Data Structure: Crawler with Selenium

Posted on Jan 13, 2021
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


The crawler is comprised of several different components to make the unstructured data accessible for cleaning. As the data we are looking to scrap here is financial in nature, we take on several webpages to comprise and give the data structure. First, the structure is given with two feats, its endpoints or URLs and the writing of features for further analysis. Both processes we will describe in full to explain with further visualizations.


The first set of data takes on a relative amount of information pertaining to each URL from a home's designated webpage. These URLs are scrapped and written to commas separated valued that can be read in by a separate function. Through Google Chrome's driver the initial arguments were passed through a list. While these strings are not the complete endpoints, we reformat each in the designated list to iterate through the various needed websites.

Considering the site has JavaScript embedded into at least its user interface (UI) then Selenium was the chosen tool after attempting with Beautiful Soup and the less cumbersome tools. The file must run through a shell to test an XPath's endpoint. Other, or sometimes older, programming interface can be seen to work depending on how a website has been built. Regardless, trail and error must be done and printing checks were made for both exception and county pages. To describe further, each button at the end of each page was used to retrieve a detailed more detailed endpoint. This endpoint was the feature that is to be read in the following script.

1. Endpointย -

2. Endpoint -


As each home's endpoint was retrieved, its main scrapping occurred through the ' '. The divisions on the page that were taken off the HTML are from Town, County, MLSID, Square feet, Table_Appreciation, Bath, Beds, Table_Event, Home Price, Table_Date, Land Value, Table _Source, Table_Price, Tax Year, Tax Amount, Tax Additional, and Tax Total. While these columns have been made and also cleaned of frivolous characters, each value has went through some level of a preprocessing. This cleaning has mainly taken place through regular expressions.

The structure of the file is rather simple but each predictor or independent feature can be quite cumbersome. Through each expression the files must be reran in order to see if its output removed character strings or formatted the numerical values. It is also not short of if the file is ran enough times, the bot will be flagged for security parameters.


Further analysis and exploration should done through this information. Prior to this, feature transformation must be done for null values and other potential bugs that may arise. This and other mapping may also be done has the exploration occurs. It is a powerful tool that if advised may revolve more or around other scrapping tools.


Github:ย Scrapper


About Author

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI