Hello Regular Expression Exercises

A variety of regular expression exercises that are probably way too hard for you to figure out after just one lesson.


To do this assignment, read through the overview:

Overview: A Quick Intro to Regular Expressions

Then read the Regular-Expressions.info tutorial, specifically through capturing groups

Capturing groups is about as complicated as you need to get in regex land, for now. You shouldn’t have to worry about things like lookaheads for this assignment.


There is no one right answer for any of these problems, depending on how you interpret the prompt, and depending on how many assumptions you make. So just try to come up with a pattern that matches something for each prompt – we’ll discuss in class why this is such an (annoyingly) difficult assignment.


Due date:
1:00 PM, Day 3: Tuesday, January 17, 2017 - Practical Pipes
Points Metric
1 Having a correct subject line of yourid::hello-regex
1 Your email is in plaintext
3 Lines 1-10 are the answers to the included regex exercises

Delivery format

Send an email to dun@stanford.edu with the subject:


Where your_sunet_id is your Stanford student ID, all-lowercase.


Sending a plaintext email

Email should have exactly 10 lines and be in plaintext.

The answers to each of the exercises should be one-line only. No need to number them.

Regex Exercises 1 to 4, using the Presidential Debate transcript

The answers to each of the regex exercises should consist of a single line and the regex pattern that satisfies the prompt.

Download this text transcript from the first presidential debate between Trump and Clinton and open it in your text editor: Presidential Debate Transcript, 2016-09-27.

1. All words of 15 letters or more.

2. Each time someone was interrupted.

I’ll just give this to you. In the transcript, an interruption seems to be indicated by a hyphen, or double-hyphen, at the end of the line. But hyphens are also used to indicate pauses in the middle of speech:

Wallace: Thank you secretary Clinton. I want to follow-up-

Trump: Chris, I think it’s -- I think I should respond. First of all, I had a very good meeting with the President of Mexico. Very nice man. We will be doing very much better with Mexico on trade deals. Believe me. The NAFTA deal signed by her husband is one of the worst deals ever made of any kind signed by anybody. It’s a disaster. Hillary Clinton wanted the wall. Hillary Clinton fought for the wall in 2006 or there abouts. Now, she never gets anything done, so naturally the wall wasn't built. But Hillary Clinton wanted the wall.

Wallace: Well, let me --

Trump: We are a country of laws. By the way --

So we need to use the $ anchor to specify that we want only the hyphens in that position.

Here is the pattern that seems to answer this question:


Bonus question: what would be the pattern needed to count interruptions per speaker?

3. When the words “right” or “wrong” were used to end a sentence.

Hint: the end of a sentence is generally indicated by some kind of punctuation. But remember you need to match a literal dot.

4. Each time Trump spoke in 140 or fewer characters.

Doesn’t have to be word characters.

Regex Exercises 5 to 7 with Trump tweets

Download this CSV file of @realDonaldTrump tweets into your text editor

5. All words that are followed by the word, me

We don’t have an easy way to specify just the Text field of each tweet, but that’s ok, the other fields don’t have free-form text.

6. Match the hour of the day that a tweet was sent.

Here’s what a tweet’s timestamp looks like:

2016-12-31 13:17:21 +0000

7. Match every URL that is in the tweet text

Even though the web-verison of each tweet has the URLs full-resolved:


In the simplified data, only the Twitter-t.co-shortened versions are used:

But assume that the URL could have any domain, not just t.co. Better to be safe and lexible than make a bad assumption...

Using the San Francisco HSA 90-day emergency shelter waitlist data

Download this CSV file of emergency shelter waitlist data.

The data as it appears on Socrata can be found here

8. Match every row in which the date of birth was before 1950.

OK, this exercise is meant to show that there are limitations to regexes. We can’t do math with them, for example, e.g. filter the birthdates to be older than 1950.

The best we can do is think of an admittedly clunky hack: what’s another way to describe the set of numbers smaller than 50? Or, for that matter, 5?

9. Capture the month, day, and year of birth for each row.


So this is where you want to read up on capturing groups, which is one of the more complicated things about regex, at least to read about. They’re pretty easy to grok once you’ve seen them in action.

Given that the DOB field is in this format:


Here’s what the pattern without capturing groups looks like:


And here is the answer, with capturing groups for each datapoint:


10. Reformat each date of birth so that they are in YYYY-MM-DD format

Here’s what the original data looks like:


With the correct replacement format, this is the result:



TRUMP: +.{1,140}$
\w+ *me\b


For: 1. All words of 15 letters or more

If we assume all words consist of letters from the American alphabet, then using a character range for all letters from A to Z (uppercase and lowercase) should suffice: [a-Z]{15,}

However, consider all the valid long words that this pattern would exclude:

  • Words split by an apostrophe, e.g. procrastinator's
  • Any word containing non-American alphabet letters, such as é and ô.
  • Compound words, e.g. Merry-go-round

For 8. Match every row in which the date of birth was before 1950.

The first part of th is pattern is straightforward, as we don’t care about specific days/months:


But we do care about limiting the years. My proposed answer was to look for the pattern 19, followed by the digits ranging from 0 to 4, followed by any digit:


But some answers didn’t assume that 1900 was the lower limit for birth year, and that’s a good mindset to have. Yes, in reality, this data covers 90-days of a waitlist in 2016, and no one in America currently is at that age. But, that’s kind of an assumption. Also an assumption: that other data in this realm is limited to this present timeframe.

Thsoe assumptions are totally reasonably within the context of the homework. But when programming, we often can’t make such assumptions.