Python code: | R code:

# 12.Scraping online data

Abstract In this chapter, you will learn how to retrieve your data from online sources. We first discuss the use of Application Programming Interfaces, more commonly known as APIs, which allow you to retrieve data from social media platforms, government websites, or other forms of open data, in a machine-readable format. We then discuss how to do web scraping in a narrower sense to retrieve data from websites that do not offer an API. We also discuss how to deal with authentication mechanisms, cookies, and the like, as well as ethical, legal, and practical considerations.
Keywords: web scraping, application programming interface (API), crawling, HTML parsing
Chapter objectives:
• Be able to use APIs for data retrieval
• Be able to write your own web scraper
• Assess basics of legal, ethical, and practical constraints
Packages used in this chapter
This chapter uses in particular httr (R) and requests (Python) to retrieve data, json (Python) and jsonlite (R) to handle JSON responses, and lxml and Selenium for web scraping. You can install these and some additional packages (e.g., for geocoding) with the code below if needed (see Section 1.4 for more details):
Python code
!pip3 install requests geopandas geopy selenium


R code
install.packages(c("tidyverse", 
                   "httr", "jsonlite", "glue", 
                   "data.table"))  
After installing, you need to import (activate) the packages every session:
Python code
# accessing APIs and URLs
import requests

# handling of JSON responses
import json
from pprint import pprint
from pandas import json_normalize

# general data handling
# note: you need to additionally install geopy
import geopandas as gpd 
import pandas as pd

# static web scraping
from urllib.request import urlopen
from lxml.html import parse, fromstring

# selenium
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import (
    WebDriverWait)
from selenium.webdriver.support import (
    expected_conditions as EC)
from selenium.webdriver.common.by import By

import time  
R code
library("tidyverse")
library("httr")
library("jsonlite")
library("rvest")
library("xml2")
library("glue")
library("data.table")



## 12.1.Using Web APIs: From Open Resources to Twitter

Let's assume we want to retrieve data from some online service. This could be some social media platform, but could also be a government website, some open data platform or initiative, or sometimes a commercial organization that provides some online service. Of course, we could surf to their website, enter a search query, and somehow save the result. This would result in a lot of impracticalities, though. Most notably, websites are designed such that they are perfectly readable and understandable for humans, but the cues that are used often have no “meaning” for a computer program. As humans, we have no problem understanding which parts of a web page refer to the author of some item on a web page, what the numbers “2006” and “2008” mean, and on. But it is not trivial to think of a way to explain to a computer program how to identify variables like author, title, or year on a web page. We will learn how to do exactly that in Section 12.2. Writing such a parser is often necessary, but it is also error-prone and a detour, as we are trying to bring some information that has been optimized for human reading back to a more structured data structure.

Luckily, however, many online services not only have web interfaces optimized for human reading, but also offer another possibility to access the data they provide: an API (Application Programming Interface). The vast amount of contemporary web APIs work like this: you send a request to some URL, and you get back a JSON object. As you learned in Section 5.2, JSON is a nested data structure, very much like a Python dictionary or R named list (and, in fact, JSON data are typically represented as such in Python and R). In other words: APIs directly gives us machine-readable data that we can work with without any need to develop a custom parser.

Discussing specific APIs in a book can be a bit tricky, as there is a chance that it will be outdated: after all, the API provider may change it at any time. We therefore decided not to include a chapter on very specific applications such as “How to use the Twitter API” or similar – given the popularity of such APIs, a quick online search will produce enough up-to-date (and out-of-date) tutorials on these. Instead, we discuss the generic principles of APIs that should easily translate to examples other than ours.

In its simplest form, using an API is nothing more than visiting a specific URL. The first part of the URL specifies the so-called API endpoint: the address of the specific API you want to use. This address is then followed by a ? and one or more key-value pairs with an equal sign like this: key=value. Multiple key-value pairs are separated with a &.

For instance, at the time of the writing of this book, Google offers an API endpoint, https://www.googleapis.com/books/v1/volumes, to search for books on Google Books. If you want to search for books about Python, you can supply a key q (which stands for query) with the value “python” (Example 12.1). We do not need any specific software for this – we could, in fact, use a web browser as well. Popular packages that allow us to do it programatically are httr in combination with jsonlite (R) and requests (Python).

But how do we know which parameters (i.e., which key-value pairs) we can use? We need to look it up in the documentation of the API we are interested in (in this example developers.google.com/books/docs/v1/using). There is no other way of knowing that the key to submit a query is called q, and which other parameters can be specified.

In our example, we used a simple value to include in the request: the string “python”. But what if we want to submit a string that contains, let's say, a space, or a character like & or ? which, as we have seen, have a special meaning in the request? In these cases, you need to “encode” your URL using a mechanism called URL encoding or percent encoding. You may have seen this earlier: a space, for instance, is represented by %20

#### Example 12.1. Retrieving JSON data from the Google Books API.

Python code
r = requests.get("https://www.googleapis.com/"
                 "books/v1/volumes?q=python")
data = r.json()
print(data.keys())  # "items" seems most promising
pprint(data["items"][0]) # let's print the 1st one

R code
url = str_c("https://www.googleapis.com/books/",
          "v1/volumes?q=python")
r = GET(url)
data = content(r, as="parsed")
print(names(data))
print(data$items[[1]])  Python output. Note that R output may look slightly different dict_keys(['kind', 'totalItems', 'items']) {'accessInfo': {'accessViewStatus': 'NONE', 'country': 'NL', 'embeddable': False, 'epub': {'isAvailable': False}, 'pdf': {'isAvailable': False}, 'publicDomain': False, 'quoteSharingAllowed': False, 'textToSpeechPermission': 'ALLOWED', 'viewability': 'NO_PAGES', 'webReaderLink': 'http://play.google.com/books/reader?id=yijjwAEACAAJ&hl=&printsec=frontcover&source=gbs_api'}, 'etag': 'zp/xhhsKukU', 'id': 'yijjwAEACAAJ', 'kind': 'books#volume', 'saleInfo': {'country': 'NL', 'isEbook': False, 'saleability': 'NOT_FOR_SALE'}, 'searchInfo': {'textSnippet': 'With this handbook, you'll learn how to ' 'use: IPython and Jupyter: provide ' 'computational environments for data scientists ' 'using Python NumPy: includes the ndarray for ' 'efficient storage and manipulation of dense ' 'data arrays in Python Pandas: ...'}, 'selfLink': 'https://www.googleapis.com/books/v1/volumes/yijjwAEACAAJ', 'volumeInfo': {'allowAnonLogging': False, 'authors': ['Jacob T. Vanderplas', 'Jake VanderPlas'], 'averageRating': 5, 'canonicalVolumeLink': 'https://books.google.com/books/about/Python_Data_Science_Handbook.html?hl=&id=yijjwAEACAAJ', 'categories': ['Computers'], 'contentVersion': 'preview-1.0.0', 'description': 'For many researchers, Python is a first-class ' 'tool mainly because of its libraries for ' 'storing, manipulating, and gaining insight ' 'from data. Several resources exist for ' 'individual pieces of this data science stack, ' 'but only with the Python Data Science Handbook ' 'do you get them all--IPython, NumPy, Pandas, ' 'Matplotlib, Scikit-Learn, and other related ' 'tools. Working scientists and data crunchers ' 'familiar with reading and writing Python code ' 'will find this comprehensive desk reference ' 'ideal for tackling day-to-day issues: ' 'manipulating, transforming, and cleaning data; ' 'visualizing different types of data; and using ' 'data to build statistical or machine learning ' 'models. Quite simply, this is the must-have ' 'reference for scientific computing in Python. ' "With this handbook, you'll learn how to use: " 'IPython and Jupyter: provide computational ' 'environments for data scientists using Python ' 'NumPy: includes the ndarray for efficient ' 'storage and manipulation of dense data arrays ' 'in Python Pandas: features the DataFrame for ' 'efficient storage and manipulation of ' 'labeled/columnar data in Python Matplotlib: ' 'includes capabilities for a flexible range of ' 'data visualizations in Python Scikit-Learn: ' 'for efficient and clean Python implementations ' 'of the most important and established machine ' 'learning algorithms', 'imageLinks': {'smallThumbnail': 'http://books.google.com/books/content?id=yijjwAEACAAJ&printsec=frontcover&img=1&zoom=5&source=gbs_api', 'thumbnail': 'http://books.google.com/books/content?id=yijjwAEACAAJ&printsec=frontcover&img=1&zoom=1&source=gbs_api'}, 'industryIdentifiers': [{'identifier': '1491912057', 'type': 'ISBN_10'}, {'identifier': '9781491912058', 'type': 'ISBN_13'}], 'infoLink': 'http://books.google.nl/books?id=yijjwAEACAAJ&dq=python&hl=&source=gbs_api', 'language': 'un', 'maturityRating': 'NOT_MATURE', 'pageCount': 529, 'panelizationSummary': {'containsEpubBubbles': False, 'containsImageBubbles': False}, 'previewLink': 'http://books.google.nl/books?id=yijjwAEACAAJ&dq=python&hl=&cd=1&source=gbs_api', 'printType': 'BOOK', 'publishedDate': '2016', 'publisher': "O'Reilly Media", 'ratingsCount': 1, 'readingModes': {'image': False, 'text': False}, 'subtitle': 'Essential Tools for Working with Data', 'title': 'Python Data Science Handbook'}} The data our request returns are nested data, and hence, they do not really “fit” in a tabular data frame. We could keep the data as they are (and then, for instance, just extract the key-value pairs that we are interested in), but – for the sake of getting a quick overview – let's flatten the data so that they can be represented in a data frame (Example 12.2). This works quite well here, but may be more problematic when the items have a widely varying structure. If that is the case, we probably would want to write a loop to iterate over the different items and extract the information we are interested in. #### Example 12.2. Transforming the data into a data frame. Python code d = json_normalize(data["items"]) d.head()  R code r_text = content(r, "text") data_json = fromJSON(r_text, flatten=T) d = as.data.frame(data_json) head(d)  R output. Note that Python output may look slightly different A data.frame: 6 × 5 kindtotalItemsitems.kinditems.iditems.etag <chr><int><chr><chr><chr> 1books#volumes443books#volume2ZggjwEACAAJSL/fqJ1dIHQ 2books#volumes443books#volume1mZtP9H6OMQC9Xwidh/MUg4 3books#volumes443books#volumeENIVBdZIJ6cC54DFGUmBbfQ 4books#volumes443books#volumeyijjwAEACAAJPBNxIPu/Dk8 5books#volumes443books#volume9MS9BQAAQBAJ83CRBc7BfXw 6books#volumes443books#volumeBP_WAgAAQBAJgOkbxCI0oFI You may have realized that you did not get all results. This protects you from accidentally downloading a huge dataset (you may have underestimated the number of Python books available on the market), and saves the provider of the API a lot of bandwidth. This does not mean that you cannot get more data. In fact, many APIs work with pagination: you first get the first “page” of results, then the next, and so on. Sometimes, the API response contains a specific key-value pair (sometimes called a “continuation key”) that you can use to get the next results; sometimes, you can just say at which result you want to start (say, result number 11) and then get the next “page”. You can then write a loop to retrieve as many results as you need (Example 12.3) – just make sure that you do not get stuck in an eternal loop. When you start playing around with APIs, make sure you do not cause unnecessary traffic, but limit the number of calls that are made (see also Section 12.4). #### Example 12.3. Full script including pagination. Python code allitems = [] i = 0 while True:  r = requests.get("https://www.googleapis.com/"  "books/v1/volumes?q=python&maxResults="  f"40&startIndex={i}")  data = r.json()  if not "items" in data:  print(f"Retrieved {len(allitems)},"  "it seems like that's it")  break  allitems.extend(data["items"])  i+=40 d = json_normalize(allitems)  R code i = 0 j = 1 url = str_c("https://www.googleapis.com/books/",  "v1/volumes?q=python&maxResults=40",  "&startIndex={i}") alldata = list() while (TRUE) {  r = GET(glue(url))  r_text = content(r, "text")  data_json = fromJSON(r_text, flatten=T)  if (length(data_json$items)==0) {break}
    alldata[[j]] = as.data.frame(data_json)
    i = i + 40
    j = j + 1} 
d = rbindlist(alldata, fill=TRUE)  
R output. Note that Python output may look slightly different
A data.table: 6 × 5
kindtotalItemsitems.kinditems.iditems.etag
<chr><int><chr><chr><chr>
books#volumes433books#volume2ZggjwEACAAJw8RpK1LsfT4
books#volumes433books#volume1mZtP9H6OMQCT7fABU4eveM
books#volumes433books#volumeENIVBdZIJ6cCaCgPlDS7evE
books#volumes433books#volumeyijjwAEACAAJvtQw9sGLFuA
books#volumes433books#volume9MS9BQAAQBAJWb3CmjuAY/I
books#volumes433books#volumepjqbAgAAQBAJMuu0EHCF6k8

Many APIs work very much like the example we discussed, and you can adapt the logic above to many APIs once you have read their documentation. You would usually start by playing around with single requests, and then try to automate the process by means of a loop.

However, many APIs have restrictions regarding who can use them, how many requests can be made, and so on. For instance, you may need to limit the number of requests per minute by calling a sleep function within your loop to delay the execution of the next call. Or, you may need to authenticate yourself. In the example of the Google Books API, this will allow you to request more data (such as whether you own an (electronic) copy of the books you retrieved). In this case, the documentation outlines that you can simply pass an authentication token as a parameter with the URL. However, many APIs use more advanced authentication methods such as OAuth (see Section 12.3).

Lastly, for many APIs that are very popular with social scientists, specific wrapper packages exist (such as tweepy (Python) or rtweet (R) for downloading twitter messages) which are a bit more user-friendly and handle things like authentication, pagination, respecting rate-limits, etc., for you.

## 12.2.Retrieving and Parsing Web Pages

Unfortunately, not all online services we may be interested in offer an API – in fact, it has even been suggested that computational researchers have arrived in an “post-API age” Freelon, 2018, as API access for researchers has become increasingly restricted.

If data cannot be collected using an API (or a similar service, such as RSS feeds), we need to resort to web scraping. Before you start a web scraping project, make sure to ask the appropriate authorities for ethical and legal advice (see also Section 12.4).

Web scraping (sometimes also referred to as harvesting), in essence, boils down to automatically downloading web pages aimed at a human audience, and extracting meaningful information out of them. One could also say that we are reverse-engineering the way the information was published on the web. For instance, a news site may always use a specific formatting to denote the title of an article – and we would then use this to extract the title. This process is called “parsing”, which in this context is just a fancy term for “extracting meaningful information”.

When scraping data from the web, we can distinguish two different tasks: (1) downloading a (possibly large) number of webpages, and (2) parsing the content of the webpages. Often, both go hand in hand. For instance, the URL of the next page to be downloaded might actually be parsed from the content of the current page; or some overview page may contain the links and thus has to be parsed first in order to download subsequent pages.

We will first discuss how to parse a single HTML page (say, the page containing one specific product review, or one specific news article), and then describe how to “scale up” and repeat the process in a loop (to scrape, let's say, all reviews for the product; or all articles in a specific time frame).

### 12.2.1.Retrieving and Parsing an HTML Page

In order to parse an HTML file, you need to have a basic understanding of the structure of an HTML file. Open your web browser, visit a website of your choice (we suggest to use a simple page, such as css-book.net/d/restaurants/index.html), and inspect its underlying HTML code (almost all browsers have a function called something like “view source”, which enables you to do so).

You will see that there are some regular patterns in there. For example, you may see that each paragraph is enclosed with the tags <p> and </p>. Thinking back to Section 9.2, you may figure out that you could, for instance, use a regular expression to extract the text of the first paragraph. In fact, packages like beautifulsoup under the hood use regular expressions to do exactly that.

Writing your own set of regular expressions to parse an HTML page is usually not a good idea (but it can be a last resort when everything else fails). Chances are high that you will make a mistake or not handle some edge case correctly; and besides, it would be a bit like re-inventing the wheel. Packages like rvest (R), beautifulsoup, and lxml (both Python) already do this for you.

In order to use them, though, you need to have a basic understanding of what an HTML page looks like. Here is a simplified example:

<html>
<body>
<h1>This is a title</h1>
<div id="main">
<p> Some text with one <a href="test.html">link </a> </p>
<img src = "plaatje.jpg">an image </img>
</div>
<div id="body">
<p class="lead"> Some more text </p>
<p> Even more... </p>
<p> And more. </p>
</div>
</body>
</html>


For now, it is not too important to understand the function of each specific tag (although it might help, for instance, to realize that a denotes a link, h1 a first-level heading, p a paragraph and div some kind of section).

What is important, though, is to realize that each tag is opened and closed (e.g., <p> is closed by </p>). Because tags can be nested, we can actually draw the code as a tree. In our example, this would look like this:

• html
• body
• h1
• div#main
• p
• a
• img
• div
• p
• p

Additionally, tags can have attributes. For instance, the makers of a page with customer reviews may use attributes to specify what a section contains. For example, they may have written <p class="lead"> ... </div> to mark the lead paragraph of an article, and <a href=test.html"> ...</a> to specify the target of a hyperlink. Especially important here are the id and class attributes, which are often used by webpages to control the formatting. id (indicated with the hash sign # above) gives a unique ID to a single element, while class (indicated with a period) assigns a class label to one or more elements. This enables web sites to specify their layout and formatting using a technique called Cascading Style Sheets (CSS). For example, the web page could set the lead paragraph to be bold. The nice thing is that we can exploit this information to tell our parser where to find the elements we are interested in.

#### Table 12.1. Overview of CSSSelect and XPath syntax

Example CSS Select XPath
h1 anywhere in document h1 //h1
h1 inside a body body h1 //body//h1
h1 directly inside div div > h1 //div/h1
Any node directly inside div div > * //div/*
p next to a h1 h1   p //h1/following-sibling::p
p next to a h1 h1 + p //h1/following-sibling::p[1]
Node attributes
<div id='x1'> div#x1 //div[@id='x1']
any node with id x1 #x1 //*[@id='x1']
<div class='row'> div.row //div[@class='row']
any node with class row .row //*[@class='row']
a with href="#" a[href="#"] //a[@href="#"]
a in a div with class 'meta' #main > div.meta a //*[@id='main']
$$\;\;\;\;$$ directly inside the main element $$\;\;\;\;$$ /div[@class='meta']//a
First p in a div div p:first-of-type //div/p[1]
First child of a div div :first-child //div/*[1]
Second p in a div div p:nth-of-type(2) //div/p[2]
Second p in a div div p:nth-of-type(2) //div/p[2]
parent of the div with id x1 (not possible) //div[@id='x1']/parent::*

CSS Selectors The easiest way to specify our parser to look for a specific element is to use a CSS Selector, which might be familiar to you if you have created web pages. For example, to find the lead paragraph(s) we specify p.lead. To find the node with id="body", we can specify #body. You can also use this to specify relations between nodes. For example, to find all paragraphs within the body element we would write #body p.

Table 12.1 gives an overview of the possibilities of CSS Select. In general, a CSS selector is a set of node specifiers (like h1, .lead or div#body), optionally with relation specifiers between them. So, #body p finds a p anywhere inside the id=body element, while #body > p requires the p to be directly contained inside the body (with no other nodes in between).

XPath An alternative to CSS Selectors is XPath. Where CSS Selectors are directly based on HTML and CSS styling, XPath is a general way to describe nodes in XML (and HTML) documents. The general form of XPath is similar to CSS Select: a sequence of node descriptors (such as h1 or *[@id='body']). Contrary to CSS Select, you always have to specify the relationship, where // means any direct or indirect descendant and / means a direct child. If the relationship is not a child or descendant relationship (but for example a sibling or parent), you specify the axis with e.g. //a/parent::p meaning an a anywhere in the document (//a) which has a direct parent (/parent::) that is a p.

A second difference with CSS Selectors is that the class and id attributes are not given special treatment, but can be used with the general [@attribute='value'] pattern. Thus, to get the lead paragraph you would specify //p[@class='lead'].

The advantage of XPath is that it is a very powerful tool. Everything that you can describe with a CSS Selector can also be described with an XPath pattern, but there are some things that CSS Selectors cannot describe, such as parents. On the other hand, XPath patterns can be a bit harder to write, read, and debug. You can choose to use either tool, and you can even mix and match them in a single script, but our general recommendation is to use CSS Selectors unless you need to use the specific abilities of XPath.

Example 12.4 shows how to use XPATHs and CSS selectors to parse an HTML page. To fully understand it, open cssbook.net/d/restaurants/index.html in a browser and look at its source code (all modern browsers have a function “View page source” or similar), or – more comfortable – right-click on an element you are interested in (such as a restaurant name) and select “Inspect element” or similar. This will give you a user-friendly view of the HTML code.

#### Example 12.4. Parsing websites using XPATHs or CSS selectors

Python code
tree=parse(urlopen(
    "https://cssbook.net/d/eat/index.html"))

# get the restaurant names via XPATH 
print([e.text_content().strip() for e in 
       tree.xpath("//h3")])

# get the restaurant names via CSS Selector
print([e.text_content().strip() for e in
       tree.getroot().cssselect("h3")])  
R code
url = "https://cssbook.net/d/eat/index.html"
page = read_html(url)

# get the restaurant names via XPATH
page %>% html_nodes(xpath="//h3") %>% html_text() 

# get the restaurant names via CSS Selector
page %>% html_nodes("h3") %>% html_text() 


Python output. Note that R output may look slightly different
['Pizzeria Roma', 'Trattoria Napoli', 'Curry King']
['Pizzeria Roma', 'Trattoria Napoli', 'Curry King']

Of course, Example 12.4 only parses one possible element of interest: the restaurant names. Try to retrieve other elements as well!

Do you care about children? Regardless of whether you use XPATHS or CSS Selectors to specify which part of the page you are interested in, it is often the case that there are other elements within it. Depending on whether you want to also retrieve the text of these elements or not, you have to use different approaches (see Example 12.5).

#### Example 12.5. Getting the text of an HTML element versus getting the text of the element and its children

Python code
# three ways of extracting text
print("Appending /text() to the XPATH gives you "
      "exactly the text that is in the element "
      "itself, including line-breaks that happen "
      "to be in the source code:" )
print(tree.xpath(
    "//div[@class='restaurant']/text()"))

print("\nUsing the text property of the"
      "elements in the list of elements that are "
      "matched by the XPATH expression gives you "
      "the text of the elements themselves "
      "without the line breaks: ")
print([e.text for e in tree.xpath(
    "//div[@class='restaurant']")])

print("\nUsing the text_content() method "
      "instead returns the text of the element "
      "*and the text of its children*:")
print([e.text_content() for e in tree.xpath(
    "//div[@class='restaurant']")])

print("\nThe same but using CSS Selectors (note "
      "the .getroot() method, because the "
      "selectors can only be applied to HTML "
      "elements, not to DOM trees): ")
print([e.text_content() for e in
       tree.getroot().cssselect(".restaurant")])  
R code
url = "https://cssbook.net/d/eat/index.html"
page = read_html(url)

glue("Appending /text() to the XPATH gives you\\
exactly the text that is in the element itself, \\
including line-breaks that happen to be in the \\
source code:" )
page %>% html_nodes(xpath=
    "//div[@class='restaurant']/text()") 

glue("Using the html_text function instead \\
returns the text of the element *and the text \\
of its children*:")
page %>% html_nodes(xpath=
    "//div[@class='restaurant']") %>% html_text()

glue("The same but using CSS Selectors:")
page %>% html_nodes(".restaurant") %>% html_text()


Python output. Note that R output may look slightly different
Appending /text() to the XPATH gives you exactly the text that is in the element itself, including line-breaks that happen to be in the source code:
[' ', '\n      ', '\n      ', '\n    ', ' ', '\n      ', '\n      ', '\n    ', ' ', '\n      ', '\n      ', '\n    ']

Using the text property of theelements in the list of elements that are matched by the XPATH expression gives you the text of the elements themselves without the line breaks:
[' ', ' ', ' ']

Using the text_content() method instead returns the text of the element *and the text of its children*:
['  Pizzeria Roma \n       Here you can get ... ... \n       Read the full review here\n    ', '  Trattoria Napoli \n       Another restaurant ... ... \n       Read the full review here\n    ', '  Curry King \n       Some description. \n       Read the full review here\n    ']

The same but using CSS Selectors (note the .getroot() method, because the selectors can only be applied to HTML elements, not to DOM trees):
['  Pizzeria Roma \n       Here you can get ... ... \n       Read the full review here\n    ', '  Trattoria Napoli \n       Another restaurant ... ... \n       Read the full review here\n    ', '  Curry King \n       Some description. \n       Read the full review here\n    ']

Notably, you may want to parse links. In HTML, links use a specific tag, a. These tags have an attribute, href, which contains the link itself. Example 12.6 shows how, after selecting the a tags, we can access these attributes.

Python code
linkelements = tree.xpath("//a")
linktexts = [e.text for e in linkelements]
links = [e.attrib["href"] for e in linkelements]

print(linktexts)
print(links)

R code
page %>% 
  html_nodes(xpath="//a") %>% 
  html_text() 
page %>% 
  html_nodes(xpath="//a") %>% 
  html_attr("href")

Python output. Note that R output may look slightly different
['here', 'here', 'here']
['review0001.html', 'review0002.html', 'review0003.html']
Pretending to be a specific browser. When lxml, rvest, or your web browser download an HTML page, they send a so-called HTTP request. This request contains the URL, but also some meta-data, such as a so-called user-agent string. This string specifies the name and version of the browser. Some sites may block specific user agents (such as, for instance, the ones that lxml or rvest use); and sometimes, they deliver different content for different browsers. By using a more powerful module for downloading the HTML code (such as requests or httr) before parsing it, you can specify your own user-agent string and thus pretend to be a specific browser. If you do a web search, you will quickly find long lists with popular strings. In Example 12.7, we rewrote Example 12.4 such that a custom user-agent can be specified.

#### Example 12.7. Specifying a user agent to pretend to be a specific browser

Python code
import requests
from lxml.html import fromstring
headers = {"User-Agent": "Mozilla/5.0 (Windows "
    "NT 10.0; Win64; x64; rv:60.0) "
    "Gecko/20100101 Firefox/60.0"}

htmlsource = requests.get(
    "https://cssbook.net/d/eat/index.html", 
    headers = headers).text
tree = fromstring(htmlsource)
print([e.text_content().strip() for e in 
       tree.xpath("//h3")])  
R code
r = GET("https://cssbook.net/d/eat/index.html",
    user_agent=str_c("Mozilla/5.0 (Windows NT ",
    "10.0; Win64; x64; rv:60.0) Gecko/20100101 ",
    "Firefox/60.0"))
page = read_html(r)
page %>% html_nodes(xpath="//h3") %>% html_text() 


Python output. Note that R output may look slightly different
['Pizzeria Roma', 'Trattoria Napoli', 'Curry King']

### 12.2.2.Crawling Websites

Once we have mastered parsing a single HTML page, it is time to scale up. Only rarely are we interested in parsing a single page. In most cases, we want to use an HTML page as a starting point, parse it, follow a link to some other interesting page, parse it as well, and so on. There are some dedicated frameworks for this such as scrapy, but in our experience, it may be more of a burden to learn that framework than to just implement your crawler yourself.

Staying with the example of a restaurant review website, we might be interested in retrieving all restaurants from a specific city, and for all of these restaurants, all available reviews.

Our approach, thus, could look as follows:

1. Retrieve the overview page.
2. Parse the names of the restaurants and the corresponding links.
3. Loop over all the links, retrieve the corresponding pages.
4. On each of these pages, parse the interesting content (i.e., the reviews, ratings, and so on).

So, what if there are multiple overview pages (or multiple pages with reviews)? Basically, there are two possibilities: the first possibility is to look for the link to the next page, parse it, download the next page, and so on. The second possibility exploits the fact that often, URLs are very systematic: for instance, the first page of restaurants might have a URL such as myreviewsite.com/amsterdam/restaurants.html?page=1. If this is the case, we can simply construct a list with all possible URLs (Example 12.8)

#### Example 12.8. Generating a list of URLs that follow the same pattern.

Python code
baseurl="https://reviews.com/?page="
tenpages = [f"{baseurl}{i+1}" for i in range(10)]
print(tenpages)  
R code
baseurl="https://reviews.com/?page="
tenpages=glue("{baseurl}{1:10}")
print(tenpages)  
Python output. Note that R output may look slightly different
['https://reviews.com/?page=1', 'https://reviews.com/?page=2', 'https://reviews.com/?page=3', 'https://reviews.com/?page=4', 'https://reviews.com/?page=5', 'https://reviews.com/?page=6', 'https://reviews.com/?page=7', 'https://reviews.com/?page=8', 'https://reviews.com/?page=9', 'https://reviews.com/?page=10']

Afterwards, we would just loop over this list and retrieve all the pages (a bit like how we approached Example 12.3 in Section 12.1).

However, often, things are not as straightforward, and we need to find the correct links on a page that we have been parsing – that's why we crawl through the website.

Writing a good crawler can take some time, and they will look very differently for different pages. The best advice is to build them up step-by-step. Carefully inspect the website you are interested in. Take a sheet of paper, draw its structure, and try to find out which pages you need to parse, and how you can get from one page to the next. Also think about how the data that you want to extract should be organized.

We will illustrate this process using our mock-up review website cssbook.net/d/restaurants/. First, have a look at the site and try to understand its structure.

You will see that it has an overview page, index.html, with the names of all restaurants and, per restaurant, a link to a page with reviews. Click on these links, and note your observations, such as:

• the pages have different numbers of reviews;
• each review consists of an author name, a review text, and a rating;
• some, but not all, pages have a link saying “Get older reviews”

If you combine what you just learned about extracting text and links from HTML pages with your knowledge about control structures like loops and conditional statements (Section 3.2), you can now write your own crawler.

Writing a scraper is a craft, and there are several ways of achieving your goal. You probably want to develop your scraper in steps: first write a function to parse the overview page, then a function to parse the review pages, then try to combine all elements into one script. Before you read on, try to write such a scraper.

To show you one possible solution, we implemented a scraper in Python that crawls and parses all reviews for all restaurants (Example 12.9), which we describe in detail below.

#### Example 12.9. Crawling a website

Python code
BASEURL = "https://cssbook.net/d/eat/"

def get_restaurants(url):
  """takes the URL of an overview page as input
  returns a list of (name, link) tuples"""
  tree = parse(urlopen(url))
  names = [e.text.strip() for e in 
    tree.xpath("//div[@class='restaurant']/h3")]
  links = [e.attrib["href"] for e in 
    tree.xpath("//div[@class='restaurant']//a")]
  return list(zip(names, links))

def get_reviews(url):
  """yields reviews on the specified page"""
  while True:
    print(f"Downloading {url}...")
    tree = parse(urlopen(url))
    names = [e.text.strip() for e in 
      tree.xpath("//div[@class='review']/h3")]
    texts = [e.text.strip() for e in 
      tree.xpath("//div[@class='review']/p")]
    ratings = [e.text.strip() for e in tree.xpath(
      "//div[@class='rating']")]
    for u,txt,rating in zip(names,texts,ratings):
      review = {}
      review["username"] = u.replace("wrote:","")
      review["reviewtext"] = txt
      review["rating"] = rating
      yield review
    bb=tree.xpath("//span[@class='backbutton']/a")
    if bb:
      print("Processing next page")
      url = BASEURL+bb[0].attrib["href"]
    else:
      print("No more pages found.")
      break
        
print("Retrieving all restaurants...")
links = get_restaurants(BASEURL+"index.html")
print(links)

with open("reviews.json", mode = "w") as f:
    for restaurant, link in links:
        print(f"Processing {restaurant}...")
        for r in get_reviews(BASEURL+link):
            r["restaurant"] = restaurant
            f.write(json.dumps(r))
            f.write("\n")
            
# You can process the results with pandas
# (using lines=True since it"s one json per line)
df = pd.read_json("reviews.json", lines=True)
print(df)  
Python output. Note that R output may look slightly different
Retrieving all restaurants...
[('Pizzeria Roma', 'review0001.html'), ('Trattoria Napoli', 'review0002.html'), ('Curry King', 'review0003.html')]
Processing Pizzeria Roma...
No more pages found.
Processing Trattoria Napoli...
No more pages found.
Processing Curry King...
Processing next page
Processing next page
No more pages found.
0     gourmet2536   The best thing to do is ordering a full menu, ...  7.0/10
1        foodie12                          The worst food I ever had!  1.0/10
2    mrsdiningout             If nothing else is open, you can do it.  6.5/10
3        foodie12                               Best Italian in town!  8.6/10
4           smith                                            Love it!  9.0/10
5        foodie12                                             Superb!  9.2/10
6      dontlikeit                       As expected, I didn't like it  4.0/10
7        otherguy                              Try the yoghurt curry!  7.7/10
8           tasty                    We went here for dinner once and  7.0/10
10           hans                                     Not much to say  5.0/10
11        bee1983                                    I am a huge fan!   10/10
12         rhebjf           The service is good, the food not so much  6.5/10
13  foodcritic555                              Once and never again!.  1.0/10

restaurant
0      Pizzeria Roma
1      Pizzeria Roma
2   Trattoria Napoli
3   Trattoria Napoli
4         Curry King
5         Curry King
6         Curry King
7         Curry King
8         Curry King
9         Curry King
10        Curry King
11        Curry King
12        Curry King
13        Curry King

First, we need to get a list of all restaurants and the links to their reviews. That's what is done in the function get_restaurants. This is actually the first thing we do (see line 32).

We now want to loop over these links and retrieve the reviews. We decided to use a generator (Section 3.2): instead of writing a function that collects all reviews in a list first, we let the function yield each review immediately – and then append that review to a file. This has a big advantage: if our scraper fails (for instance, due to a time out, a block, or a programming error), then we have already saved the reviews we got so far.

We loop over the links to the restaurants (line 36) and call the function get_reviews (line 38). Each review it returns (the review is a dict) gets the name of the restaurant as an extra key, and then gets written to a file which contains one JSON-object per line (also known as a jsonlines-file).

The function get_reviews takes a link to a review page as input and yields reviews. If we knew all pages with reviews already, then we would not need the while loop statement in line 12 and the lines 24–29. However, as we have seen, some review pages contain a link to older reviews. We therefore use a loop that runs forever (that is what while True: does), unless it encounters a break statement (line 29). An inspection of the HTML code shows that these links have a span tag with the attribute class="backbutton". We therefore check if such a button exists (line 24), and if so, we get its href attribute (i.e., the link itself), overwrite the url variable with it, and then go back to line 16, the beginning of the loop, so that we can download and parse this next URL. This goes on until such a link is no longer found.

### 12.2.3.Dynamic Web Pages

You may have realized that all our scraping efforts until now proceeded in two steps: we retrieved (downloaded) the HTML source of a web page and then parsed it. However, modern websites more and more frequently are dynamic rather than static. For example, after being loaded, they load additional content, or what is displayed changes based on what the user does. Frequently, some JavaScript is run within the user's browser to do that. However, we do not have a browser here. The HTML code we downloaded may contain some instructions for the browser that some code needs to be run, but in the absence of a browser, our Python or R script cannot do this.

As a first test to check out whether this is a concern, you can simply check whether the HTML code in your browser is the same as that you would get if you downloaded it with R or Python. After having retrieved the page (Example 12.7), you simply dump it to a file (Example 12.10) and open this file in your browser to verify that you indeed downloaded what you intended to download (and not, for instance, a login page, a cookie wall, or an error message).

#### Example 12.10. Dumping the HTML source to a file

Python code
with open("test.html", mode="w") as fo:
    fo.write(htmlsource)

R code
fileConn<-file("test.html")
writeLines(content(r, as = "text"), fileConn)
close(fileConn)  

If this test shows that the data you are interested in is indeed not part of the HTML code you can retrieve with R or Python, and use the following checklist to find

1. Does using a different user-agent string (see above) solve the issue?
2. Is the issue due to some cookie that needs to be accepted or requires you to log in (see below)?
3. Is a different page delivered for different browsers, devices, display settings, etc.?

If all of this does not help, or if you already know for sure that the content you are interested in is dynamically fetched via JavaScript or similar, you can use Selenium to literally start a browser and extract the content you are interested in from there. Selenium has been designed for testing web sites and allows you to automate clicks in a browser window, and also supports CSS selectors and Xpaths to specify parts of the web page.

Using Selenium may require some additional setup on your computer, which may depend on your operating system and the software versions you are using – check out the usual online sources for guidance if needed. It is possible to use Selenium through R using Rselenium. However, doing so can be quite a hassle and requires, running a separate Selenium server, for instance, using Docker. If you opt to use Selenium for web scraping, your safest bet is probably to follow an online tutorial and/or to dive into the documentation. To give you a first impression of the general working, Example 12.11 shows you how to (at the time of writing of this book) open Firefox, surf to Google, google for Tintin by entering that string and pressing the return key, click on the first link containing that string, and take a screenshot of the result.

#### Example 12.11. Using Selenium to literally open a browser, input text, click on a link, and take a screenshot.

Python code
driver = webdriver.Firefox()
driver.implicitly_wait(10)
driver.get("https://www.duckduckgo.com")
element = driver.find_element_by_name("q")
# also check out other options such as 
# .find_element_by_xpath
# or .find_element_by_css_selector
element.send_keys("TinTin")
element.send_keys(Keys.RETURN)
try:
    driver.find_element_by_css_selector(
        "#links a").click()
    # let"s be cautious and wait 10 seconds
    # so that everything is loaded
    time.sleep(10)
    driver.save_screenshot("screenshotTinTin.png")
finally:
    # whatever happens, close the browser
    driver.quit()  
Losing your head If you want to run long-lasting scraping processes using Selenium in the background (or on a server without a graphical user interface), you may want to look into what is called a “headless” browser. For instance, Selenium can start Firefox in “headless” mode, which means that it will run without making any connection to a graphical interface. Of course, that also means that you cannot watch Selenium scrape, which may make debugging more difficult. You could opt for developing your scraper first using a normal browser, and then changing it to use a headless browser once everything works.

### 12.3.1.Authentication and APIs

When we introduced APIs in Section 12.1, we used the example of an API where you did not need to authenticate yourself. As we have seen, using such an API is as simple as sending an HTTP request to an endpoint and getting a response (usually, a JSON object) back. And indeed, there are plenty of interesting APIs (think for instance of open government APIs) that work this way.

While this has obvious advantages for you, it also has some serious downsides from the perspective of the API provider as well as from a security and privacy standpoint. The more confidential the data is, the more likely it is that the API provider needs to know who you are in order to determine which data you are allowed to retrieve; and even if the data are not confidential, authentication may be used to limit the number of requests that an individual can make in a given time frame.

In its most simple form, you just need to provide a unique key that identifies you as a user. For instance, Example 12.12 shows how such a key can be passed along as an HTTP header, essentially as additional information next to the URL that you want to retrieve (see also Section 12.3.2). The example shows a call to an endpoint of a commercial API for natural language processing to inform how many requests we have made today.

#### Example 12.12. Passing a key as HTTP request header to authenticate at an API endpoint

Python code
requests.get("https://api.textrazor.com/account/",
  headers={"x-textrazor-key": "SECRET"}).json()

R code
r = GET("https://api.textrazor.com/account/", 
  add_headers("x-textrazor-key"="SECRET"))
print(content(r, "text"))  
Python output. Note that R output may look slightly different
{'ok': False, 'time': 0, 'error': 'Your TextRazor API Key was invalid.'}

As you see, using an API that requires authentication by passing a key as an HTTP header is hardly more complicated than using APIs that do not require authentication such as outlined in Section 12.1. However, many APIs use more complex protocols for authentication.

### 12.3.2.Authentication and Webpages

In this section, we briefly discuss different approaches for dealing with websites where you need to log on, accept something (e.g., a so-called cookie wall), or have to otherwise authenticate yourself. One approach can be the use of a web testing framework like Selenium (see Section 12.2.3): you let your script literally open a browser and, for instance, fill in your login information.

However, sometimes that's not necessary and we can still use simpler and more efficient webscraping without invoking a browser. As we have already seen in Section 12.2.1, when making an HTTP request, we can transmit additional information, such as the so-called user-agent string. In a similar way, we can pass other information, such as cookies.

In the developer tools of your browser (which we already used to determine XPATHs and CSS selectors), you can look up which cookies a specific website has placed. For instance, you could inspect all cookies before you logged on (or passed a cookie wall) and again inspect them afterwards to determine what has changed. With this kind of reverse-engineering, you can find out what cookies you need to manually set.

In Example 12.13, we illustrate this for a specific page (at the time of writing of our book). Here, by inspecting the cookies in Firefox, we found out that clicking “Accept” on the cookie wall landing page caused a cookie with the name cpc and the value 10 to be set. To set those cookies in our scraper, the easiest way is to retrieve that page first and store the cookies sent by the server. In Example 12.13, we therefore start a session and try to download the page. We know that this will only show us the cookie wall – but it will also generate the necessary cookies. We then store these cookies, and add the cookie that we want to be set (cpc=10) to this cookie jar. Now, we have all cookies that we need for future requests. They will stay there for the whole session.

If we only want to get a single page, we may not need to start a session to remember all the cookies, and we can just directly pass the single cookie we care about to a request instead (Example 12.14).

#### Example 12.13. Explicitly setting a cookie to circumvent a cookie wall

Python code
URL = "https://www.geenstijl.nl/5160019/page"

# circumvent cookie wall by setting a specific
# cookie: the key-value pair (cpc: 10)
client = requests.session()
r = client.get(URL)

cookies = client.cookies.items()
cookies.append(("cpc","10"))
response = client.get(URL,cookies=dict(cookies))
# end circumvention

tree = fromstring(response.text)
allcomments = [e.text_content().strip() for e in 
               tree.cssselect(".cmt-content")]
print(f"There are {len(allcomments)} comments.")


R code
URL = "https://www.geenstijl.nl/5160019/page/"

# circumvent cookie wall by setting a specific
# cookie: the key-value pair (cpc: 10)
r = GET(URL)
cookies = setNames(cookies(r)$value,   cookies(r)$name)
cookies = c(cookies, cpc=10)
r = GET(URL, set_cookies(cookies))
# end circumvention

allcomments = r %>% 
  read_html() %>%
  html_nodes(".cmt-content") %>% 
  html_text()

glue("There are {length(allcomments)} comments.")

Python output. Note that R output may look slightly different
Een kudtkoekiewall. Omdat dat moet, van de kudtkoekiewet.
There are 318 comments.

#### Example 12.14. Shorter version of <a href='#ex:cookiewall'>Example 12.13</a> for single requests

Python code
r = requests.get(URL,cookies={"cpc": "10"})
tree = fromstring(r.text)
allcomments = [e.text_content().strip() for e in 
               tree.cssselect(".cmt-content")]
print(f"There are {len(allcomments)} comments.")

R code
r = GET(URL, set_cookies(cpc=10))
allcomments = r %>% 
  read_html() %>%
  html_nodes(".cmt-content") %>% 
  html_text()
glue("There are {length(allcomments)} comments.")  

## 12.4.Ethical, Legal, and Practical Considerations

Web scraping is a powerful tool, but it needs to be handled responsibly. Between the white area of sites that explicitly consented to creating a copy of their data (for instance, by using a creative commons license) and the black area of an exact copy of copyrighted material and redistributing it as it is, there is a large gray area where it is less clear what is acceptable and what is not.

There is a tension between legitimate interests of the operators of web sites and the producers of content on the one hand, and the societal interest of studying online communication on the other hand. Which interest prevails may differ on a case-to-case basis. For instance, when using APIs as described in Section 12.1, in most cases, you have to consent to the terms of service (TOS) of the API provider.

For example, Twitter's TOS allow you to redistribute the numerical tweet ids, but not the tweets themselves, and therefore, it is common to share such lists of ids with fellow researchers instead of the “real” Twitter datasets. Of course, this is not optimal from a reproducibility point of view: if another researcher has to retrieve the tweets again based on their ids, then this is not only cumbersome, but most likely also leads to a slightly different dataset, because tweets may have been deleted in the meantime. At the same time, it is a compromise most people can live with.

Other social media platforms have closed their APIs or tightened the restrictions a lot, making it impossible to study many pressing research questions. Therefore, some have even called researchers to neglect these TOS, because “in some circumstances the benefits to society from breaching the terms of service outweigh the detriments to the platform itself” Bruns, 2019p.~1561 . Others acknowledge the problem, but doubt that this is a good solution Puschmann, 2019. In general, one needs to distinguish between the act of collecting the data and sharing the data. For instance, in many jurisdictions, there are legal exemptions for collecting data for scientific purposes, but that does not mean that they can be re-distributed as they are Van Atteveldt et al., 2019.

This chapter can by no means replace the consultation of a legal expert and/or an ethics board, but we would like to offer some strategies to minimize potential problems.

Be nice Of course, you could send hundreds of requests per minute (or second) to a website and try to download everything that they have ever published. However, this causes unnecessary load on their servers (and you would probably get blocked). If, on the other hand, you carefully think about what you really need to download, and include a lot of waiting times (for instance, using sys.sleep (R) or time.sleep (Python) so that your script essentially does the same as could be done by hiring a couple of student assistants to copy-paste the data manually, then problems are much less likely to arise.

Collaborate Another way to minimize traffic and server load is to collaborate more. A concerted effort with multiple researchers may lead to less duplicate data and in the end probably an even better, re-usable dataset.

Be extra careful with personal data Both from an ethical and a legal point of view, the situation changes drastically as soon as personal data are involved. Especially since the General Data Protection Regulation (GDPR) regulations took effect in the European Union, collecting and processing such data requires a lot of additional precaution and is usually subject to explicit consent. It is clearly infeasible to ask every Twitter user for consent to process their tweet and doing so is probably covered by research exceptions, the general advice is to store as little personal data as possible and only what is absolutely needed. Most likely, you need to have a data management plan in place, and should get appropriate advice from your legal department. Therefore, think carefully whether you really need, for instance, the user names of the authors of reviews you are going to scrape, or whether the text alone suffices.

Once all ethical and legal concerns are sorted out and you have made sure that you have written a scraper in such a way that it does not cause unnecessary traffic and load on the servers from which you are scraping, and after doing some test runs, it is time to think about how to actually run it on a larger scale. You may already have figured that you probably do not want to run your scraper from a Jupyter Notebook that is constantly open in your browser on your personal laptop. Also here, we would like to offer some suggestions.

Consider using a database Imagine the following scenario: your scraper visits hundreds of websites, collects its results in a list or in a data frame, and after hours of running suddenly crashes – maybe because some element that you were sure must exist on each page, exists only on 999 out of 1000 pages, because a connection timed out, or any other error. Your data is lost, you need to start again (not only annoying, but also undesirable from a traffic minimization point of view). A better strategy may be to immediately write the data for each page to a file. But then, you need to handle a potentially huge number of files later on. A much better approach, especially if you plan to run your scraper repeatedly over a long period of time, is to consider the use of a database in which you dump the results immediately after a page has been scraped (see Section 15.1).

Run your script from the command line Store your scraper as a .py or .R script and run it from your terminal (your command line) by typing python myscript.py or R myscript.R rather than using an IDE such as Spyder or R Studio or a Jupyter Notebook. You may want to have your script print a lot of status information (for instance, which page it is currently scraping), so that you can watch what it is doing. If you want to, you can have your computer run this script in regular intervals (e.g., once an hour). On Linux and MacOS, for instance, you can use a so-called cron job to automate this.

Run your script on a server If your scraper runs for longer than a couple of hours, you may not want to run it on your laptop, especially if your Internet connection is not stable. Instead, you may consider using a server. As we will explain in Section 15.2, it is quite affordable to set up a Linux VM on a cloud computing platform (and next to commercial services, in some countries and institutions there are free services for academics). You can then use tools like nohup or screen to run your script on the background, even if you are no longer connected to the server (see Section 15.2).