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1. #MAKING A WEBSCRAPER USING AWS INSTALL#
2. #MAKING A WEBSCRAPER USING AWS ARCHIVE#
3. #MAKING A WEBSCRAPER USING AWS CODE#

Next line of code will create S3 bucket as follows − Now for storing data to S3 bucket, we need to create S3 client as follows − Step 3 − Next, we can use the following Python script for scraping data from web page and saving it to AWS S3 bucket.įirst, we need to import Python libraries for scraping, here we are working with requests, and boto3 saving data to S3 bucket.ĭata = requests.get("Enter the URL").text Installed with the help of the following command −

#MAKING A WEBSCRAPER USING AWS INSTALL#

Step 2 − Next, we need to install boto3 Python library for accessing S3 bucket. It will create a S3 bucket in which we can store our data. Step 1 − First we need an AWS account which will provide us the secret keys for using in our Python script while storing the data. We can follow the following steps for storing data in AWS S3 − Basically AWS S3 is an object storage which is built to store and retrieve any amount of data from anywhere. But what if the we need to store and analyze this data at a massive scale? The answer is cloud storage service named Amazon S3 or AWS S3 (Simple Storage Service).

#MAKING A WEBSCRAPER USING AWS ARCHIVE#

Sometimes we may want to save scraped data in our local storage for archive purpose. title of the webpage will be saved in the above mentioned text file on your local machine. With open('JSONFile.txt', 'wt') as outfile:Īfter running this script, the grabbed information i.e. The following is an easy to understand Python script for doing the same in which we are grabbing the same information as we did in last Python script, but this time the grabbed information is saved in JSONfile.txt by using JSON Python module. Similarly, we can save the collected information in a JSON file.

Now, with the help of next lines of code, we will write the grabbed data into a CSV file named dataprocessing.csv.į = csv.writer(open(' dataprocessing.csv ','w'))Īfter running this script, the textual information or the title of the webpage will be saved in the above mentioned CSV file on your local machine. Now, we need to create a Soup object as follows − In this following line of code, we use requests to make a GET HTTP requests for the url:

Let us first understand through a simple example in which we will first grab the information using BeautifulSoup module, as did earlier, and then by using Python CSV module we will write that textual information into CSV file.įirst, we need to import the necessary Python libraries as follows − CSV and JSON Data Processingįirst, we are going to write the information, after grabbing from web page, into a CSV file or a spreadsheet. To process the data that has been scraped, we must store the data on our local machine in a particular format like spreadsheet (CSV), JSON or sometimes in databases like MySQL. In this chapter, let us look into various techniques to process the data that has been scraped. In earlier chapters, we learned about extracting the data from web pages or web scraping by various Python modules.