Introduction
Web scraping or crawling refers to the technique to extract the information from a website and transform into structured data for later analysis. There are generally a few reasons that you may need to implement a web scraping scripts to automate the data collection process:
- There isn’t any public API available for you to get data from the source sites
- The information is updated from time to time, such as the exchange rate, you cannot manage it in a manual way
- The final data you need is piecemeal from multiple sites; and so on
Before you decide to implement a scraping script, you will also need to check to be sure that you are not violating the term of use for the data you are going to scrape. Some sites are against the scraping robot. This article is intended for education purpose to help you to understand the overall processes of web scraping, so we will assume you already know the implication of the web scraping and possible legal issues on how the data is used.
Scraping a website sometimes can be difficult depends on how the target website is designed and where the data is resided. But generally you can split the process into 3 steps. Let’s walk through them one by one.
Understand the structure of your target website
As the first step, you shall take a quick look at your target website to see how the front end interacts with the backend, and how the data is populated to the web page. To keep our example simple, let’s assume user authentication is not required and our target is to extract the price change for the top 20 cryptocurrencies from coindesk for further analysis.
The first thing we shall do is to understand how this information is organized on the website. Below is the screenshot of the data presented on the web page:
In Chrome browser, if you right click on the web page to inspect the HTML elements, you shall see that the entire data table is under <section class=”cex-table”>…</section>. You can verify this by hovering your mouse to this element, you would see there is a light blue overlay on the data table as per below:
Next, you may want to inspect each text field on the page to further understand how the table header and records are arranged. For instance, when you check the “Asset” text field, you would see the below HTML structure:
<section class="cex-table"> <section class="thead"> <div>...</div> <div class="tr-wrapper"> <div class="tr-left"> <div class="tr"> <div>...</div> <div style="flex:7" class="th"> <span class="cell"> <i class="sorting-icon"> </i> <span class="cell-text">Asset</span> </span> </div> </div> </div> </div> ... </section> </section>
And similarly you can find the structure of the first row in the table body as per below:
<section class="tbody"> <section class="tr-section"> <a href="/price/bitcoin"> <div class="tr-wrapper"> <div class="tr-left"> <div class="tr"> <div style="flex:2" class="td"> <span class="cell cell-rank"> <strong>01</strong> </span> </div> <div style="flex:7" class="td"> <span class="cell cell-asset"> <img>...</img> <strong class="cell-asset-title">Bitcoin</strong> <span class="cell-asset-iso">BTC</span> </span> </div> </div> </div> </div> </a> </section> </section>
You may notice that majority of these HTML elements does not have a id or name attribute as the unique identifier, but the style sheet (“class” attribute) is quite consistent for the same row of data. So in this case, we shall consider to use the style sheet as a reference to find our data elements.
Locate and parse the target data element with XPath
With the initial understanding on HTML structure of our target website, we shall start to find a way to locate the data elements programmatically.
For this demonstration, we will use requests and lxml libraries to send the http requests and parse the results. There are other package for parsing DOM such as beautifulsoup, but personally I find using XPath expression is more straightforward when locating an element although the syntax may not as intuitive as the way beautifulsoup does.
Below is the pip command if you do not have these two packages installed:
pip install requests pip install lxml
Let’s import the packages and send a GET request to our target URL:
import requests from lxml import html target_url = "https://www.coindesk.com/coindesk20" result = requests.get(target_url)
Our target URL does not require any parameters, in case you need to pass in parameters, you can pass via the params argument as per below:
payload = {"q" : "bitcoin", "s" : "relevant"}
result = requests.get("https://www.coindesk.com/search", params=payload)
The result is a response object which has a status_code attribute to indicate if correct response has been returned from the target website. To simplify the code, let’s assume we can always get the correct response with the return HTML in string format from the text attribute.
We then pass our HTML string to lxml and use it to parse the DOM tree as per below:
tree = html.fromstring(result.text)
Now we come to the most important step, we will need to use XPath syntax to locate the data elements we want and extract the data out.
Since the id or name attributes are not available for these elements, we will need to use the style sheet to locate our data elements. To locate the table header, we need to perform the below:
- Find the section tag with style sheet class as “cex-table” from the entire DOM
- Find its child section node with style sheet class as “thead“
- Further find its child div node with style sheet as “tr-wrapper“
Below is how the syntax looks like in XPath:
table_header = tree.xpath("//section[@class='cex-table']/section[@class='thead']/div[@class='tr-wrapper']")
It will scan through the entire DOM tree to find if any element matches this structure and return a list of nodes matched.
If everything goes well, the table_header list should only contain 1 element which is the div with “tr-wrapper” style sheet. Sometimes if it returns multiple nodes, you may need recheck your path expression to see how you can fine-tune it to get only the unique node that you need.
From the wrapper div, there are still a few levels before we can reach to the node with the text. But you may notice that all the data fields we need are under the span tag which has a style name “cell-text“. So we can actually locate all these span tags with CSS class and extract its text with text() function. Below is how it works in XPath expression:
headers = table_header[0].xpath(".//span[@class='cell']/span[@class='cell-text']/text()")
Note that “.” means to start from current node, and “//” is to indicate the following path expression is relative path
If you examine the headers now, you can see all the column headers are extracted into a list as per below:
['Asset', 'Price', 'Market Cap', 'Total Exchange Volume', 'Returns (24h)', 'Total Supply', 'Category', 'Value Proposition', 'Consensus Mechanism']
Let’s continue to move the table body. Following the same logic, we shall be able to locate to the section with “tr-section” in below syntax:
table_body = tree.xpath("//section[@class='cex-table']/section[@class='tbody']/section[@class='tr-section']")
This means that we have already collected all the nodes for rows in the table body. We can now loop through the rows to get the elements. We will use the style sheet to locate our elements, but for the “Asset” column, it actually contains a few child nodes with different style sheet, so we need to handle them separately from the rest of the columns. Below is the code to extract the data row by row and add it into a record list:
records = []
for row in table_body:
tokens = row.xpath(".//span[contains(@class, 'cell-asset-iso')]/text()")
ranks = row.xpath(".//span[contains(@class, 'cell-rank')]/strong/text()")
assets = row.xpath(".//span[contains(@class, 'cell-asset')]/strong/text()")
spans = row.xpath(".//div[contains(@class,'tr-right-wrapper')]/div/span[contains(@class, 'cell')]")
rest_cols = [span.text_content().strip() for span in spans]
row_data = ranks + tokens + assets + rest_cols
records.append(row_data)
Note that we are using “contains” in order to match the node with class like “cell cell-rank“, and use text_content() to extract all the text from its current nodes and child nodes.
Occasionally you may find that the number of columns we extracted does not tally with the original column header due to header column merged or hidden, such as our above ranking and token ticker column. So let’s also give them column name as “Rank” and “Token”:
column_header = ["Rank", "Token"] + headers
Save the scraping result
With both the header and data ready, we can easily load the data into pandas as per below:
import pandas as pd df = pd.DataFrame(records, columns=column_header)
You can see the below result in pandas dataframe, which looks pretty good except some formatting to be done to convert all the amount into proper number format.
Or you can also write the scrapped data into a csv file with the csv module:
import csv
with open("token_price.csv", "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(column_header)
for row in records:
writer.writerow(row)
Limitations & Constraints
In your real scraping project, you may encounter more complicated scenarios rather than directly getting the data from a GET request. So it’s better to understand how are the constraints/limitations for our above mentioned approach.
- Go through the authentication process can be time-consuming with requests
If your target website requires authentication before you can retrieve the data, you may need to create a session and send multiple POST/GET requests to the server in order to get yourself authorized. Depends on how complicated the authentication process is, you will need to understand what are the parameters to be supplied and how the requests are chained together. This process may take some time and effort.
- You cannot trigger JavaScript code to get your data
If the response from your target website returns some JavaScript code to populate the data, or you need to trigger some JavaScript function in order to have the data populated on the web page, you may find requests package simply would not work.
For both scenarios, you may consider to use selenium which I have mentioned in one of my past post. It has a headless mode where you can simulate user’s action such as key in user credentials or click buttons without actually showing the browser, and you can also execute JavaScript code to interact with the web page. The downside is that you will have to periodically upgrade your driver file to match with the browser’s version.
Conclusion
In this article, we have reviewed through a very basic example to scrape data with requests and lxml packages, and we have also discussed a few limitations where you may start looking for alternatives such as selenium or even the scrapy framework in case you have more complicated scenarios to be handled. No matter which libraries you choose to use, the fundamental remains the same. Hope this article gives you some hints on how to start your web scraping journey.