Automating Text Cleaning

I am fundamentally ambivalent about the automation of text-cleaning: spending time with the data, by getting unexpected results from your attempts at normalization strikes me as one way to get to know the data and to be in a position to do better analysis. That noted, there have been a number of interesting text-cleaning libraries, or text-cleaning functionality built into analytic libraries, that have caught my attention over the past year or so. The most recent of these is clean-text. Installation is simple:

pip install clean-text

And then:

from clean-text import clean

The clean(the_string, *parameters*) takes a number of interesting parameters that focus on a particular array of difficulties:

Quick Labels with Python’s f-string

Sometimes I need a list of titles or labels for a project on which I am working. E.g., I am working with a toy dataset and I’ve created a 10 x 10 array and I want to give the rows and columns headers so I can try slicing and dicing. I prefer human-readable/thinkable names for headers, loc over iloc in pandas-speak. And this one-liner works a treat, as they say:

labels = [label{item}' for item in range(1,11)]

Done. Place it into your dataframe creation (as below) and you are good to go.

df = pd.DataFrame(data=scores, index=names, columns=labels)

Flattening a List in Python

There has to be a more elegant, and pythonic, way to do this, but none of my experiments with nested list comprehensions or with itertool’s chain function worked.

What I started with is a function that creates a list of sentences, each of which is a list of words from a text (string):

def sentience (the_string):
    sentences = [
            [word.lower() for word in nltk.word_tokenize(sentence)]
            for sentence in nltk.sent_tokenize(the_string)
        ]
    return sentences

But in the current moment, I didn’t need all of a text, but only two sentences to examine with the NLTK’s part-of-speech tagger. nltk.pos_tag(text), however, only accepts a flat list of words. So I needed to flatten my lists of lists into one list, and I only needed, in this case, the first two sentences:

test = []
for i in range(len(text2[0:2])): #the main list
    for j in range (len(text2[i])): #the sublists
        test.append(text2[i][j]) 

I’d still like to make this a single line of code, a nested list comprehension, but, for now, this works.

Useful Pandas Posts

Please note that this post is, yes, “under construction” as I compile various notes from across my file system and decide what’s worth keeping here and what’s going into the virtual trash bin.

If, like me, you are not very familiar with R and thus you do not readily grasp how pandas brings much of R’s coolness to Python data analysis workflows, then having the occasional overview and/or cheat sheet on hand is useful.

For overviews, I found the following really helpful in understanding how pandas organizes data and the methods available for working with it:

For quick tips that border on almost being cheat sheets, there is Chris Albon’s “Technical Notes on Using Data Science & Artificial Intelligence to Fight for Something That Matters”, at the bottom of which is a compendium of great tutorials and tips on using pandas. (And as you scroll, you glimpse a lot of other really useful stuff as well.)

Python and PDFs

Real Python has a tutorial on How to Work With a PDF in Python. I subscribe to Real Python because I find their tutorials well-written or, in the case of video tutorials, well-presented. The focus of this tutorial is the PythonPDF module, which can get metadata from a PDF, rotate pages, merge or split a PDF, and/or encrypt it. While the tutorial mentions “extract information” it does not mean PythonPDF can get text from a PDF that does not have a text layer already embedded on its pages — you could argue that the unintuitive nature of PDFs reveals their brokenness but that’s for another time. If you want to get text where there is no text layer, but you still want to use Python, it looks like you have to turn to PDFMiner — though a quick skim of its GH page doesn’t reveal if it has OCR capabilities backed in. Sigh.

Understanding How Beautiful Soup Works

Two years ago, when I first grabbed the transcripts of the TED talks, using wget, I relied upon the wisdom and generosity of Padraic C on StackOverflow to help me use Python’s BeautifulSoup library to get the data out of the downloaded HTML files that I wanted. Now that Katherine Kinnaird and I have decided to add talks published since then, and perhaps even go so far as to re-download the entire corpus so that everything is as much the same as possible, it was time for me to understand how BeautifulSoup (hereafter BS4) works for myself.

from bs4 import BeautifulSoup

# NB: no need to read() the file: BS4 does that
thesoup = BeautifulSoup(open("transcript.0.html"), "html5lib")

# Talk metadata is in <meta> tags in the <head>.
# This finds all <meta> tags
metas = thesoup.find_all("meta")

# Let's see what this object is...
print(type(metas))

Output: <class 'bs4.element.ResultSet'>, and we can interact with it as if it were a list. Thus, metas[0] yields: <meta charset="utf-8"/>, which is the first of a long line of <meta tags. (The complete output is at the bottom of this note below under the heading Appendix A.)

type(metas[0]) outputs: <class 'bs4.element.Tag'>. That means we will need to understand how to select items within a BS4 Tag. The items we are interested in are towards the bottom of the result set:

<meta content="Good news in the fight against pancreatic cancer" itemprop="name"/>
<meta content="Anyone who has lost a loved one to pancreatic cancer knows the devastating speed with which it can affect an otherwise healthy person. TED Fellow and biomedical entrepreneur Laura Indolfi is developing a revolutionary way to treat this complex and lethal disease: a drug delivery device that acts as a cage at the site of a tumor, preventing it from spreading and delivering medicine only where it's needed. &quot;We are hoping that one day we can make pancreatic cancer a curable disease,&quot; she says." itemprop="description"/>
<meta content="PT6M3S" itemprop="duration"/>
<meta content="2016-05-17T14:46:20+00:00" itemprop="uploadDate"/>
<meta content="1246654" itemprop="interactionCount"/>
<meta content="Laura Indolfi" itemprop="name"/>

This gives us the slug, the description, the run time, the publication date, the number of hits, and the speaker. So, the question is, how do we navigate the “parse tree” so that we turn up the value of the content attributes when the value of the itemprop attribute is one of the above?

[meta.attrs for meta in metas] returns a list of dictionaries, with each meta its own dictionary. Here is a small sample from the larger list:

{'content': 'PT6M3S', 'itemprop': 'duration'},
{'content': '2016-05-17T14:46:20+00:00', 'itemprop': 'uploadDate'},
{'content': '1246654', 'itemprop': 'interactionCount'},
{'content': 'Laura Indolfi', 'itemprop': 'name'},

What we need to do is identify the dictionary’s position in the list by finding those dictionaries that have the values duration, etc. We then use that position to slice to that dictionary, and get the value associated with content, yes?

It turns out that the best way to do this is built into BS4, though the method was not immediately obvious. One of the answers to the StackOverflow question “Get meta tag content property with BeautifulSoup and Python” suggested the following possibility:

for tag in thesoup.find_all("meta"):
    if tag.get("name", None) == "author":
        speaker = tag.get("content", None)
    if tag.get("itemprop", None) == "duration":
        length = tag.get("content", None)
    if tag.get("itemprop", None) == "uploadDate":
        published = tag.get("content", None)
    if tag.get("itemprop", None) == "interactionCount":
        views = tag.get("content", None)
    if tag.get("itemprop", None) == "description":
        description = tag.get("content", None)

If we ask to see these values with print(speaker, length, published, views, description), we get:

Laura Indolfi PT6M3S 2016-05-17T14:46:20+00:00 1246654 Anyone
who has lost a loved one to pancreatic cancer knows the devastating
speed with which it can affect an otherwise healthy person. TED
Fellow and biomedical entrepreneur Laura Indolfi is developing a
revolutionary way to treat this complex and lethal disease: a drug
delivery device that acts as a cage at the site of a tumor,
preventing it from spreading and delivering medicine only where
it's needed. "We are hoping that one day we can make pancreatic
cancer a curable disease," she says.

Now we need to get the text of the talk out, which is made somewhat difficult by the lack of semantic markup. The start of the text looks like this:

<!-- Transcript text -->
  <div class="Grid Grid--with-gutter d:f@md p-b:4">
    <div class="Grid__cell d:f h:full m-b:.5 m-b:0@md w:12"></div>

    <div class="Grid__cell flx-s:1 p-r:4">

The only reliable thing is the comment tag: there’s also a closing one at the end of the transcript text, so if we can find some way to select all the <p> tags between the two comments, I think we’ll be in good shape.

Appendix A

The output of [print(meta) for meta in metas] is:

<meta charset="utf-8"/>
<meta content="TED Talk Subtitles and Transcript: Anyone who has lost a loved one to pancreatic cancer knows the devastating speed with which it can affect an otherwise healthy person. TED Fellow and biomedical entrepreneur Laura Indolfi is developing a revolutionary way to treat this complex and lethal disease: a drug delivery device that acts as a cage at the site of a tumor, preventing it from spreading and delivering medicine only where it's needed. &quot;We are hoping that one day we can make pancreatic cancer a curable disease,&quot; she says." name="description"/>
<meta content="Laura Indolfi" name="author"/>
<meta content='Transcript of "Good news in the fight against pancreatic cancer"' property="og:title"/>
<meta content="https://pi.tedcdn.com/r/talkstar-photos.s3.amazonaws.com/uploads/70d551c2-1e5c-411e-b926-7d72590f66bb/LauraIndolfi_2016U-embed.jpg?c=1050%2C550&amp;w=1050" property="og:image"/>
<meta content="https://pi.tedcdn.com/r/talkstar-photos.s3.amazonaws.com/uploads/70d551c2-1e5c-411e-b926-7d72590f66bb/LauraIndolfi_2016U-embed.jpg?c=1050%2C550&amp;w=1050" property="og:image:secure_url"/>
<meta content="1050" property="og:image:width"/>
<meta content="550" property="og:image:height"/>
<meta content="article" property="og:type"/>
<meta content="TED, Talks, Themes, Speakers, Technology, Entertainment, Design" name="keywords"/>
<meta content="#E62B1E" name="theme-color"/>
<meta content="True" name="HandheldFriendly"/>
<meta content="320" name="MobileOptimized"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<meta content="TED Talks" name="apple-mobile-web-app-title"/>
<meta content="yes" name="apple-mobile-web-app-capable"/>
<meta content="black" name="apple-mobile-web-app-status-bar-style"/>
<meta content="TED Talks" name="application-name"/>
<meta content="https://www.ted.com/browserconfig.xml" name="msapplication-config"/>
<meta content="#000000" name="msapplication-TileColor"/>
<meta content="on" http-equiv="cleartype"/>
<meta content="Laura Indolfi: Good news in the fight against pancreatic cancer" name="title"/>
<meta content="TED Talk Subtitles and Transcript: Anyone who has lost a loved one to pancreatic cancer knows the devastating speed with which it can affect an otherwise healthy person. TED Fellow and biomedical entrepreneur Laura Indolfi is developing a revolutionary way to treat this complex and lethal disease: a drug delivery device that acts as a cage at the site of a tumor, preventing it from spreading and delivering medicine only where it's needed. &quot;We are hoping that one day we can make pancreatic cancer a curable disease,&quot; she says." property="og:description"/>
<meta content="https://www.ted.com/talks/laura_indolfi_good_news_in_the_fight_against_pancreatic_cancer/transcript" property="og:url"/>
<meta content="201021956610141" property="fb:app_id"/>
<meta content="Good news in the fight against pancreatic cancer" itemprop="name"/>
<meta content="Anyone who has lost a loved one to pancreatic cancer knows the devastating speed with which it can affect an otherwise healthy person. TED Fellow and biomedical entrepreneur Laura Indolfi is developing a revolutionary way to treat this complex and lethal disease: a drug delivery device that acts as a cage at the site of a tumor, preventing it from spreading and delivering medicine only where it's needed. &quot;We are hoping that one day we can make pancreatic cancer a curable disease,&quot; she says." itemprop="description"/>
<meta content="PT6M3S" itemprop="duration"/>
<meta content="2016-05-17T14:46:20+00:00" itemprop="uploadDate"/>
<meta content="1246654" itemprop="interactionCount"/>
<meta content="Laura Indolfi" itemprop="name"/>
<meta content="Flash HTML5" itemprop="playerType"/>
<meta content="640" itemprop="width"/>
<meta content="360" itemprop="height"/>