Heading here
My First paragraph
My second paragraph
from dsc80_utils import *
Goal: Access information about HDSI faculty members from the HDSI Faculty page.
Let's start by making a GET request to the HDSI Faculty page and see what the resulting HTML looks like.
import requests
fac_response = requests.get('https://datascience.ucsd.edu/faculty/', verify=False)
fac_response
/Users/sam/miniforge3/envs/dsc80/lib/python3.12/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'datascience.ucsd.edu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings warnings.warn(
<Response [200]>
fac_text = fac_response.text
len(fac_text)
248317
print(fac_text[:1000])
<!DOCTYPE html><html lang="en-US"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta http-equiv="X-UA-Compatible" content="IE=edge"><link rel="profile" href="https://gmpg.org/xfn/11" /><title>Faculty – Halıcıoğlu Data Science Institute – UC San Diego</title> <script defer src="data:text/javascript;base64,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
Wow, that is gross looking! 😰
robots.txt
file.robots.txt
file in their root directory, which contains a policy that allows or disallows automatic access to their site.If you make too many requests:
For instance, here's the content of a very basic webpage.
!cat data/lec10_ex1.html
<html> <head> <title>Page title</title> </head> <body> <h1>This is a heading</h1> <p>This is a paragraph.</p> <p>This is <b>another</b> paragraph.</p> </body> </html>
Using IPython.display.HTML
, we can render it directly in our notebook.
from IPython.display import HTML
HTML(filename=Path('data') / 'lec10_ex1.html')
This is a paragraph.
This is another paragraph.
HTML document: The totality of markup that makes up a webpage.
Document Object Model (DOM): The internal representation of an HTML document as a hierarchical tree structure.
HTML element: An object in the DOM, such as a paragraph, header, or title.
HTML tags: Markers that denote the start and end of an element, such as <p>
and </p>
.
Element | Description |
---|---|
<html> |
the document |
<head> |
the header |
<body> |
the body |
<div> |
a logical division of the document |
<span> |
an inline logical division |
<p> |
a paragraph |
<a> |
an anchor (hyperlink) |
<h1>, <h2>, ... |
header(s) |
<img> |
an image |
There are many, many more, but these are by far the most common. See this article for examples.
Tags can have attributes, which further specify how to display information on a webpage.
For instance, <img>
tags have src
and alt
attributes (among others):
<img src="king-selfie.png" alt="A photograph of King Triton." width=500>
Hyperlinks have href
attributes:
Click <a href="https://practice.dsc80.com">this link</a> to access past exams.
What do you think this webpage looks like?
!cat data/lec10_ex2.html
<html> <head> <title>Project 4A and 4B - DSC 80, Spring 2024</title> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css" rel="stylesheet" /> </head> <body> <h1>Project Overview</h1> <img src="../imgs/platter.png" width="200" alt="My dinner last night." /> <p> When the project is released, you can start it by <a href="https://github.com/dsc-courses/dsc80-2024-wi/" >public GitHub repo</a >. </p> <center> <h3> Note that you'll have to submit your notebook as a PDF and a link to your website. </h3> </center> </body> </html>
<div>
tag¶<div style="background-color:lightblue">
<h3>This is a heading</h3>
<p>This is a paragraph.</p>
</div>
The <div>
tag defines a division or a "section" of an HTML document.
<div>
as a "cell" in a Jupyter Notebook.The <div>
element is often used as a container for other HTML elements to style them with CSS or to perform operations involving them using JavaScript.
<div>
elements often have attributes, which are important when scraping!
Under the document object model (DOM), HTML documents are trees. In DOM trees, child nodes are ordered.
What does the DOM tree look like for this document?
To start, we'll work with the source code for an HTML page with the DOM tree shown below:
The string html_string
contains an HTML "document".
html_string = '''
<html>
<body>
<div id="content">
<h1>Heading here</h1>
<p>My First paragraph</p>
<p>My <em>second</em> paragraph</p>
<hr>
</div>
<div id="nav">
<ul>
<li>item 1</li>
<li>item 2</li>
<li>item 3</li>
</ul>
</div>
</body>
</html>
'''.strip()
HTML(html_string)
My First paragraph
My second paragraph
BeautifulSoup
objects¶bs4.BeautifulSoup
takes in a string or file-like object representing HTML (markup
) and returns a parsed document.
import bs4
bs4.BeautifulSoup?
Init signature: bs4.BeautifulSoup( markup='', features=None, builder=None, parse_only=None, from_encoding=None, exclude_encodings=None, element_classes=None, **kwargs, ) Docstring: A data structure representing a parsed HTML or XML document. Most of the methods you'll call on a BeautifulSoup object are inherited from PageElement or Tag. Internally, this class defines the basic interface called by the tree builders when converting an HTML/XML document into a data structure. The interface abstracts away the differences between parsers. To write a new tree builder, you'll need to understand these methods as a whole. These methods will be called by the BeautifulSoup constructor: * reset() * feed(markup) The tree builder may call these methods from its feed() implementation: * handle_starttag(name, attrs) # See note about return value * handle_endtag(name) * handle_data(data) # Appends to the current data node * endData(containerClass) # Ends the current data node No matter how complicated the underlying parser is, you should be able to build a tree using 'start tag' events, 'end tag' events, 'data' events, and "done with data" events. If you encounter an empty-element tag (aka a self-closing tag, like HTML's <br> tag), call handle_starttag and then handle_endtag. Init docstring: Constructor. :param markup: A string or a file-like object representing markup to be parsed. :param features: Desirable features of the parser to be used. This may be the name of a specific parser ("lxml", "lxml-xml", "html.parser", or "html5lib") or it may be the type of markup to be used ("html", "html5", "xml"). It's recommended that you name a specific parser, so that Beautiful Soup gives you the same results across platforms and virtual environments. :param builder: A TreeBuilder subclass to instantiate (or instance to use) instead of looking one up based on `features`. You only need to use this if you've implemented a custom TreeBuilder. :param parse_only: A SoupStrainer. Only parts of the document matching the SoupStrainer will be considered. This is useful when parsing part of a document that would otherwise be too large to fit into memory. :param from_encoding: A string indicating the encoding of the document to be parsed. Pass this in if Beautiful Soup is guessing wrongly about the document's encoding. :param exclude_encodings: A list of strings indicating encodings known to be wrong. Pass this in if you don't know the document's encoding but you know Beautiful Soup's guess is wrong. :param element_classes: A dictionary mapping BeautifulSoup classes like Tag and NavigableString, to other classes you'd like to be instantiated instead as the parse tree is built. This is useful for subclassing Tag or NavigableString to modify default behavior. :param kwargs: For backwards compatibility purposes, the constructor accepts certain keyword arguments used in Beautiful Soup 3. None of these arguments do anything in Beautiful Soup 4; they will result in a warning and then be ignored. Apart from this, any keyword arguments passed into the BeautifulSoup constructor are propagated to the TreeBuilder constructor. This makes it possible to configure a TreeBuilder by passing in arguments, not just by saying which one to use. File: ~/miniforge3/envs/dsc80/lib/python3.12/site-packages/bs4/__init__.py Type: type Subclasses: BeautifulStoneSoup
Normally, we pass the result of a GET request to bs4.BeautifulSoup
, but here we will pass our hand-crafted html_string
.
soup = bs4.BeautifulSoup(html_string)
soup
<html> <body> <div id="content"> <h1>Heading here</h1> <p>My First paragraph</p> <p>My <em>second</em> paragraph</p> <hr/> </div> <div id="nav"> <ul> <li>item 1</li> <li>item 2</li> <li>item 3</li> </ul> </div> </body> </html>
type(soup)
bs4.BeautifulSoup
BeautifulSoup
objects have several useful attributes, e.g. text
:
print(soup.text)
Heading here My First paragraph My second paragraph item 1 item 2 item 3
descendants
¶The descendants
attribute traverses a BeautifulSoup
tree using depth-first traversal.
Why depth-first? Elements closer to one another on a page are more likely to be related than elements further away.
soup.descendants
<generator object Tag.descendants at 0x30c64e400>
for child in soup.descendants:
# print(child) # What would happen if we ran this instead?
if isinstance(child, str):
continue
print(child.name)
html body div h1 p p em hr div ul li li li
Practically speaking, you will not use the descendants
attribute (or the related children
attribute) directly very often. Instead, you will use the following methods:
soup.find(tag)
, which finds the first instance of a tag (the first one on the page, i.e. the first one that DFS sees).soup.find(name=None, attrs={}, recursive=True, text=None, **kwargs)
.soup.find_all(tag)
will find all instances of a tag.find
finds tags!
soup.find('div')
<div id="content"> <h1>Heading here</h1> <p>My First paragraph</p> <p>My <em>second</em> paragraph</p> <hr/> </div>
Let's try and find the <div>
element that has an id
attribute equal to 'nav'
.
soup.find('div', attrs={'id': 'nav'})
<div id="nav"> <ul> <li>item 1</li> <li>item 2</li> <li>item 3</li> </ul> </div>
find
will return the first occurrence of a tag, regardless of its depth in the tree.
# The ul child is not at the top of the tree, but we can still find it.
soup.find('ul')
<ul> <li>item 1</li> <li>item 2</li> <li>item 3</li> </ul>
find_all
¶find_all
returns a list of all matches.
soup.find_all('div')
[<div id="content"> <h1>Heading here</h1> <p>My First paragraph</p> <p>My <em>second</em> paragraph</p> <hr/> </div>, <div id="nav"> <ul> <li>item 1</li> <li>item 2</li> <li>item 3</li> </ul> </div>]
soup.find_all('li')
[<li>item 1</li>, <li>item 2</li>, <li>item 3</li>]
[x.text for x in soup.find_all('li')]
['item 1', 'item 2', 'item 3']
text
attribute of a tag element gets the text between the opening and closing tags.attrs
attribute of a tag element lists all of its attributes.get
method of a tag element gets the value of an attribute.soup.find('p')
<p>My First paragraph</p>
soup.find('p').text
'My First paragraph'
soup.find('div')
<div id="content"> <h1>Heading here</h1> <p>My First paragraph</p> <p>My <em>second</em> paragraph</p> <hr/> </div>
soup.find('div').text
'\nHeading here\nMy First paragraph\nMy second paragraph\n\n'
soup.find('div').attrs
{'id': 'content'}
soup.find('div').get('id')
'content'
The get
method must be called directly on the node that contains the attribute you're looking for.
soup
<html> <body> <div id="content"> <h1>Heading here</h1> <p>My First paragraph</p> <p>My <em>second</em> paragraph</p> <hr/> </div> <div id="nav"> <ul> <li>item 1</li> <li>item 2</li> <li>item 3</li> </ul> </div> </body> </html>
# While there are multiple 'id' attributes, none of them are in the <html> tag at the top.
soup.get('id')
soup.find('div').get('id')
'content'
Code: streams
Consider the following HTML document, which represents a webpage containing the top few songs with the most streams on Spotify today in Canada.
<head>
<title>3*Canada-2022-06-04</title>
</head>
<body>
<h1>Spotify Top 3 - Canada</h1>
<table>
<tr class='heading'>
<th>Rank</th>
<th>Artist(s)</th>
<th>Song</th>
</tr>
<tr class=1>
<td>1</td>
<td>Harry Styles</td>
<td>As It Was</td>
</tr>
<tr class=2>
<td>2</td>
<td>Jack Harlow</td>
<td>First Class</td>
</tr>
<tr class=3>
<td>3</td>
<td>Kendrick Lamar</td>
<td>N95</td>
</tr>
</table>
</body>
Part 1: How many leaf nodes are there in the DOM tree of the previous document — that is, how many nodes have no children?
Part 2: What does the following line of code evaluate to?
len(soup.find_all("td"))
Part 3: What does the following line of code evaluate to?
soup.find("tr").get("class")
Consider quotes.toscrape.com.
Goal: Extract quotes (and relevant metadata) into a DataFrame.
Specifically, let's try to make a DataFrame that looks like the one below:
quote | author | author_url | tags | |
---|---|---|---|---|
0 | “The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.” | Albert Einstein | https://quotes.toscrape.com/author/Albert-Einstein | change,deep-thoughts,thinking,world |
1 | “It is our choices, Harry, that show what we truly are, far more than our abilities.” | J.K. Rowling | https://quotes.toscrape.com/author/J-K-Rowling | abilities,choices |
2 | “There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.” | Albert Einstein | https://quotes.toscrape.com/author/Albert-Einstein | inspirational,life,live,miracle,miracles |
Code: quotes
Ask an LLM to write code to scrape the first ten pages of quotes from https://quotes.toscrape.com/ into a DataFrame called quotes_llm
. The first three rows of quotes_llm
should have the three quotes above. The last row of quotes_llm
should contain a quote from George R.R. Martin.
After having an LLM write code, paste it below and see if it works. If it doesn't work, try to adjust your prompt until it does. Once you have something that works, submit your final prompt and generated code to http://dsc80.com/q.
Eventually, we will create a single function – make_quote_df
– which takes in an integer n
and returns a DataFrame with the quotes on the first n
pages of quotes.toscrape.com.
To do this, we will define several helper functions:
download_page(i)
, which downloads a single page (page i
) and returns a BeautifulSoup
object of the response.
process_quote(div)
, which takes in a <div>
tree corresponding to a single quote and returns a dictionary containing all of the relevant information for that quote.
process_page(divs)
, which takes in a list of <div>
trees corresponding to a single page and returns a DataFrame containing all of the relevant information for all quotes on that page.
Key principle: some of our helper functions will make requests, and others will parse, but none will do both!
def download_page(i):
url = f'https://quotes.toscrape.com/page/{i}'
request = requests.get(url)
return bs4.BeautifulSoup(request.text)
In make_quote_df
, we will call download_page
repeatedly – once for i=1
, once for i=2
, ..., i=n
. For now, we will work with just page 1 (chosen arbitrarily).
soup = download_page(1)
Let's look at the page's source code (right click the page and click "Inspect" in Chrome) to find where the quotes in the page are located.
divs = soup.find_all('div', class_='quote')
# Shortcut for the following, just for when the attribute key is class:
# divs = soup.find_all('div', attrs={'class': 'quote'})
divs[0]
<div class="quote" itemscope="" itemtype="http://schema.org/CreativeWork"> <span class="text" itemprop="text">“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”</span> <span>by <small class="author" itemprop="author">Albert Einstein</small> <a href="/author/Albert-Einstein">(about)</a> </span> <div class="tags"> Tags: <meta class="keywords" content="change,deep-thoughts,thinking,world" itemprop="keywords"/> <a class="tag" href="/tag/change/page/1/">change</a> <a class="tag" href="/tag/deep-thoughts/page/1/">deep-thoughts</a> <a class="tag" href="/tag/thinking/page/1/">thinking</a> <a class="tag" href="/tag/world/page/1/">world</a> </div> </div>
From this <div>
, we can extract the quote, author name, author's URL, and tags.
divs[0]
<div class="quote" itemscope="" itemtype="http://schema.org/CreativeWork"> <span class="text" itemprop="text">“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”</span> <span>by <small class="author" itemprop="author">Albert Einstein</small> <a href="/author/Albert-Einstein">(about)</a> </span> <div class="tags"> Tags: <meta class="keywords" content="change,deep-thoughts,thinking,world" itemprop="keywords"/> <a class="tag" href="/tag/change/page/1/">change</a> <a class="tag" href="/tag/deep-thoughts/page/1/">deep-thoughts</a> <a class="tag" href="/tag/thinking/page/1/">thinking</a> <a class="tag" href="/tag/world/page/1/">world</a> </div> </div>
# The quote.
divs[0].find('span', class_='text').text
'“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”'
# The author.
divs[0].find('small', class_='author').text
'Albert Einstein'
# The URL for the author.
divs[0].find('a').get('href')
'/author/Albert-Einstein'
# The quote's tags.
divs[0].find('meta', class_='keywords').get('content')
'change,deep-thoughts,thinking,world'
Let's implement our next function, process_quote
, which takes in a <div>
corresponding to a single quote and returns a dictionary containing the quote's information.
Why use a dictionary? Passing pd.DataFrame
a list of dictionaries is an easy way to create a DataFrame.
def process_quote(div):
quote = div.find('span', class_='text').text
author = div.find('small', class_='author').text
author_url = 'https://quotes.toscrape.com' + div.find('a').get('href')
tags = div.find('meta', class_='keywords').get('content')
return {'quote': quote, 'author': author, 'author_url': author_url, 'tags': tags}
process_quote(divs[-1])
{'quote': '“A day without sunshine is like, you know, night.”', 'author': 'Steve Martin', 'author_url': 'https://quotes.toscrape.com/author/Steve-Martin', 'tags': 'humor,obvious,simile'}
Our last helper function will take in a list of <div>
s, call process_quote
on each <div>
in the list, and return a DataFrame.
def process_page(divs):
return pd.DataFrame([process_quote(div) for div in divs])
process_page(divs)
quote | author | author_url | tags | |
---|---|---|---|---|
0 | “The world as we have created it is a process ... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | change,deep-thoughts,thinking,world |
1 | “It is our choices, Harry, that show what we t... | J.K. Rowling | https://quotes.toscrape.com/author/J-K-Rowling | abilities,choices |
2 | “There are only two ways to live your life. On... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | inspirational,life,live,miracle,miracles |
... | ... | ... | ... | ... |
7 | “I have not failed. I've just found 10,000 way... | Thomas A. Edison | https://quotes.toscrape.com/author/Thomas-A-Ed... | edison,failure,inspirational,paraphrased |
8 | “A woman is like a tea bag; you never know how... | Eleanor Roosevelt | https://quotes.toscrape.com/author/Eleanor-Roo... | misattributed-eleanor-roosevelt |
9 | “A day without sunshine is like, you know, nig... | Steve Martin | https://quotes.toscrape.com/author/Steve-Martin | humor,obvious,simile |
10 rows × 4 columns
def make_quote_df(n):
'''Returns a DataFrame containing the quotes on the first n pages of https://quotes.toscrape.com/.'''
dfs = []
for i in range(1, n+1):
# Download page n and create a BeautifulSoup object.
soup = download_page(i)
# Create DataFrame using the information in that page.
divs = soup.find_all('div', class_='quote')
df = process_page(divs)
# Append DataFrame to dfs.
dfs.append(df)
# Stitch all DataFrames together.
return pd.concat(dfs).reset_index(drop=True)
quotes = make_quote_df(3)
quotes.head()
quote | author | author_url | tags | |
---|---|---|---|---|
0 | “The world as we have created it is a process ... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | change,deep-thoughts,thinking,world |
1 | “It is our choices, Harry, that show what we t... | J.K. Rowling | https://quotes.toscrape.com/author/J-K-Rowling | abilities,choices |
2 | “There are only two ways to live your life. On... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | inspirational,life,live,miracle,miracles |
3 | “The person, be it gentleman or lady, who has ... | Jane Austen | https://quotes.toscrape.com/author/Jane-Austen | aliteracy,books,classic,humor |
4 | “Imperfection is beauty, madness is genius and... | Marilyn Monroe | https://quotes.toscrape.com/author/Marilyn-Monroe | be-yourself,inspirational |
quotes[quotes['author'] == 'Albert Einstein']
quote | author | author_url | tags | |
---|---|---|---|---|
0 | “The world as we have created it is a process ... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | change,deep-thoughts,thinking,world |
2 | “There are only two ways to live your life. On... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | inspirational,life,live,miracle,miracles |
5 | “Try not to become a man of success. Rather be... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | adulthood,success,value |
12 | “If you can't explain it to a six year old, yo... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | simplicity,understand |
26 | “If you want your children to be intelligent, ... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | children,fairy-tales |
28 | “Logic will get you from A to Z; imagination w... | Albert Einstein | https://quotes.toscrape.com/author/Albert-Eins... | imagination |
The elements in the 'tags'
column are all strings, but they look like lists. This is not ideal, as we will see shortly.
Let's try and extract a list of HDSI Faculty from datascience.ucsd.edu/faculty.
fac_response = requests.get('https://datascience.ucsd.edu/faculty/', verify=False)
fac_response
/Users/sam/miniforge3/envs/dsc80/lib/python3.12/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'datascience.ucsd.edu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings warnings.warn(
<Response [200]>
soup = bs4.BeautifulSoup(fac_response.text)
It's not easy identifying which <div>
s we want. The Inspect tool makes this easier, but it's good to verify that find_all
is finding the right number of elements.
divs = soup.find_all(
class_='vc_grid-item',
)
len(divs)
62
Within here, we need to extract each faculty member's name. It seems like names are stored as text within the <h4>
tag.
divs[0]
<div class="vc_grid-item vc_clearfix col_1-5 vc_grid-item-zone-c-bottom vc_visible-item vc_grid-term-faculty-fellows vc_grid-term-council vc_grid-term-faculty"> <a class="anchor-link" id="ilkay-altintas" name="ilkay-altintas"></a><div class="vc_grid-item-mini vc_clearfix"><div class="vc_gitem-animated-block"><div class="vc_gitem-zone vc_gitem-zone-a vc-gitem-zone-height-mode-auto vc-gitem-zone-height-mode-auto-1-1" style="background-image: url(https://datascience.ucsd.edu/wp-content/uploads/2022/10/ilkayaltintas_headshot.jpg) !important;"><a class="vc_gitem-link vc-zone-link" href="https://datascience.ucsd.edu/people/ilkay-altintas/"></a><img alt="Ilkay Altintas" class="vc_gitem-zone-img" decoding="async" src="https://datascience.ucsd.edu/wp-content/uploads/2022/10/ilkayaltintas_headshot.jpg"/><div class="vc_gitem-zone-mini"></div></div></div><div class="vc_gitem-zone vc_gitem-zone-c"><div class="vc_gitem-zone-mini"><div class="vc_gitem_row vc_row vc_gitem-row-position-top"><div class="vc_col-sm-12 vc_gitem-col vc_gitem-col-align-"><div class="vc_custom_heading vc_gitem-post-data vc_gitem-post-data-source-post_title"><h4 style="text-align: left"><a href="https://datascience.ucsd.edu/people/ilkay-altintas/">Ilkay Altintas</a></h4></div><div class="vc_gitem-align-left fields"><div class="field pendari_people_title">SDSC Chief Data Science Officer & HDSI Founding Faculty Fellow</div></div><div class="excerpt"></div><div class="terms"> Faculty Fellows Council Faculty</div></div></div></div></div></div></div>
divs[0].find('h4').text
'Ilkay Altintas'
We can also extract job titles:
divs[0].find(class_='field').text
'SDSC Chief Data Science Officer & HDSI Founding Faculty Fellow'
Let's create a DataFrame consisting of names and job titles for each faculty member.
names = [div.find('h4').text for div in divs]
names[:10]
['Ilkay Altintas', 'Tiffany Amariuta', 'Mikio Aoi', 'Ery Arias-Castro', 'Vineet Bafna', 'Mikhail Belkin', 'Umesh Bellur', 'Jelena Bradic', 'Henrik Christensen', 'Alex Cloninger']
titles = [div.find(class_='field').text for div in divs]
titles[:10]
['SDSC Chief Data Science Officer & HDSI Founding Faculty Fellow', 'Assistant Professor', 'Assistant Professor', 'Professor', 'Professor', 'Professor', 'Visiting Professor, UCSD HDSI', 'Professor', 'Distinguished Scientist, Professor', 'Associate Professor']
faculty = pd.DataFrame({
'name': names,
'title': titles,
})
faculty.head()
name | title | |
---|---|---|
0 | Ilkay Altintas | SDSC Chief Data Science Officer & HDSI Foundin... |
1 | Tiffany Amariuta | Assistant Professor |
2 | Mikio Aoi | Assistant Professor |
3 | Ery Arias-Castro | Professor |
4 | Vineet Bafna | Professor |
Now we have a DataFrame!
faculty[faculty['title'].str.contains('Teaching') | faculty['title'].str.contains('Lecturer')]
name | title | |
---|---|---|
13 | Justin Eldridge | Associate Teaching Professor |
14 | Shannon Ellis | Associate Teaching Professor |
28 | Marina Langlois | Lecturer |
... | ... | ... |
38 | Giorgio Quer | Lecturer |
46 | Jack Silberman | Lecturer |
50 | Janine Tiefenbruck | Lecturer |
8 rows × 2 columns
What if we want to get faculty members' pictures?
from IPython.display import Image, display
def show_picture(name):
idx = faculty[faculty['name'].str.lower().str.contains(name.lower())].index[0]
display(Image(url=divs[idx].find('img')['src'], width=200, height=200))
show_picture('marina')
Code: streams2
Consider the following HTML document, which represents a webpage containing the top few songs with the most streams on Spotify today in Canada.
<head>
<title>3*Canada-2022-06-04</title>
</head>
<body>
<h1>Spotify Top 3 - Canada</h1>
<table>
<tr class='heading'>
<th>Rank</th>
<th>Artist(s)</th>
<th>Song</th>
</tr>
<tr class=1>
<td>1</td>
<td>Harry Styles</td>
<td>As It Was</td>
</tr>
<tr class=2>
<td>2</td>
<td>Jack Harlow</td>
<td>First Class</td>
</tr>
<tr class=3>
<td>3</td>
<td>Kendrick Lamar</td>
<td>N95</td>
</tr>
</table>
</body>
Part 4: Complete the implementation of the function top_nth
, which takes in a positive integer n
and returns the name of the n-th ranked song in the HTML document. For instance, top_nth(2)
should evaluate to "First Class"
(n=1
corresponds to the top song).
Note: Your implementation should work in the case that the page contains more than 3 songs.
def top_nth(n):
return soup.find("tr", attrs=__(a)__).find_all("td")__(b)__
The spread of true and false news online by Vosoughi et al. compared how true and false news spreads via Twitter:
There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being. To understand how false news spreads, Vosoughi et al. used a data set of rumor cascades on Twitter from 2006 to 2017. About 126,000 rumors were spread by ∼3 million people. False news reached more people than the truth; the top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to more than 1000 people. Falsehood also diffused faster than the truth. The degree of novelty and the emotional reactions of recipients may be responsible for the differences observed.
To conduct this study, the authors used the Twitter API for accessing tweets and web-scraped fact-checking websites to verify whether news was false or not.
soup.find
and soup.find_all
are the functions you will use most often.Regular expressions!