Waldo Pizza! We have dairy allergies and Waldo offers a wide range of vegan cheeses as well as a ton of different toppings. The vegan dessert here is always excellent as well, super rich in flavor. Of course the traditional pizza, pasta and dessert are also amazing! It's great to have both options under one roof!Dine in: Yes\n" +

        'Outdoor seating: No ...More',

      likes: 1,

      user: [Object],

      images: [Array]

    }

    . . . 

]    

And that's it! You have successfully scraped Google Maps reviews using our API and you can now use the obtained data for various purposes such as data analysis, business analysis, machine learning and more. For further reference and code samples in other 6 programming languages, you can check out our Google Maps reviews documentation.

Even though using a professional scraper to extract Google Maps reviews can be more efficient and accurate than building your own scraper, there are still some limitations to keep in mind. One limitation is that some professional scrapers may have usage limits, which means that you can only scrape a certain number of reviews per day or per month. Another limitation is that some professional scrapers may not be able to bypass IP blocks or CAPTCHAs, which can make it difficult to extract large amounts of data without encountering errors. Luckily, at WebScrapingAPI we have residential proxies which rotate the IP addresses, thus getting you covered and eliminating the worry of being banned or rate limited. One thing you should keep in mind is that Google Maps reviews are usually in natural language, which can make them difficult to analyze and interpret without the use of natural language processing techniques.

In conclusion, scraping Google Maps reviews can be a valuable tool for businesses, researchers, and individuals. It allows you to gather data on a large scale and analyze it for various purposes. However, it's important to keep in mind that there are limitations to scraping Google Maps reviews, including usage limits, CAPTCHAs and IP blocks and natural language processing. Using a professional scraper can make the process more efficient and accurate and can get you rid of some of the limitations.Overall, scraping Google Maps reviews can provide useful information, but it's important to approach it with caution and care.

How to Web Scrape Reviews from Google Maps

Scraping this data from Google Maps can be useful for a variety of purposes. For example, businesses can use this data to track and analyze the performance of their locations, researchers can use it to study patterns in consumer behavior, and individuals can use it to find and explore new places.

The purpose of this article is to provide a step-by-step guide on how to scrape Google Maps place results with our API using Node.js. We will cover everything from setting up the development environment to extracting relevant data and describe potential issues. By the end of this article, you will have the knowledge and tools you need to scrape Google Maps place results on your own.

Finally, in our particular case, in order to scrape Google Maps place results, you need to pass a data parameter to your Google URL like this : 

https://www.google.com/maps/place/Waldo+Pizza/@43.821489,-43.4299553,3z/data=!4m5!3m4!1s0x87c0ef253b04093f:0xafdfd6dc1d3a2b4e!8m2!3d38.9930011!4d-94.5936805

The data parameter usually looks something like this: !4m5!3m4!1s + data_id + !8m2!3 + latitude + !4d + longitude. I know this may sound intimidating at first as you may have no idea how to get the data_id property for a specific place and you are right because Google hides this information and it is not visible on the page when you are searching for a place in Google Maps. But, fortunately, using a professional scraper like ours takes care of that by finding this data for you. We will talk in the later sections about how to get the data_id, coordinates, and how to easily build the data parameter.

Google Maps place results are the results that are displayed when a user searches for a place on Google Maps. These results can include places such as businesses, restaurants, hotels, landmarks, and other points of interest. Each place result includes information such as the place's name, address, phone number, website, reviews, and photos. Place results also include a Google Maps Street View image of the location, and a map showing the location of the place. The place results can also include a link to the place's Google My Business page.

When a user searches for a place on Google Maps, they are presented with a list of place results that match their search query. These results are displayed on the map and in a list format, and they can be filtered by various criteria such as rating, price, and distance.

Scraping these data can be useful for businesses who want to track and analyze the performance of their locations, researchers who want to study patterns in consumer behavior, and individuals who want to find and explore new places.

Before we can start scraping Google Maps place results, we need to make sure that we have the necessary tools available for us. The first thing we will need is Node.js, a JavaScript runtime that allows us to run JavaScript on the server-side which you can install from their official website. The 2nd thing you need to have is an API KEY, which you can get easily by creating an account here and activating SERP service. 

Once you have these things set up, in order to run a Node.js script, you just need to create a js file. You can achieve that by running the following command:

If you are now able to see the message “Hello World!” printed on the terminal it means you successfully installed Node.js and you are ready to proceed to the last step.

Now the last step is to get the data_id information about the place you are interested in. This is the time when our API comes into play. Using it is very straightforward and does not require any external libraries to be installed. 

Now, what you need to do is to pass this information in an options object so our API gets to understand which place you need to scrape information from: 

Finally, you just need to run the script you have built and wait for your results:

And that’s it, at this point you have everything you need to create the data parameter and you are ready to move to the next section which contains the purpose of this article. 

Let’s start scraping Google Place Results

Now that you have your environment set up, you are now ready to start scraping Google Maps place results with Node.js. As already mentioned above, in order to scrape Google Maps place results you now need to set up the data parameter. Having everything available by now, you can achieve that as following:

const data_id = "0x87c0ef253b04093f:0xafdfd6dc1d3a2b4e" // the data_id we retrieved earlier

const latitude = '38.99313451901278'

const longitude = '-94.59368586441806'

const data = '!4m5!3m4!1s' + data_id + '!8m2!3d' + latitude  + '!4d' + longitude

Now, you need to modify your options object to tell our API that you are looking for place results. Having this new data parameter, our API will know exactly what place you need to scrape information about:

const options = {

  "method": "GET",

  "hostname": "serpapi.webscrapingapi.com",

  "port": null,

  "path": `/v1?engine=google_maps&api_key=${API_KEY}&type=place&data=${data}`, // this time the type is place and there is no query needed

  "headers": {}

};

The resulting script should look like this : 

And after running this script, you should get back a response looking like this: 

place_results: {

    title: 'Waldo Pizza',

    data_id: '0x89c259a61c75684f:0x79d31adb123348d2',

    place_id: 'ChIJT2h1HKZZwokR0kgzEtsa03k',

    data_cid: '8778389626880739538',

    website: 'https://www.stumptowntogo.com/',

    gps_coordinates: { latitude: 38.99313451901278, longitude: -94.59368586 },

    reviews_link: 'https://serpapi.webscrapingapi.com/v1?engine=google_maps_reviews&data_id=0x89c259a61c75684f:0x79d31adb123348d2',

    place_id_search: 'https://serpapi.webscrapingapi.com/v1?engine=google_maps&type=place&device=desktop&data=!4m5!3m4!1s0x89c259a61c75684f:0x79d31adb123348d2!8m2!3d38.99313451901278!4d-94.59368586',

    thumbnail: 'https://lh5.googleusercontent.com/p/AF1QipNtnPBJ2Oi_C2YNamHTXyqU9I8mRBarCIvM5g5v=w408-h272-k-no',

    rating: 4.6,

    reviews: 2594,

    price: '$$',

    type: [ 'Pizza restaurant' ],

    service_options: { dine_in: true, curbsidepickup: true, no_contactdelivery: true },

    extensions: [

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object], [Object]

    ],

    open_state: 'Closed',

    hours: [

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object]

    ],

    contact_details: {

      address: [Object],

      action_1: [Object],

      menu: [Object],

      phone: [Object],

      plus_code: [Object]

    },

    address: '7433 Broadway St, Kansas City, MO 64114',

    images: [

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object], [Object],

      [Object], [Object]

    ],

    people_also_search_for: [ [Object], [Object], [Object] ],

    user_reviews: { summaries: [Array], most_relevant: [Array] },

    popular_times: { graph_results: [Object] }

  }

}

And that’s it. That means you have managed successfully to scrape the Google Maps place results using our API and you are ready now to use the data obtained for a lot of different purposes such as data analysis, business analysis, machine learning, and more. For further reference and code samples in other 6 programming languages for you to get started, feel free to explore our Google Maps docs.

Google Maps place results, although a powerful tool to find and explore places, have some limitations which you should keep in mind. Firstly, the amount of data available for each place is limited and some data like photos, Place ID, etc are only available through the Google Maps API and require an API key. Additionally, the data provided in the Google Maps place results can be dynamic and change over time, which means that the data you scrape may not be accurate or up-to-date. 

In conclusion, scraping Google Maps place results can be a useful tool for businesses, researchers, and individuals to find and explore new places. By following the steps outlined in this article, you should now have the knowledge and tools you need to scrape Google Maps place results using our API using Node.js.

How to Web Scrape Google Maps Place Results

Web scraping is a powerful tool that allows you to extract data from websites and use it for a variety of purposes, such as data mining, data analysis, and machine learning. One common task in web scraping is extracting data from HTML tables, which can be found on a variety of websites and are used to present data in a structured, tabular format. In this article, we will learn how to use Python to scrape data from HTML tables and store it in a format that is easy to work with and analyze.

By the end of this article, you will have the skills and knowledge to build your own web scraper that can extract data from HTML tables and use it for a variety of purposes. Whether you are a data scientist looking to gather data for your next project, a business owner looking to gather data for market research, or a developer looking to build your own web scraping tool, this article will provide a valuable resource for getting started with HTML table scraping using Python.

HTML tables are a type of element in HTML (Hypertext Markup Language) that is used to represent tabular data on a web page. An HTML table consists of rows and columns of cells, which can contain text, images, or other HTML elements. HTML tables are created using the table element, and are structured using the ‘<tr>’ (table row) ,‘<td>’ (table cell), ‘<th>’ (table header), ‘<caption>’ , ‘<col>’, ‘<colgroup>’, ‘<tbody>’ (table body), ‘<thead>’ (table head) and ‘<tfoot>’ (table foot) elements. Now let’s go through each one and get in more detail: 

th (table header) element: Defines a header cell in an HTML table. Header cells are displayed in bold and centered by default, and are used to label the rows or columns of the table.

For a better understanding of this concept, let’s see how a HTML table look like:

On the first look it seems like a normal table and we can not see structure with the above described elements. It does not mean they are not present, it means the browser already parsed that for us. In order to be able to see the HTML structure, you need to go one step deeper and use dev tools. You can do that by right-clicking on the page, click on inspect, click on select element tool and click on the element ( table in this case ) you want to see the HTML structure for. After following this steps, you should see something like this:

Before we can start scraping data from HTML tables, we need to set up our environment and make sure that we have all of the necessary tools and libraries installed. The first step is to make sure that you have Python installed on your computer. If you do not have Python installed, you can download it from the official Python website (https://www.python.org/) and follow the instructions to install it.

Next, we will need to install some libraries that will help us scrape data from HTML tables. Some of the most popular libraries for web scraping in Python are Beautiful Soup, Selenium and Scrapy. In this article the focus will be on using Beautiful Soup, since it is very straightforward compared to the other ones. Beautiful Soup is a library that makes it easy to parse HTML and XML documents, and is particularly useful for extracting data from web pages. While this is enough to scrape the HTML data we are looking for, it is not going to be too readable for the human eye in the HTML format, so you may want to parse the data somehow. This is the moment where Pandas library comes into play.

Pandas is a data analysis library that provides tools for working with structured data, such as HTML tables. You can install these libraries using the pip package manager, which is included with Python:

Once you have Python and the necessary libraries installed, you are ready to start scraping data from HTML tables. In the next section, we will walk through the steps of building a web scraper that can extract data from an HTML table and store it in a structured format.

Now that we have our environment set up and have a basic understanding of HTML tables, we can start building a web scraper to extract data from an HTML table. In this section, we will walk through the steps of building a simple scraper that can extract data from a table and store it in a structured format.

Andrei Ogiolan