How to do it...

1. Import re:

>>> import re

2. Match a phone pattern as part of a group (in brackets). Note the use of \d as a special character for any digit:

>>> match = re.search(r'the phone number is ([\d-]+)', '37: the phone number is 1234-567-890')
>>> match.group()
'the phone number is 1234-567-890'
>>> match.group(1)
'1234-567-890'

3. Compile a pattern and capture a case insensitive pattern with a yes|no option:

>>> pattern = re.compile(r'The answer to question (\w+) is (yes|no)', re.IGNORECASE)
>>> pattern.search('Naturaly, the answer to question 3b is YES')
<_sre.SRE_Match object; span=(10, 42), match='the answer to question 3b is YES'>
>>> _.groups()
('3b', 'YES')

4. Match all the occurrences of cities and state abbreviations in the text. Note that they are separated by a single character and the name of the city always starts with an uppercase letter. Only four states are matched for simplicity:

>>> PATTERN = re.compile(r'([A-Z][\w\s]+).(TX|OR|OH|MI)')
>>> TEXT ='the jackalopes are the team of Odessa,TX while the knights are native of Corvallis OR and the mud hens come from Toledo.OH; the whitecaps have their base in Grand Rapids,MI'
>>> list(PATTERN.finditer(TEXT))
[<_sre.SRE_Match object; span=(31, 40), match='Odessa,TX'>, <_sre.SRE_Match object; span=(73, 85), match='Corvallis OR'>, <_sre.SRE_Match object; span=(113, 122), match='Toledo.OH'>, <_sre.SRE_Match object; span=(157, 172), match='Grand Rapids,MI'>]
>>> _[0].groups()
('Odessa', 'TX')