fabiochiu/medium-articles huggingface dataset:
Rabbit Vs Kafka
By: Phani Susarla
Tags: Rabbitmq, Basics, Compare, Kafka

RabbitMQ and Kafka are the two popular technologies for asynchronous communication. Both technologies are fundamentally different. Kafka is newer and comes with lot more features when compared to Rabbit but at the same time is much more complex. Below are few details and comparison between these two technologies.

Rabbit

Messages are published to Exchange, which then distributes message copies to Queues using rules called Bindings.

When a message is delivered to a consumer the consumer sends message acknowledgment to the broker, which then deletes the message from the queue.

Exchanges and Queues can be either transient or durable. A durable one survives broker restarts whereas transient one does not.

Kafka

Zookeeper manages Kafka brokers. Consumers poll messages. Offset is a consumer’s pointer on the last message it polled.

Messages are stored in Topics. A topic can have one or more partitions. Partitions are for scalability. Replicas are backups for partitions.

Comparison

Producer has to publish messages to Kafka/Rabbit, there is no difference here. Consumer has to poll from Kafka. Rabbit pushes messages to the consumer.

Kafka is designed for high scale and is less resource intensive, Rabbit is not.

Kafka is durable, it keeps the messages for a set period of time. Rabbit deletes messages as soon as the consumer receives it.

Everything in Kafka is based on Topics, routing is simple. Rabbit supports Queues and Topics, it also supports complex routing.

By default, messages are not ordered in Kafka. Ordering is guaranteed in Rabbit.

Conclusion

This was a very high level comparison between these two technologies. Both are open sourced, popular and well supported. If you are weighing between them, pick the one that suits your use cases and beware of the scalability but operational complexity that comes with Kafka.

References
Craigslist scraping made simple
Tags: Data Science, Web Scraping, Python, Craigslist

Craigslist is an excellent medium to list and view classified advertisements in your local area. I’ve used Craigslist to buy and sell cars, find a 1-bedroom apartment, and much more.

Craigslist isn’t obscure — people from around the world use it for all sorts of listings: events, discussions, sales, jobs, etc. It’s an understatement to say that Craigslist holds a sea of information.

Craigslist is convenient because it’s split into regions. If you are looking for a Subaru Outback near Eugene, Oregon, you’d look in cars and trucks for sale in Eugene.

Big and organized data is a prelude to an exciting data science project. We can answer complex and simple questions: for example, what is the median price difference for a studio in San Francisco and Monterey?
Web Data Scraping and Scraper site API
By: Jennifer Dsoza
Tags: API, Web Scraping, Scraping, Web Crawler, Data Extraction

Hello there,

If you want to collect a lot of information from a website or scrap any data of product sites like amazon, ali express or look up for newsletters or compare prices on the Internet, then you may want to do this in a short time to save money. Using API can be network scraping, network data extraction or network collection, etc.

Generally, web crawling is divided into two parts: Get data by making HTTP requests

Extract significant data by parsing HTML DOM But first, what is an API? API (application programming interface) allows you to easily extract data from most web pages and get results in seconds. API is a better choice. API has many advantages, such as automation, efficiency, adaptability, etc. Every API service provides opportunities.

If you are a JavaScript developer, Node.js provides a variety of tools to directly obtain and parse data from the website. The puppet show is one of them. Puppeteer is a node library API that allows us to control Chrome without a header. However, in this article. I will not provide any information about manipulating up. There are various APIs today. If you manage agents, browsers, and verification codes, you should check out the Scraper site API. Using the API, you can complete web scraping in minutes. The process is simple and the functions are well thought out. This allows you to get HTML from any web page through a simple API call. Scraper site APIsupports Bash, Node, Python / Scrapy, PHP, Ruby and Java. It also has Javascript rendering functionality, POST / PUT requests, custom addresses, sessions, specific geographic locations, and proxy modes.

Scraper site API helps you to get data from any site or even you can set country IP location to extract data from a specific location. Like amazon shows different product details if a user visit from the US and different details if a user from China, so scrapersite.com helps you to set your region for product scraping.

OR if any site data show after page render and you want to scrap it through backend coding, so scrapersite helps you to get your achievement, scrapersite render any site for few seconds so all Javascript and other data shows on the screen, then it will return HTML response to you through its API.
An Insight about Web Scrapping.
By: Luis Alfonso Buelvas Betancourt
Tags: Programming, Web Scraping

You may not like it but a lot of information on the web is either static or dynamically generated as plain hypertext, this means you don’t always can use an API or a generally uniform way to get the data you need.

Learning web scrapping is cool and all, but using a library is dead easy, what is important is to understand that the web and a lot of places where you get data, is made up of recursive structures.

Yes indeed, I’m talking about trees and whatnot, you may be able to pip install that python library, but you will be clumsy and slow if you don’t understand how recursive structures work. Having a good grasp at the concept will help you implement good scrappers, and to actually get what you really want from a source and not what you can, and this is true for a lot of skills that are “easy” to learn in the sense of using ideas and algorithms that someone already made, but not so much when it comes to really getting what it is all about.

SO!

Do yourself a favor and don’t just make it work, because you can’t wait to be an expert to get things done, but don’t deceive yourself from your ignorance just because something works and go learn:
How To Scrape Data From Indeed (Job Title, Job Description, Location, etc.)
By: Hiren Patel
Tags: Indeed, Web Scraping, No Code, Data Mining, Jobs

Introduction:

Indeed is the leading job portal with more than 150 million job-seeking visitors per month. Indeed contains huge data of various companies that are hiring, demand and average salaries per city. All this data will help create a huge database and lead generation for your startup.

But, If you are wondering, how will you extract such huge data from Indeed website?

Don’t worry……

Using ProWebScraper, you can extract data within few clicks. you won’t require knowledge of coding. You can extract job details like job type and its description, position, location, etc.

In this tutorial, we will build an Indeed Job Scraper to scrape job data like title, company name, location, description, etc.

This Indeed Job Scraper will scrape the following fields, from the Indeed Job Detail Page:

Job title Company Name Job Location Job Description

Below is a screenshot of the data which we will be scrapping.
Statistical Modeling of Time Series Data Part 1: Data Preparation and Preprocessing
By: Yashveer Singh Sohi
Tags: Statistical Analysis, Web Scraping, Time Series Analysis, Machine Learning, Time Series Forecasting

Extracting Relevant Series

In this series of articles, the Close prices of the S&P 500 market index are analyzed. Here, we extract the series we’re interested in:

Extracting the required series from the collection provided by yfinance

Since the data is stock market data, we will not observe any value for the weekends. Thus, the time interval between 2 successive observations is 1 day even if the 2 observations are recorded on a Friday and then on a Monday. To convert our dates to follow the business days format (5 days a week), we use the asfreq() method of pandas with “b” as the argument.

Handling Missing Values

Next, let’s see whether this data has any missing values. This is an important preprocessing step, as the way we fill these values can have a huge impact on the tend.

Checking Null values, statistical description, and imputing the null values of the series

Output for the cleaning_spx.py code block

In the code cell above, data.spx.isnull().sum() takes the dataframe ( data ), extracts the column ( spx ) and applies the function isnull() to it. This results in a boolean array with True for every Null value encountered. The sum() function takes the sum of these boolean values. Since, True is represented as 1 and False as 0 , this gives the number of missing values in the spx series. The describe() function gives a few statistical measures of the series.

The number of null values ( 233 ) is clearly very small as compared to the count of observations ( 6459 ). Thus, a simple imputing function from pandas will be enough for this case. The fillna() function fills the missing values with the values encountered just before the missing value. This behavior is governed by the “ffill” (front fill) argument passed to the method argument. Click here for more information on other arguments for method .

Deriving S&P Returns and Volatility

Now that the data is cleaned, it can be used to build some other useful series that helps us to understand the market trends better. These are: Returns and Volatility.

Returns: The percent change in a stock price over a given amount of time. In this case, the returns over each day are calculated and stored in the column spx_ret .

Volatility: The volatility in a market index refers to the fluctuations in its returns. To gauge the fluctuations or stability in the market, sometimes the magnitude of returns or squared returns are chosen. In this series, the magnitude of returns is chosen as the measure of volatility. The Volatility of spx is stored in the column spx_vol

Thus, Returns are a measure of the gain (positive returns) or loss (negative returns) of a market index, and Volatility (magnitude of Returns) is the measure of stability in the index.
Web scraping made Easy — ScrapingAdda
Tags: Website Scraping Tool, Web Scraping, Web Crawling, Data Extraction, Web Crawling Services

Welcome to the ultimate platform ScrapingAdda, to get structured data from any website. On our platform, you can tell us about your web scraping, web automation or any web crawling requirement by posting your project freely without any extra freelancing cost and get your data in your required format.

Do you want to reach out to the right audience, do you want to expend your business?

ScrapingAdda can help you getting the right data and can provide you related information in to grow your business exponentially.
SCRAPING DYNAMIC WEBPAGES
Tags: Beautifulsoup, Python, Web Scraping

PREREQUISITES:

Before moving ahead, we need to make sure that we have imported the bs4 library onto the python environment we are working on. Moving ahead dynamic web pages are such that they scroll on and on without displaying the page number button which would actually be present on that page, in this article I have briefed about how we can handle this situation while extracting data.

What would be the problem with this?

When scraping data, we often tend to use URL to move from one page to the other and pull data from there, we then create a beautiful soup object of each page and extract the need text after parsing the information from the beautiful soup object, now as we do not have access to the URL with page number, we face trouble while accessing the page elements. Moving ahead..

Now turn off the internet connection of your pc and scroll down, now you can see the page buttons on the webpage, click on any one of these page buttons and see the URL change now you can loop through the URL just by changing the page number.

Pay attention to how the URL has changed, now it has a new attribute, page_number available.

Hope this article helped you!

For more such content follow Akash_freeflow on Medium!
Extracting selected data from a single page using lxml.html.xpath
Tags: Python, Web Scraping, Lxml, Web Scrapping Tips, Data Science

Fast and Simple ways to learn scrapping!

In this exercise, we will use XPath to collect information from the provided URL and use lxml. But, before we start it, please refer to the XPath and CSS selectors using DevTools. This article

Here is the little documentation about XPath expression:

Part 1

Part 2

Part 3

Part 4

We’re going to use this link for doing a “web scrapping”:

First, we have to install the module in our environment, if you are using macOS try to install through terminal, then we can import the module through IDE. It depends on you either using jupyter notebook, pycharm or VScode.

Step-1

A musicURL string object contains a link to the main page. musicURL is parsed using the parse() function, which results in the doc object which has lxml.etree.ElementTree object type.

Let’s get a base element through <article>

Get the <article> path

Make a new object as articles and put the <article> path into cell by using xpath() function and do not forget to add [0] at the last of code cause we’re just get the first area of <article> tags. The XPath for the articles posseses all of the fields that are available inside , such as title, price, availability, imageUrl, and starRating.

First <article> path

After this, we have to set up the individual expression. If we are going to get information about title, price, availability and imageURL, we should to declare the individual XPath expression such as:

Set up Individual XPath Expression

Check the result by printing each result object. There is still messy data on availability, imageUrl and starRating object. So let’s try to clean up the data

Checking

Cleaning Process

So, here is the final step, we are going to make a DataFrame after doing a “Web Scrapping” using lxml.html and XPath.

Make a list of number

DataFrame from Web Scrapping

This article is used to fast learning documentation about web-scrapping using lxml.html and XPath. Hope u enjoy it!
Extract Reddit comments with 2FA with Python
By: Rare Loot
Tags: Python, Web Scraping, NLP, Data Science, Reddit

Photo by Stéphane Paul on Unsplash

Having not used Reddit/PRAW for any Python projects in over 2 years, I recently came across a hiccup with extracting comments using Python’s Reddit API Wrapper — PRAW.

If you have 2FA enabled on your Reddit account then accessing the Reddit API for longer than an hour requires you to authenticate your access on a regular basis (every hour). This is clearly inconvenient if you are either running web apps or are accessing Reddit’s API for long sessions.

This issue will cause errors containing “invalid_grant” or “prawcore.OAuthException”. The instructions below should address this issue quite easily.

If you’re using PRAW for less than a hour

You only need to authenticate your account once if your session lasts less than an hour.This can be done manually by using the code below:

auth_code = input() #Input your Authenication code

auth_2 = str(your_reddit_password) + ‘:’ + str(auth_code) #You need to manually update this number every hour reddit = praw.Reddit

(

client_id=”SI8pN3DSbt0zor”,

client_secret=”xaxkj7HNh8kwg8e5t4m6KvSrbTI”,

password=auth_2,

user_agent=”testscript by u/fakebot3",

username=”fakebot3"

)

The code above, let’s you update your 2FA code (from the authenticator app on your smartphone) via input at the beginning of your code before running the authentication process.

With your authorised reddit code, you can now perform PRAW functions using your reddit api credentials for the next hour.

If you intend to access PRAW for longer than hour (or are using a live web app)

Then you need a refresh token.This is very easy to get, just follow these steps:

Go to the PRAW Refresh Token Page

Copy and paste this code into an IDLE or Notebook of your choice.

Have your full Reddit Dev Credentials and API keys at hand

Run the refresh code and enter your details accordingly

Your Web Browser will open a new tab in which you will select “Allow”

In response, your web browser will then produce your Refresh Token

Save your Refresh Token to a document or somewhere safe. You will need this later.

With your Refresh Token you can include it in your code which will grant you continuous authentication. See the example below:

reddit = praw.Reddit

(

client_id=”SI8pN3DSbt0zor”,

client_secret=”xaxkj7HNh8kwg8e5t4m6KvSrbTI”,

password=auth_2,

refresh_token=”WeheY7PwgeCZj4S3QgUcLhKE5S2s4eAYdxM”,

user_agent=”testscript by u/fakebot3"

)

print(reddit.auth.scopes())

For more information on 2FA and Reddit API Access

Disclaimer: Below contains an affiliate link in which I will earn commission if you were to invest in the product or services provided. Hopefully you find value in it.
Scraping the Amazon.
By: Bilal Khan
Tags: Amazon, Tutorial, Python, Web Scraping

Python is so powerful!

This is the second article of my web scraping guide. In the first article,I showed how to use BeautifulSoup & Requests to perform a quick and effective web-scraping for TSLA Stock Price Alert, an email was automatically generated to notify the price drop.

Scraping the single pages using beautiful soup is easy but what if you want to scrap the multiple pages at a time or want to automate this process? Yes, it is possible to do it manually but this way is too time-consuming, not scalable, and highly not recommended.

Image Credits: Real Python

In this post, we’ll learn how to use BeautifulSoup & Requests to perform a quick and effective techniques to scrap the product titles from multiple pages at Amazon. Let’s dive in.

Complete Code.

1-Import The Necessary Libraries.

2-Loading Webpages with request.

The requests module helps to send HTTP requests in Python. So the next step is to inspect the required data classes from the complete source code. We select the card and click on the ‘Inspect Element’ option to get the source code of that particular section. You will get something similar to this:

Inspecting the titles.

Now we need to write a function that could scrap the title and price from the webpage.

Get the contents of the following URL using the requests module: https://www.amazon.com/s?k=macbook&ref=nb_sb_noss_2

module: Define the headers.

Find the title using class the name inspected from the webpage.

Up till now, we have scraped the titles of the first page. Now we want to scrap the titles from the other pages.

If we inspect the pagination box at the bottom of the page, we can see the href for the second and third page.
Web Scraping Example in Python using BeautifulSoup
By: İsmail Gök
Tags: Beautifulsoup, Web Scrapers, Python, Web Scraping

After that basic introduction, we can move on to the coding example. This small application is developed in Python using BeautifulSoup library as the web scraping library.

First thing first. We need to import relevant libraries.

Import BeautifulSoup in order to use the famous web scrapper library. Import urllib.request in order to open up HTTP requests to specific URLs. More detailed explanation: https://docs.python.org/3/library/urllib.request.html.

Further, import re (used for regular expressions) or any other module if you need to parse data from URLs.

After the imports, the first thing to do is to get the main URL and open a connection to that URL. I wanted to store the data I got from the connection in a text file, so I open a file connection as well.

We have done no scrapping at this moment, only opened up a connection to a URL, read all the HTML data and closed the connection. Now, it is time to parse the HTML data and scrape some meaningful information out of them.

The line of code above is very important in my experience. Most of the time, BeautifulSoup can figure out a site’s character encoding correctly, but sometimes it is not the case. Sometimes it cannot get the correct encoding and that caused me a lot of headaches. So, to be perfectly safe, I tell BeautifulSoup how he need to parse the HTML data from the URL with the code above. In most of the cases, the character encoding is UTF-8 and the data is in HTML format.

There are a bunch of different ways to identify your target html element like element tag name, id, class name, even with styles. I used some of these ways to get the data I need. I wanted to get all the rows from a specific table and I accessed the table using its element id. After that I accessed its ‘tbody’ using only the element name and got all the rows utilizing their ‘tr’ tags.

All the rows had URLs that link to website’s subdomain addresses. My main target was to traverse all the subdomains and get the desired data from the main tables in those subdomains.

It is not always viable but here I could get the desired table rows by distinguishing them by their styles. I wanted to get all the table rows (‘tr’)if their ‘style’ attributes not equals to ‘height: 46px’ (which was the height of table header). Then I proceeded to getting values from each cell in desired table rows. Using row.findAll(‘td’) I have got all the cell in an array. After that, I accessed the ‘a’ tag and then the ‘href’ tag of it and store them in an array called allURLs.

Now, I have got all the URLs of the subdomains. It was time to iterate over all the URLs and access the desired table data.

After accessing all the ‘td’s in the table, I disregarded a specific column by checking its text value. The rest is applying some operations just to extract the meaningful data from the data I scraped. Lastly I closed the file reader and printed out a basic information message.
Web Scraping and Login using Python Selenium
By: Rahman Taufik
Tags: News, Web Scraping, Selenium, Login, Data Science

Have a web scraping problem when website must be logged in first?

Well, we can use Selenium for that problem. Basically, selenium is used for automated testing web validation, but it can also be used for scraping, because it can be controlled automatically by scripts, easily work with javascript, DOM or complex html tags

For example, we try to scrap news from websites that need to logged first, such as www.wsj.com or www.barrons.com

The first thing we do is install libraries, including selenium python library, webdriver manager library and import several selenium functions in your file

The Libraries

Create your function/class for login, the codes include:

put the url

set the web driver options (e.g. windows size, headless, etc.) and

login with your username and password

Login to Website through Selenium

After successful login, we can continue the code to get the news. We can choose the information what we need (e.g. title, article, date, etc) and store it to csv
COVID-19: Medios a Contramano de la Pandemia — Parte III: Importancia
By: Juan Manuel Barriola
Tags: Web Scraping, NLP, Data Visualization, Covid 19, Data Science

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
¿Cómo ganar dinero usando el Web Scraping? ¡Sin vender datos!
Tags: Selenium, Python, Web Scraping

Web Scraping Basics — Scraping a Betting Site in 10 Minutes

In this 10-minute tutorial, I’ll show you how to scrape websites with Python even if you don’t code at all!
Website Crawling for laptop price and spec via Selenium — part 1 (Amazon)
Tags: Amazon, Python, Web Scraping, Selenium, Python Programming

Purpose

We are trying to search and grab the laptop information in the Amazon website by rendering through the browser via Selenium so that we can easily play around with the data. We originally tried to leverage Scrapy but it does not allow us to go this way.

Tool

Python package: selenium/ BeautifulSoup/random/ time/ os

Target Website

Steps

Set up the environment. Download the chromedriver driven by selenium to execute the browser rendering. Go through the structure of the website to locate where the target elements are.

When you inspect the website, you can pick price of an example and look it up across the source codes. You will find the information located in the tag of <data-component-type=”s-search-results”>.

Let’s search for the detailed info like model#, item description, price, etc.

a. Model#: Take an example and search on where it resides. It looks like it is located in the tag of “data-asin”

b. SKU description: It is located under the text where “class” equals “a-size-medium a-color-base a-text-normal”.

c. price: It is located under the text where “class” equals “a-offscreen”.

3. Py scripts

a. Define the csv file you would like to write in.

os.chdir('C:/Users/scrap/amazon')

outfile = open('sample.csv','w')

b. For price/ item description, replace “,” with “” so that it won’t create a additional columns when writing data into csv file.

price = price.replace(",", "")

item = item.replace(",", " ")

c. Find the element handling page change.

driver.find_element_by_class_name("a-last").click()

Full py codes

from selenium import webdriver

from selenium.webdriver.support.ui import WebDriverWait

from bs4 import BeautifulSoup

import os

import time

from random import randint



os.chdir('C:/Users/scrap/amazon')

outfile = open('sample.csv','w')



driver = webdriver.Chrome('./chromedriver')

driver.implicitly_wait(30)



driver.get('https://www.amazon.com/s?k=laptop&i=computers&rh=n%3A13896617011%2Cn%3A13896615011&dc&page=138&qid=1606316331&rnid=2941120011&ref=sr_pg_1')

time.sleep(randint(10, 15))



WebDriverWait(driver, 3)



for y in range(400):

driver.get(driver.current_url)

soup = BeautifulSoup(driver.page_source, 'html.parser')

time.sleep(randint(8, 15))

for tag in soup.find_all(attrs = {"data-component-type": "s-search-result"}):



try:

model = tag['data-asin']

price = tag.find(attrs={"class": "a-offscreen"}).text

price = price.replace(",", "")

item = tag.find(attrs={"class": "a-size-medium a-color-base a-text-normal"}).text

item = item.replace(",", " ")

outfile.write('%s,%s,%s

' % (model, price, item))

except:

model = ''

price = '0'

item = ''





driver.find_element_by_class_name("a-last").click()



driver.quit()

outfile.close()

Thank you! Enjoy it:)
How I Made A YouTube Bot ?
By: Pradip Kharal
Tags: Paratube, Web Scraping, Youtube Bot, Automation, Bot

Well, let’s start with a bot. A bot is a software application designed to do automation task without help of human. So typically they perform repetitive task.

As technology grow rapidly, large amount of data are being created every seconds and managing these data is one challenge but securing data is the most challenging and every time when you post something there might be someone or something like automated bot use and manipulate your data.

For Research purpose and to demonstrate this topic i have created one simple system called ParaTube . You can watch demo videos on YouTube

System Diagram of ParaTube

Web Scrapper

Web Scrapper (web scraping ) also known as web harvesting is data scrapping used for extracting data from web. Here i have used web scrapper for scrapping Instagram (source of video content) and YouTube (source of audio) . Basically i scrap Instagram for content in image/video format and YouTube for audio and later by editing image/video and mixing it with scrapped audio i have created new video content.

2. Database

Main purpose of database in this system is to minimize repentance of content. By filtering previous content with the help of database i am able to minimize duplicate content.

3. Scheduler

Scheduler is used for to regulate task in certain time interval.

4. Google API

Google API is used to authenticate and to upload created video to YouTube.

5. Mail

Finally When the video is uploaded to YouTube, The completion of task is notify by mail .

Nowadays web scrapping is widely used . Artificial Intelligence is one of the emerging technologies and we need to feed large amount of data for AI based projects. For example : AI model of object detection to sentiment analysis social site’s data might be used. Deepfake is another AI based technology that can entirely modify your data and can create vulnerable content.
Web Scraper — Scrape Amazon book store without code
By: O K.
Tags: Journalism, Tutorial, Web Scraping, No Code, Amazon Book Publishing

One journalist asked me how to scrape a book list from a search result of Amazon book store. They are not very familiar with coding. This time, I think it is a high time to explore some existing tools that could help journalism. The outcome is not bad, the journalist told me that now they have unlocked the skill sets and they can do a lot more using the tool — Web Scraper. More data-drive stories from them can be expected :)

Here are the steps to show how to do it. (This shows only the steps of importing pre-defined scrapers. For create new scraper, may visit its official tutorial video clip: https://www.youtube.com/watch?v=n7fob_XVsbY )

Step 0: Install Web Scraper Chrome Extension in Google Chrome

Open Google Chrome

Go to https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn and install the extension.

Install this extension to your Chrome browser

Step 1: Open the Web Scrape panel

Open a new browser tab

Press “F12” to open the inspector

There is a tab “Web Scraper” at the top of the inspector panel, usually is located at the last item (the most right one)

Step 2: Import “Sitemap”

“Sitemap” here means a scraper.

Click “Create new sitemap” at the second row of the Inspector

Click “Import Sitemap”

Step 3: Get a pre-defined JSON files and import them

Open the one for scraping listing JSON, paste the content into the text area blank under “Sitemap JSON”

Fill the “Sitemap name” (Remarks: recommend to fill a distinct name, all sitemap names must be different)

Click “Import Sitemap”

Notes: There will be a warning message if the JSON is not valid. (e.g.: Does not copied completely)

Paste the content of JSON file into the text box.

Should be showing green color in the border of the text boxes.

This JSON file is a valid one! It is going to scrape the book title, author and published date.

Step 4: Ready to start scraping

If import is successful, you will see this.

Selector may need to be changed as the HTML of the Web page changes. But it can be done by clicking on browser.

Click Sitemap <your name filled as sitemap name>

Click “Scrape” to process

Scrape!Scrape!Scrape!

There are some options for scraping, use the default value should be fine. (Remarks: Amazon website may sometime issue Robot checking, when the scraping to too frequent.)

Click “Start scraping” to start the scraping process.

Use the default value is fine.

It will pop up a new browser window, and do the scraping automatically.

The computer is working for you and helps you to save your time.

After looping all the pages, the window will be closed automatically.

Step 5: Download the data as CSV file

Click “Export data as CSV”

Click “Download now!”

It will start downloading:

Done the scarper~
Tutorial: Web Scraping de Tablas HTML desde Wikipedia (o Cualquier Página Web)
By: Enrique Antonio Mosqueira Rubio
Tags: Python, Web Scraping, Programming, Data Science, Pandas

Hello World 👋🌎 Welcome to my thoughts and projects on Data Science and Machine Learning I’ve been documenting through blogging ✍️. Let’s learn together. 💪💯
How To Scrape Walmart Data: Price, Title, SKU, Description [For Free]
By: Hiren Patel
Tags: Web Scraping Tools, Web Scraping Service, Web Scraping, Scraping, Walmart

Introduction:

Nowadays people are using internet services and from the last few years, people are diverted to e-commerce. E-commerce is a business model that allows a person to buy and sell things over the internet. When you don’t have time for shopping and you want to do shopping, the best thing is online platforms. E-commerce is the best business model for someone who wants to start a new business. But for that, there are some questions one has to face.

Which e-commerce business is best for newcomers?

What are the techniques? Which will attract more customers?

How to survive with your business model concerning competitor business model, which is already doing well in the market?

If you have the same questions then there is one option which is called web scraping. This will help in the identification of the consumer’s choices and preferences. The process of web scraping includes the use of automated crawlers across visible content of different e-commerce platforms. Automated web scraping for e-commerce website portals generates information effortlessly. So newcomers can use this tool for their own benefit.

To help you learn the process of web scraping, we have come up with an easy tutorial on how to scrape different fields of the Walmart eCommerce website.

In this tutorial, we will build a Walmart Scraper to scrape product data like title, price, image, condition, etc. We’ll build this web scraper using ProWebScraper.

This Walmart Scraper will scrape the following fields, from the Walmart Detail Page:

Brand Title Model No. Price

Below is a screenshot of the data which we will be scrapping.
Automate Your Life, Using Web Scraping
By: Christopher Zita
Tags: Python, Web Scraping, Data Scienc, HTML, Web Development

Automate Your Life, Using Web Scraping

Photo by Nicole Wolf from Unslpash

Did you know what your reading right now is data? It may just seem like a few words to you but on the back end, everything you read online is data that can be taken, picked apart, and manipulated with. Simplified this is what a Web Scraper is. They go through the code that was created to make a website (HTML code) and take the data they want. Web Scrapers also interact with webpages using code to click buttons, send text to input boxes, and self-scroll.

If you didn’t know what a Web Scraper is, well now you have an idea and we can get to the point of why your reading this article. Time. Time is everyone’s most valuable asset, and often people spend that asset doing something they don’t enjoy.

But what if you didn’t have to. What if you could let your computer take care of all the stuff you didn’t want to do yourself.

You see Web Scraping is a unique skill that can help automate your life that isn’t as difficult as it sounds. In fact, all the examples I’m going to show you took less than 50 lines of code to make, can be learned in only a couple of hours and has saved me time to do the things I truly enjoy. So with that said let me show you…

3 ways to Automate Your Life, Using Web Scraping

1. Save Time Shopping

Shopping can be extremely time consuming if you don’t know what you’re looking for, or are trying to find the best deal for a specific product. Instead of spending hours looking through websites to find the best price on clothing, a hotel, or even a car, let a computer do it in mere minutes.

You’re probably asking how is this possible. Well by using Web Scraping we can actually gather all the data on the entire site and put it into a nice clean table where you can filter based off price, reviews, or the product in general.

To demonstrate this I’m going to scrape the details of all the products on the Nike site, and put the data into an organized excel spreadsheet.
Canım “Web Scraping” ❤
By: Cansu Altunbas
Tags: Web Scraping, Yazılım, Veri Analizi, Türkçe, Kodlama

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Web Scraping All the Links With Python
By: Python Is Rad
Tags: Python, Web Scraping, Links, Automation, Beautifulsoup

Recently I wanted to get all the links in an archive of newsletters. The goal was to have a text file with the links so that I didn’t have to manually go through each newsletter.

This is a real world example so I wanted it to be fast and easy. This is how I actually did it with python.

The best web scraping package for python in BeautifulSoup. And the best package for making url calls is Requests.

pip install beautifulsoup4, requests

I did all the python work in a jupyter notebook. I tend to use jupyter if I am experimenting in python or making a quick script. But you can certainly do the same thing in a .py file or a code editor.

Import the packages

from bs4 import BeautifulSoup

import requests

Get a list of pages in the newsletter archive

Get the html from the newsletter archive page. I picked a random archive page from the internet just for demonstration purposes.

URL = "https://www.comnetwork.org/newsletter-archive/"

page = requests.get(URL) soup = BeautifulSoup(page.content, "html.parser")

for a_href in soup.find_all("a", href=True):

print(a_href["href"])

Since there are going to be more than just newsletter links in the newsletter-archive, I just printed them all out and pasted them into vim(but you can use any text editor). Then I deleted anything that didn’t look like a newsletter link.

There are better ways of doing this. You could instead append to a list in python if you knew what the newsletter links started with. But at this point I figured it would be faster to do the work manually.

I noticed that all the newsletter pages started with mailchi.mp.

So I deleted everything else and added quotes around the links and a comma after the links.

When I was done I pasted the links back into python.

Now I had a list of the newsletter links. Here is a sample:

Get all the links from all the pages

The last step is to get all the links on all of those newsletter pages and save them to a text file.

for link in archive_links:

page = requests.get(link)

soup = BeautifulSoup(page.content, "html.parser") for a_href in soup.find_all("a", href=True):

with open("newsletter_links.txt", "a") as linkfile:

linkfile.write(a_href["href"]+"

")

Use a for loop to go through the list. Get the html from each page. Find all the links. And write them to a text file.

Notice that I use with open to write the text file. The with is recommended because it automatically closes the file when it is done. There is an “a” in the open because I am appending each link and not overwriting. The “

” is the new line character. It puts each link on a separate line.

At this point newsletter_links.txt if filled with all the links from all the newsletters, including stuff that I didn’t need. So I went into the text file. Sorted the links. And deleted anything that I didn’t want.

You can check out a full video walkthrough of my process here:
A STEP BY STEP GUIDE ON HOW TO DETECT SENTIMENT FOR HOTEL REVIEWS USING AMAZON COMPREHEND
By: Mariam Bazzi
Tags: Web Scraping, Tripadvisor, Amazon Web Services, R, Sentiment Analysis

Are you a hotel manager, and you want to know what guests are saying to improve your services?

Or you are traveling somewhere, and you want to compare several hotels that you liked before?

Photo by Daniel Corneschi on Unsplash

Guest reviews are an essential factor that will help you answer the questions above, whether you are a hotel manager who desires that his guests get satisfied. Or a guest who wants to have the best experience in Barcelona, reviews are the primary tool you need. Since guest reviews is a huge factor in choosing a hotel, you want to read the reviews to make the best decision.

For you, the hotel manager, to improve your services, you want to know if your guests are giving positive or negative reviews.

But, how can you get the reviews you need for any of the reasons stated above? We could do sentiment analysis for each hotel’s reviews to achieve this. There are various ways to go about this. However, this article will walk you through how to do this in R using one of Amazon Web Services (AWS) machine learning tools.

All the codes demonstrated in this article can be found on GitHub.

Let’s jump right in!
How I scraped a dynamic website with Selenium
Tags: Selenium Webdriver, Web Scraping

I have used the Beautiful soup python module to scrape sites in the past. When I had to scrape this dynamic site. I thought how difficult can it be, then I saw this site ,everything I planned went out the window. Mike Tyson famously said “We all have plans until, we get the first punch”, he is so right.

The site had the information that I wanted scattered in the different pages. I had to find a scraping module that can scrape and automate the browser simultaneously. A quick Google search showed Selenium to be the top contender amongst the scraping modules. So I went on to Youtube to learn more of what is module can do. After a watching a few videos, I was finally ready to give the module a go. I followed the installation procedure for a user running Anaconda on Linux Mint, which can be found here.

I powered up Jupyter Notebook and imported all the python modules needed for the job.

As shown above, I imported selenium,pandas, regular expression and time. From the selenium module, I imported the webdriver module. I have this habit of checking the directory of new modules I import, it helps know what the module is all about. dir(module) is the code to do it in python .

A vital requirement of the process is pointing selenium to the web browser’s driver . Web browsers supported by selenium can be found here. These browsers all have drivers that can be downloaded from their websites, a quick google search will show one where to get these drivers. An executable path is then added to point selenium to the location of the driver as shown in the second line of the code diagram above.

In my view the work-horse of selenium module is find_elements_by_xpath . From code diagram above, this function was used multiple times to scrape data from different html elements.

The ease of getting to the data to be scraped in a Chrome-based browser can be shown in a two step process as shown below:

Navigate to area that contains the data to be scraped and right click to get to the “Inspect” button.

2. From the element box that pops up, navigate to data and right-click again to get to the “Copy” button, which takes you to an array of what you can copy.

A loop is needed to move from one page to next whilst scraping , much like the one shown below

The name variable stores data in an object from step 2 above using browser.find_elements_by_xpath. Another loop is then used to get to the text component of the object. This text component is the information that is needed.

Some of the data to be scraped was hidden behind a link and a form with a radio button that has to be selected to reveal the data. I got to it with the code below:

The browser.find_element_by_css_selector function was used to twice to locate the link and click it , then to locate the radio button and select it. A 3 second delay was needed to move from the link to the radio button, this was achieved using time function as shown above. The get_phone function was then called from another function as shown below.

Watching the pages change was a sight to behold. Please see below.
Web Scraping Using Python and Plotting the Data on a Map
By: Rohan Dogra
Tags: Web Scraping, Beautifulsoup, Folium, Data Visualization, Data Science

Photo by Ilya Pavlov on Unsplash

Finding an apartment in Melbourne and its suburbs can be a very challenging task if you are new to the city. I faced the same problem when I moved to Melbourne a year ago. The most common practice that people follow is to search online for an apartment. As I was searching online for a house, the thought of web scraping just came to my mind. So I decided to do 2 things :

1) Web scrape and store data regarding available properties in and around Melbourne.

2) Plot the data on a geographic map of Melbourne to get a better view of the available properties

The website that we are going to use to scrape data is Gumtree. It is like an online marketplace where people can buy and sell just about anything.
How to Order Pizza, Using Code (2021)
By: Christopher Zita
Tags: Python, Web Scraping, Data Science, HTML, Web Development

First of all I know what you’re thinking, why create a program that orders pizza for you when you can just do it on the phone or the internet. Well sir or madam… you are absolutely correct! It’s just I’m not a good enough programmer yet to code a robot to brush my teeth or comb my hair, so ordering pizza is the best I can do. And if you’re like me and want to make the process of ordering pizza even easier, then let’s learn how to order a pizza using code.

The place the pizza will be ordered is from Pizza Nova, because it’s my favorite pizza chain (please do not attack me in the comments for this), and we’re going to code it using Web Scraping in Python.

What is Web Scraping

Did you know what your reading right now is data. It may just seem like a few words to you but on the back end everything you read online is data that can be taken, picked apart, and manipulated with. Simplified this is what Web Scraping is. Web Scrapers go through the code that was created to make a website (HTML code) and take the data they want. Using Web Scraping we can also interact with webpages to click buttons, send text to input boxes, and self-scroll. This is exactly how we will use Web Scraping to go through Pizza Nova’s website and order a custom made pizza.

Ordering The Pizza

In steps I’m going to show you how I navigate the website through code to order a custom made pizza for delivery.

Each step will have a short video at the end to display what the code is doing on the site.

1. Opening The Pizza Nova Website

When you first open the website a popup opens with an input box that asks for your city. To input my city using code I need to find the location of this input box on the website and then send text to it.

Everything on any given website can be located through a selector called Xpath. So my code will find the Xpath of the input box, and then sends the text I want into it which is my city Mississauga.

#Finds the exact location of the input box

city_input_box = driver.find_element_by_xpath('/html/body/div[8]/div/div/div/div/form[2]/fieldset[1]/input') #Sends the text Mississauga (my city) into the input box

city_input_box.send_keys('Mississauga')

Next we want to write some code that clicks on the create your own tab and selects pizza. The process is basically the same as above, as we will find the location of the create your own tab using Xpath, and use a function called click(), that will click on the tab and select pizza.



create_your_own = driver.find_element_by_xpath('//*[ #Clicks on the create your own tabcreate_your_own = driver.find_element_by_xpath('//*[ @id ="menu-item-35"]').click()

pizza = driver.find_element_by_xpath('//*[ #Selects pizzapizza = driver.find_element_by_xpath('//*[ @id ="pizza-builder-selector"]/div/div[1]').click()

The video below displays exactly what our code does in this step. Also take note of how fast our code performs each action on the site.