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tennis racquet data mining - rahul كلمات الأغنية

tennis racquet data mining

methodology introduction

the information’s introduction

the first stage in any data science endeavour is data collection. though data repositories abound, using the internet as a database is more convenient. this is normally the job of a software developer, but you may find yourself performing it in a smaller organisation. let’s get this party started!

information transformation service provides web scr_ping services that provides high_quality structured data to improve business outcomes and enable intelligent decision making,their web scr_ping service allows you to scr_pe data from any websites and transfer web pages into an easy_to_use format such as excel, csv, json and many others

2. specifics

because i play tennis, the information comes from tennis_xpress.com. this little dataset contains 213 instances and 15 characteristics

i usually give credit where credit is due to useful sites, and this time is no exception: it’s available at https://www.youtube.com/watch?v=mebu_4xs2ru. if i wasn’t clear, please watch the video

in essence, we’re writing three functions: request(), p_rs_(), and output() (). the purpose of these functions is self_evident: bind, read, and save are only a few examples

we’ll be using the “requests html” library, which is available at https://pypi.org/project/requests_html/

. one of the most significant benefits of using this library is its ability to render dynamically loaded content, which can be found on practically any modern webpage. you’re probably wondering what dynamically loaded material is. the details are displayed by the client, not the server. so, what exactly is the point? the short answer is that you may be unable to find the information you require. the long answer is that when i use the library “beatifulsoup” to request data from the server, it will not return data rendered by javascript or ajax (aka dynamically loaded contents) because i am requesting data from the server, which does not render dynamically loaded contents, but the client does. don’t get too worked up; first, check to see if the data you require is dynamically loaded by _n_lysing the element in your browser for a script tag that matches javascript or ajax. sorry, but if you observe the highlighted script tag inside a larger tag that also wraps the information you require, your data is dynamically loaded. the good news is that dynamically loaded content is used on almost 90% of modern websites, therefore this knowledge is in high demand

there are several alternatives, such as utilising selenium or building a new class to mimic a client. they are, however, frequently overly complicated because they were not created for this purpose. the “requests html” library, fortunately, is available. let’s have a look at one of its characteristics: a request ()

the basic purpose of the request() function is to render dynamically loaded information and return the target data. but how do you go about it? ignore ‘productlist’ for now; i’ll show it in the p_rs_() function later. to begin building the website, we must first collect all of the data using the get() method. the url supplied as a parameter is significant since it allows us to reuse our code by passing in a different url. below the get() function, we have our primary character, html.render(), which acts as a client to render all dynamically loaded data. after that, i used xpath to locate the desired portion (right cl!ck, select copy, select xpath). this is, in my opinion, the most basic way, although it is far from the only one. other options are available on the library’s website

before i go any further, i’d like to quickly explain how the website is organised and what my plan is for making sure everything makes sense. the information i require is on a distinct page from the racquet index page, and it is customised to each individual racquet. like a result, as we did in the request() function, i must first retrieve all of the url links for each racquet from the racquet index list. the index page must then be submitted to the p_rs_ function, where i p_rs_ each individual racquet page one by one to collect their own specs on their own page, which leads me to the next function: decipher ()

- tennis racquet data mining

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