1. Regular Expressions in Python (110m) Flashcards

Question 1

Q

There is a file called names.txt containing the following text:

 Love, Kenneth	kenneth@teamtreehouse.com	(555) 555-5555	Teacher, Treehouse	@kennethlove
McFarland, Dave	dave@teamtreehouse.com	(555) 555-5554	Teacher, Treehouse
Arthur, King	king_arthur@camelot.co.uk		King, Camelot
Österberg, Sven-Erik	governor@norrbotten.co.se		Governor, Norrbotten	@sverik
, Tim	tim@killerrabbit.com		Enchanter, Killer Rabbit Cave
Carson, Ryan	ryan@teamtreehouse.com	(555) 555-5543	CEO, Treehouse	@ryancarson
Doctor, The	doctor+companion@tardis.co.uk		Time Lord, Gallifrey
Exampleson, Example	me@example.com	555-555-5552	Example, Example Co.	@example
Obama, Barack	president.44@us.gov	555 555-5551	President, United States of America	@potus44
Chalkley, Andrew	andrew@teamtreehouse.com	(555) 555-5553	Teacher, Treehouse	@chalkers
Vader, Darth	darth-vader@empire.gov	(555) 555-4444	Sith Lord, Galactic Empire	@darthvader
Fernández de la Vega Sanz, María Teresa	mtfvs@spain.gov		First Deputy Prime Minister, Spanish Govt.

The following codes are typed in sequence to explain few concepts, you are going to explain me each sequence of codes line by line:

Sequence 1:

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

print(data)

Sequence 2:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

#r means raw string
print(re.match(r'Love', data))
print(re.match(r'Kenneth', data))

Sequence 3:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

print(re.match(r'Love', data))
print(re.search(r'Kenneth', data))

Sequence 4:
~~~
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

last_name = r’Love’
first_name = r’Kenneth’
print(re.match(last_name, data))
print(re.search(first_name, data))
~~~

Answer

A

Certainly! Let’s go through each sequence of codes line by line and provide a detailed explanation:

Sequence 1:

```python
names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

print(data)
~~~

Explanation:
1. The code opens the file named ‘names.txt’ using the open() function and assigns the file object to the variable names_file.
2. The encoding='utf-8' argument specifies that the file should be read with UTF-8 encoding, which is a widely used character encoding for text files.
3. The read() method is called on the names_file object to read the entire contents of the file and store it as a string in the data variable.
4. The close() method is called on the names_file object to close the file and release system resources.
5. Finally, the code prints the contents of the data variable, which contains the entire text read from the file.

This sequence reads the contents of the ‘names.txt’ file and displays the entire text.

Sequence 2:

```python
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

r means raw string
print(re.match(r’Love’, data))
print(re.match(r’Kenneth’, data))
~~~

Explanation:
1. The import re statement imports the re module, which is required for working with regular expressions.
2. Similar to the previous sequence, the code opens the ‘names.txt’ file, reads its contents, and stores it in the data variable.
3. The close() method is called on the names_file object to close the file.
4. The re.match() function is used to check if the pattern 'Love' matches the beginning of the data string. The r before the pattern indicates that it is a raw string, which treats backslashes as literal characters. The re.match() function returns a match object if there is a match, or None otherwise. The result of this match is printed.
5. Similarly, the re.match() function is used to check if the pattern 'Kenneth' matches the beginning of the data string. The result is printed.

This sequence demonstrates the usage of re.match() to check if a pattern matches the beginning of a string. It shows that the pattern 'Love' matches the beginning of the data string, while the pattern 'Kenneth' does not.

Sequence 3:

```python
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

print(re.match(r’Love’, data))
print(re.search(r’Kenneth’, data))
~~~

Explanation:
1. This sequence is similar to the previous one, with the addition of the re.search() function.
2. The code opens the ‘names.txt’ file, reads its contents, and stores it in the data variable, just like before.
3. The close() method is called on the names_file object to close the file.
4. The re.match() function is used to check if the pattern 'Love' matches the beginning of the data string, just like in the previous sequence.
5. The re.search() function is used to search the data string for the pattern 'Kenneth'. It returns a match object if the pattern is found anywhere in the string, or None otherwise.
6. The results of both the `re

.match() and re.search()` operations are printed.

This sequence demonstrates the usage of both re.match() and re.search() functions for pattern matching. It shows that the pattern 'Love' matches the beginning of the data string, and the pattern 'Kenneth' is found later in the string using re.search().

Sequence 4:

```python
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

last_name = r’Love’
first_name = r’Kenneth’
print(re.match(last_name, data))
print(re.search(first_name, data))
~~~

Explanation:
1. This sequence is similar to the previous one but introduces the use of variables to store the patterns to be matched.
2. The import re statement ensures that the re module is available for regular expression operations.
3. The ‘names.txt’ file is opened, read, and its contents are stored in the data variable, just like before.
4. The close() method is called on the names_file object to close the file.
5. The last_name variable is assigned the value 'Love', and the first_name variable is assigned the value 'Kenneth'. These variables store the patterns to be matched.
6. The re.match() function is used to check if the pattern stored in the last_name variable matches the beginning of the data string, just like in the previous sequences.
7. The re.search() function is used to search the data string for the pattern stored in the first_name variable.
8. The results of both the re.match() and re.search() operations are printed.

This sequence demonstrates the usage of variables to store patterns and how they can be used with re.match() and re.search() functions for pattern matching. It shows that both 'Love' and 'Kenneth' are found in the data string. The r before the patterns indicates that they are raw strings, treating backslashes as literal characters.

Question 2

Q

How will you open and read files in Python? Let’s say there is file called names.txt, how will you open it and read in Python?

Answer

A

To open and read a file in Python, you can follow these steps:

Use the open() function to open the file. Pass the file name (including the file extension) as a string argument to open(). Optionally, you can specify the file mode and encoding.
```
python
file = open('names.txt', 'r', encoding='utf-8')
```
Read the contents of the file using the read() method, which is called on the file object returned by open(). This method reads the entire contents of the file as a string.
```
python
content = file.read()
```
Close the file to release system resources using the close() method called on the file object.
```
python
file.close()
```

Here’s the complete code for opening and reading the ‘names.txt’ file:

```python
file = open(‘names.txt’, ‘r’, encoding=’utf-8’)
content = file.read()
file.close()

print(content)
~~~

In this code, the file is opened in read mode ('r'), and the encoding='utf-8' argument is used to ensure proper reading of the file’s contents. The read() method is then called on the file object to read the entire file content and store it in the content variable. Finally, the file is closed using the close() method.

Remember to handle exceptions, such as FileNotFoundError, when working with file operations to ensure your code handles unexpected situations gracefully.

In Python REPL, you can do the following:

python
open('names.txt' encoding='utf-8')

Treehouse Notes:
“New Terms
open() - Opens a file in Python. This won’t contain the content of the file, it just points to it in memory.
.read() - Reads the entire contents of the file object it’s called on.
.close() - Closes the file object it’s called on. This clears the file out of Python’s memory.
r’string’ - A raw string that makes writing regular expressions easier.
re.match(pattern, text, flags) - Tries to match a pattern against the beginning of the text.
re.search(pattern, text, flags) - Tries to match a pattern anywhere in the text. Returns the first match.
A better way to read files
If you don’t know the size of a file, it’s better to read it a chunk at a time and close it automatically. The following snippet does that:

with open("some_file.txt") as open_file:
    data = open_file.read()

The with causes the file to automatically close once the action inside of it finishes. And the action inside, the .read(), will finish when there are no more bytes to read from the file.

Why didn’t I cover this in the video? There’s more going on here, behind-the-scenes, and I think it’s better to know the long way first.”

Question 3

Q

Challenge 1:
Use open() to load the file “basics.txt” into the variable file_object.

Challenge 2:
Read the contents of file_object into a new variable named data.

Challenge 3:
Now close the file_object file so it isn’t taking up memory.

Challenge 4:
Import re. Create an re.match() for the word “Four” in the data variable. Assign this to a new variable named first.

Challenge 5:
Finally, make a new variable named liberty that is an re.search() for the word “Liberty” in our data variable.

Answer

A

The answer to the challenge is:

import re

file_object = open('basics.txt', encoding='utf-8') #challenge 1
data = file_object.read() #challenge 2
file_object.close() #challenge 3

first = re.match('Four', data) #challenge 4
liberty = re.search('Liberty', data) #challenge 5

Question 4

Q

What is unicode word character?

Answer

A

A Unicode word character, often referred to as \w in regular expressions, represents any character that can be part of a word in any language. It includes alphanumeric characters (letters and digits) as well as certain connector punctuation marks used within words, such as underscores. The exact definition of a word character may vary depending on the regex engine or programming language being used, but generally, it encompasses a wide range of characters to accommodate different writing systems and languages.

Unicode is a computing standard that provides a unique numeric code for every character used in writing systems around the world. It is designed to be a universal character encoding standard, encompassing a vast range of characters from different languages, scripts, symbols, and special characters. The Unicode standard aims to ensure consistent representation and handling of text across different computer systems and software applications.

By assigning a unique code point to each character, Unicode enables the interchange, processing, and display of text in various languages and scripts, regardless of the platform or software being used. It supports a broad range of writing systems, including Latin, Cyrillic, Arabic, Chinese, Japanese, Korean, and many others. Unicode is widely adopted and implemented by modern programming languages, operating systems, and software applications to facilitate multilingual support and internationalization.

Here are a few examples of characters and their corresponding Unicode code points:

The letter “A”: U+0041
The Euro symbol “€”: U+20AC
The hiragana letter “あ” (pronounced “a” in Japanese): U+3042
The Greek letter “Ω” (Omega): U+03A9
The Arabic digit “٧” (representing the number 7): U+0667
The smiley face emoji “😊”: U+1F60A

These examples illustrate how Unicode assigns a unique code point to each character, allowing different characters from various scripts and languages to be represented and processed consistently in digital systems.

Question 5

Q

Explain each sequence of the code line-by-line and in detail with results:

Sequence 1:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
#print(re.match(last_name, data))
#print(re.search(first_name, data))
print(re.match('\w, \w', data))

Sequence 2:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
print(re.search('\d\d\d-\d\d\d\d', data))

Sequence 3:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
print(re.search(r'\(\d\d\d\) \d\d\d-\d\d\d\d', data))

While the file: names.txt contains:
~~~
Love, Kenneth kenneth@teamtreehouse.com (555) 555-5555 Teacher, Treehouse @kennethlove
McFarland, Dave dave@teamtreehouse.com (555) 555-5554 Teacher, Treehouse
Arthur, King king_arthur@camelot.co.uk King, Camelot
Österberg, Sven-Erik governor@norrbotten.co.se Governor, Norrbotten @sverik
, Tim tim@killerrabbit.com Enchanter, Killer Rabbit Cave
Carson, Ryan ryan@teamtreehouse.com (555) 555-5543 CEO, Treehouse @ryancarson
Doctor, The doctor+companion@tardis.co.uk Time Lord, Gallifrey
Exampleson, Example me@example.com 555-555-5552 Example, Example Co. @example
Obama, Barack president.44@us.gov 555 555-5551 President, United States of America @potus44
Chalkley, Andrew andrew@teamtreehouse.com (555) 555-5553 Teacher, Treehouse @chalkers
Vader, Darth darth-vader@empire.gov (555) 555-4444 Sith Lord, Galactic Empire @darthvader
Fernández de la Vega Sanz, María Teresa mtfvs@spain.gov First Deputy Prime Minister, Spanish Govt.
~~~

Answer

A

Sequence 1:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
#print(re.match(last_name, data))
#print(re.search(first_name, data))
print(re.match('\w, \w', data))

The code imports the re module, which provides support for regular expressions.
The ‘names.txt’ file is opened in read mode with the specified encoding of ‘utf-8’, and the file object is assigned to names_file.
The entire content of the file is read into the data variable using the read() method.
The file is closed with the close() method called on the names_file object to release system resources.
The variable last_name is assigned the value ‘Love’ as a raw string.
The variable first_name is assigned the value ‘Kenneth’ as a raw string.
The line print(re.match('\w, \w', data)) attempts to match a pattern consisting of a word character, followed by a comma and a space, and then another word character against the data string using the re.match() function.
The output will be None because the pattern ‘\w, \w’ does not match any portion of the data string.

Sequence 2:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
print(re.search('\d\d\d-\d\d\d\d', data))

Similar to Sequence 1, the re module is imported, and the ‘names.txt’ file is opened and read into the data variable.
The last_name and first_name variables are assigned values.
The line print(re.search('\d\d\d-\d\d\d\d', data)) searches for a pattern that matches three digits, followed by a hyphen, and then four digits within the data string using the re.search() function.
The output will be the first occurrence of a matching pattern in the data string, which is the phone number ‘555-5555’.

Sequence 3:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'
print(re.search(r'\(\d\d\d\) \d\d\d-\d\d\d\d', data))

The code imports the re module and opens the ‘names.txt’ file.
The file contents are read into the data variable, and the file is closed.
The variables last_name and first_name are assigned values.
The line print(re.search(r'$\d\d\d$ \d\d\d-\d\d\d\d', data)) searches for a pattern that matches a phone number pattern of the form ‘(###) ###-####’ within the data string using the re.search() function. The $ and $ escape sequences are used to match the literal parentheses, while \d represents a digit.
The output will be the first occurrence of a matching pattern in the data string, which is the phone number ‘(555) 555-5555’.

TreeHouse Notes:
New terms:
1. \w - matches an Unicode word character. That’s any letter, uppercase or lowercase, numbers, and the underscore character. In “new-releases-204”, \w would match each of the letters in “new” and “releases” and the numbers 2, 0, and 4. It wouldn’t match the hyphens.
2. \W - is the opposite to \w and matches anything that isn’t an Unicode word character. In “new-releases-204”, \W would only match the hyphens.
3. \s - matches whitespace, so spaces, tabs, newlines, etc.
4. \S - matches everything that isn’t whitespace.
5. \d - is how we match any number from 0 to 9
6. \D - matches anything that isn’t a number.
7. \b - matches word boundaries. What’s a word boundary? It’s the edges of word, defined by white space or the edges of the string.
8. \B - matches anything that isn’t the edges of a word.

Two other escape characters that we didn’t cover in the video are \A and \Z. These match the beginning and the end of the string, respectively. As we’ll learn later, though, ^ and $ are more commonly used and usually what you actually want.”

Question 6

Q

I want to use parentheses within a string, how to use escape to create a () within a string in Python? And how do to the same with raw-string?

Answer

A

To use parentheses within a string in Python, you can use the backslash \ to escape them. Here’s an example:

```python
my_string = “I want to use (parentheses) within a string.”
print(my_string)
~~~

Output:

I want to use (parentheses) within a string.

By using the backslash \ before each parenthesis, they are treated as literal characters within the string instead of having their usual syntactic meaning in Python.

In Python, you can use a raw string to include parentheses within a string without explicitly escaping them. Raw strings, denoted by the r prefix, treat backslashes \ as literal characters instead of escape characters. Here’s an example:

```python
my_string = r”I want to use (parentheses) within a string.”
print(my_string)
~~~

Output:

I want to use (parentheses) within a string.

By prefixing the string with r, you indicate that it is a raw string, and backslashes are treated as regular characters. Therefore, you don’t need to escape the parentheses or any other special characters within the string.

Question 7

Q

Why this three lines are not giving same results?

print(re.search(r'\(\d\d\d\) \d\d\d-\d\d\d\d', data))
print(re.search('\(\d\d\d\) \d\d\d-\d\d\d\d', data))
print(re.search(r'(\d\d\d) \d\d\d-\d\d\d\d', data))

Answer

A

Apologies for the confusion in my previous response. Let’s analyze the three lines individually:

print(re.search(r'$\d\d\d$ \d\d\d-\d\d\d\d', data))This line uses a raw string with the r prefix. It successfully matches a phone number pattern of the form (###) ###-#### within the data string, capturing the digits in the parentheses and the subsequent digits separated by a hyphen.
print(re.search('$\d\d\d$ \d\d\d-\d\d\d\d', data))This line uses a regular string without the r prefix. The backslashes are used to escape the parentheses and the special sequences \d. However, since regular strings interpret backslashes as escape characters, the expression '$\d\d\d$ \d\d\d-\d\d\d\d' is not correctly interpreted as intended. The backslashes are treated as escape characters rather than literal backslashes, resulting in an invalid regular expression pattern. This line will not match the desired phone number pattern correctly.
print(re.search(r'(\d\d\d) \d\d\d-\d\d\d\d', data))This line uses a raw string with the r prefix. It does not escape the parentheses, allowing them to be treated as literal characters. It successfully matches a phone number pattern of the form ### ###-#### within the data string, capturing the digits separated by a space and a hyphen. However, it does not capture the digits within parentheses. The parentheses are interpreted as grouping parentheses in the regular expression, affecting the behavior of capturing groups.

To summarize, the first line correctly matches the desired phone number pattern and captures the digits within parentheses. The second line fails to match the pattern due to incorrect escaping of backslashes. The third line matches a similar pattern but does not capture the digits within parentheses.

Question 8

Q

I want to match the 5 in the string. What escape character should I use?

re.search(r'\_\_\_\_', 'Johnny-5')

Answer

A

To match the digit ‘5’ in the string ‘Johnny-5’, you can use the following regular expression pattern:

```python
re.search(r’\d’, ‘Johnny-5’)
~~~

In this pattern, \d is the escape sequence for matching any digit. When used within a regular expression pattern, it matches any single digit character.

By using re.search() with the pattern \d, it will search for the first occurrence of a digit in the provided string. In this case, it will match and return the ‘5’ in ‘Johnny-5’.

Question 9

Q

Challenge 1: Write a function named first_number that takes a string as an argument. The function should search, with a regular expression, the first number in the string and return the match object.

import re

Challenge 2:
Now, write a function named numbers() that takes two arguments: a count as an integer and a string. Return an re.search for exactly count numbers in the string. Remember, you can multiply strings and integers to create your pattern.

For example: r"\w" * 5 would create r"\w\w\w\w\w".

Answer

A

Solution to Challenge 1:

import re
def first_number(string_1):
    string_1 = re.search('\d', string_1)
    return string_1

Solution to Challenge 2:

import re
def first_number(string_1):
    string_1 = re.search('\d', string_1)
    return string_1

def numbers(count, user_string):
    return re.search(r'\d' * count, user_string)

The question consists of two challenges related to regular expressions in Python.

Challenge 1:
The first challenge asks you to write a function called first_number that takes a string as an argument. The goal of this function is to search for the first number in the given string using a regular expression and return the match object.

Challenge 2:
The second challenge requires you to write a function named numbers that takes two arguments: count (an integer) and user_string (a string). The objective is to return a regular expression search for exactly count numbers in the user_string. To construct the pattern, you can multiply the string ‘\d’ (which matches any digit) by the count integer.

Certainly! Let’s dive into a more detailed explanation of the answer to each challenge.

Solution to Challenge 1:

```python
import re

def first_number(string_1):
string_1 = re.search(‘\d’, string_1)
return string_1
~~~

In this solution, the re module is imported to gain access to the regular expression functionalities in Python. The first_number function takes a parameter string_1, which represents the input string to be searched.

Inside the function, the re.search function is used to perform a regular expression search. The regular expression used is ‘\d’, which matches any digit character (0-9). It looks for the first occurrence of a digit in the input string string_1.

The re.search function returns a match object if a match is found or None if no match is found. In this solution, the match object is assigned back to the variable string_1.

Finally, the function returns the match object string_1. This means that when you call first_number with a string as an argument, it will return the match object representing the first number found in that string.

Solution to Challenge 2:

```python
import re

def first_number(string_1):
string_1 = re.search(‘\d’, string_1)
return string_1

def numbers(count, user_string):
return re.search(‘\d’ * count, user_string)
~~~

The solution to Challenge 2 builds upon the solution to Challenge 1. The re module is imported, and the first_number function from Challenge 1 is included as well.

The numbers function takes two parameters: count (an integer) and user_string (a string). Its purpose is to find exactly count numbers in the user_string.

Inside the function, the regular expression search is performed using the pattern ‘\d’ multiplied by count. The multiplication is achieved by using the * operator between the string ‘\d’ and the count integer.

For example, if count is 3, the pattern will be ‘\d’ * 3, which results in the string ‘\d\d\d’. This pattern matches three consecutive digits.

The re.search function is then called with the constructed pattern and the user_string as arguments. It searches for the first occurrence of the pattern in the user_string and returns a match object.

Finally, the function numbers returns the match object returned by re.search('\d' * count, user_string). This means that when you call numbers with an integer and a string as arguments, it will return the match object representing the first occurrence of count consecutive digits found in the user_string.

By utilizing regular expressions, the numbers function provides a flexible and powerful way to search for a specific count of numbers in a given string.

Question 10

Q

sequence 1

The names.txt contains the following:

Love, Kenneth	kenneth@teamtreehouse.com	(555) 555-5555	Teacher, Treehouse	@kennethlove
McFarland, Dave	dave@teamtreehouse.com	(555) 555-5554	Teacher, Treehouse
Arthur, King	king_arthur@camelot.co.uk		King, Camelot
Österberg, Sven-Erik	governor@norrbotten.co.se		Governor, Norrbotten	@sverik
, Tim	tim@killerrabbit.com		Enchanter, Killer Rabbit Cave
Carson, Ryan	ryan@teamtreehouse.com	(555) 555-5543	CEO, Treehouse	@ryancarson
Doctor, The	doctor+companion@tardis.co.uk		Time Lord, Gallifrey
Exampleson, Example	me@example.com	555-555-5552	Example, Example Co.	@example
Obama, Barack	president.44@us.gov	555 555-5551	President, United States of America	@potus44
Chalkley, Andrew	andrew@teamtreehouse.com	(555) 555-5553	Teacher, Treehouse	@chalkers
Vader, Darth	darth-vader@empire.gov	(555) 555-4444	Sith Lord, Galactic Empire	@darthvader
Fernández de la Vega Sanz, María Teresa	mtfvs@spain.gov		First Deputy Prime Minister, Spanish Govt.

Explain the following code contained within address_book.py sequence by sequence with detailed explanation using the results it might give when run.

address_book.py:
~~~
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

last_name = r’Love’
first_name = r’Kenneth’
#print(re.match(last_name, data))
#print(re.search(first_name, data))
#print(re.match(‘\w, \w’, data))
#print(re.search(‘\d\d\d-\d\d\d\d’, data))

print(re.search(r’(\d\d\d) \d\d\d-\d\d\d\d’, data))
print(re.search(r”\w+, \w+”, data))

print(re.search(r’(\d{3}) \d{3}-\d{4}’, data)) # using count numbers instead of typing \d multiple times

print(re.search(r’(?\d{3})? \d{3}-\d{4}’, data)) # making parenthesis optional by using ?

print(re.findall(r’(?\d{3})? \d{3}-\d{4}’, data)) # using findall to search through entire

print(re.findall(r’(?\d{3})?-?\s?\d{3}-\d{4}’, data)) # hiphen that is optional -? and space optional by using \s?

print(re.findall(r”\w+, \w+”, data)) #using findall to findout all names

print(re.findall(r”\w*, \w+”, data)) #using * instead of + to denote it is either 0 or appears infinite number of times
~~~

Answer

A

Let’s go through the code sequence by sequence and explain each step along with the potential results it might give when run.

Sequence 1:

```python
print(re.search(r’(\d\d\d) \d\d\d-\d\d\d\d’, data))
print(re.search(r”\w+, \w+”, data))
~~~

In this sequence, two re.search statements are used to search for specific patterns in the data string.

The first re.search statement uses the pattern r'$\d\d\d$ \d\d\d-\d\d\d\d'. This pattern is looking for a specific phone number format enclosed in parentheses. It matches three digits within parentheses, followed by a space, three digits, a hyphen, and four more digits. It represents a typical phone number format like (555) 555-5555. The re.search function searches for the first occurrence of this pattern in the data string.

The second re.search statement uses the pattern r"\w+, \w+". This pattern is searching for names in the format of last name followed by a comma, space, and then the first name. It matches one or more word characters (letters, digits, or underscores) for both the last name and the first name. The re.search function searches for the first occurrence of this pattern in the data string.

The potential results when running this sequence would be:
- The first re.search statement may find and return the first phone number matching the pattern r'$\d\d\d$ \d\d\d-\d\d\d\d' in the data string.
- The second re.search statement may find and return the first occurrence of a name in the format “Last Name, First Name” matching the pattern r"\w+, \w+" in the data string.

Sequence 2:

```python
print(re.search(r’(\d{3}) \d{3}-\d{4}’, data))
~~~

In this sequence, a single re.search statement is used to search for a specific phone number pattern in the data string.

The re.search statement uses the pattern r'$\d{3}$ \d{3}-\d{4}'. This pattern matches a phone number format enclosed in parentheses. It expects three digits within parentheses, followed by a space, three digits, a hyphen, and four more digits. The curly braces with numbers, like \d{3}, denote the number of times the preceding element (digit in this case) should occur. It represents a typical phone number format like (555) 555-5555. The re.search function searches for the first occurrence of this pattern in the data string.

The potential result when running this sequence would be the re.Match object representing the first phone number that matches the pattern r'$\d{3}$ \d{3}-\d{4}' in the data string.

Sequence 3:

```python
print(re.search(r’(?\d{3})? \d{3}-\d{4}’, data))
~~~

In this sequence, a single re.search statement is used to search for a phone number pattern in the data string. This time, the parentheses in the phone number are made optional.

The re.search statement uses the pattern r'$?\d{3}$? \d{3}-\d{4}'. The ? after the parentheses makes them optional. This pattern matches phone numbers with or without parentheses around the area code. It expects three

digits, followed by an optional opening parenthesis, the three-digit area code, an optional closing parenthesis, a space, three digits, a hyphen, and four more digits. The re.search function searches for the first occurrence of this pattern in the data string.

The potential result when running this sequence would be the re.Match object representing the first phone number that matches the pattern r'$?\d{3}$? \d{3}-\d{4}' in the data string.

Sequence 4:

```python
print(re.findall(r’(?\d{3})? \d{3}-\d{4}’, data))
~~~

In this sequence, the re.findall function is used to search for all occurrences of the phone number pattern in the data string.

The re.findall function uses the pattern r'$?\d{3}$? \d{3}-\d{4}', which matches phone numbers with or without parentheses around the area code. It expects three digits, followed by an optional opening parenthesis, the three-digit area code, an optional closing parenthesis, a space, three digits, a hyphen, and four more digits. The re.findall function searches for all occurrences of this pattern in the data string and returns them as a list.

The potential result when running this sequence would be a list containing all the phone numbers that match the pattern r'$?\d{3}$? \d{3}-\d{4}' in the data string.

Sequence 5:

```python
print(re.findall(r’(?\d{3})?-?\s?\d{3}-\d{4}’, data))
~~~

In this sequence, the re.findall function is used to search for all occurrences of a modified phone number pattern in the data string.

The re.findall function uses the pattern r'$?\d{3}$?-?\s?\d{3}-\d{4}'. This pattern matches phone numbers with or without parentheses around the area code, an optional hyphen after the parentheses, an optional space after the hyphen, and the rest of the phone number. The re.findall function searches for all occurrences of this pattern in the data string and returns them as a list.

The potential result when running this sequence would be a list containing all the phone numbers that match the pattern r'$?\d{3}$?-?\s?\d{3}-\d{4}' in the data string. This pattern allows flexibility in the formatting of phone numbers, allowing optional hyphens and spaces.

Sequence 6:

```python
print(re.findall(r”\w+, \w+”, data))
~~~

In this sequence, the re.findall function is used to search for all occurrences of the name pattern in the data string.

The re.findall function uses the pattern r"\w+, \w+", which matches names in the format “Last Name, First Name”. It searches for all occurrences of this pattern in the data string and returns them as a list.

The potential result when running this sequence would be a list containing all the names that match the pattern r"\w+, \w+" in the data string. Each name in the list will be in the format “Last Name, First Name”.

Sequence 7:

```python
print(re.findall(r”\w*, \w+”, data))
~~~

In this sequence, the re.findall function is used to search for all occurrences of a modified name pattern in the data string.

The re.findall function uses the pattern r"\w*, \w+", which matches names in the format “Last Name, First Name” but allows an optional comma after the Last Name. It searches for all occurrences of this pattern in the data string and returns them as a list.

The potential result when running this sequence would be a list containing all the names that match the pattern r"\w*, \w+" in the data string. Each name in the list will be in the format “Last Name, First Name”, with the Last Name possibly having an optional comma.

Question 11

Q

What is the difference between re.findall and re.search?

Answer

A

The main difference between re.findall and re.search lies in their functionality and the type of results they return.

re.findall:
- re.findall is a function provided by the re module in Python.
- It is used to search for all occurrences of a pattern in a string.
- It returns a list of all non-overlapping matches found in the string.
- Each element of the list represents a match as a string.
- If no matches are found, it returns an empty list.

re.search:
- re.search is also a function provided by the re module in Python.
- It is used to search for the first occurrence of a pattern in a string.
- It returns an re.Match object that represents the first match found.
- The re.Match object contains information about the match, such as the matched substring, its starting and ending positions, and other useful methods.
- If no match is found, it returns None.

In summary, the key differences between re.findall and re.search are:
- re.findall returns a list of all matches, while re.search returns only the first match.
- re.findall returns strings representing each match, while re.search returns an re.Match object.
- re.search is useful when you only need the first match or want access to additional match information through the re.Match object.
- re.findall is useful when you need to find all occurrences of a pattern in a string and process them as a list of strings.

Question 12

Q

Challenge: Create a function named phone_numbers that takes a string and returns all of the phone numbers in the string using re.findall(). The phone numbers will all be in the format 555-555-5555.

import re

Answer

A

Answer to Challenge:
~~~
import re
def phone_numbers(user_string):
return re.findall(r’\d{3}-\d{3}-\d{4}’, user_string)
~~~

Question 13

Q

Challenge: Create a function named find_words that takes a count and a string. Return a list of all of the words in the string that are count word characters long or longer.

import re

EXAMPLE:
# >>> find_words(4, "dog, cat, baby, balloon, me")
# ['baby', 'balloon']

Question 14

Q

»> find_words(4, “dog, cat, baby, balloon, me”)

Challenge: Create a function named find_words that takes a count and a string. Return a list of all of the words in the string that are count word characters long or longer.

import re

EXAMPLE:
# >>> find_words(4, "dog, cat, baby, balloon, me")
# ['baby', 'balloon']

Answer

A

[‘baby’, ‘balloon’]

Answer to the Challenge:

import re

def find_words(count, string):
    return re.findall(r'\b\w{' + str(count) + r',}\b', string)
                     
print(find_words(4, "dog, cat, baby, balloon, me"))

Explanation:
Certainly! Let’s break down the code step by step, assuming no prior knowledge of regular expressions:

```python
import re
~~~

This line imports the re module, which provides support for regular expressions in Python. Regular expressions are a powerful tool for pattern matching and manipulation of strings.

```python
def find_words(count, string):
return re.findall(r’\b\w{‘ + str(count) + r’,}\b’, string)
~~~

This code defines a function named find_words that takes two arguments: count and string. The purpose of this function is to find and return a list of words from the string that are at least count characters long.

The line return re.findall(r'\b\w{' + str(count) + r',}\b', string) is the core of the function. Let’s break it down further:

re.findall is a function from the re module that searches for all non-overlapping occurrences of a pattern in a string and returns them as a list.
r'\b\w{' + str(count) + r',}\b' is the regular expression pattern that is used to match the words in the string. Let’s analyze this pattern step by step:
- \b denotes a word boundary. It matches the empty string at the beginning or end of a word.
- \w matches any word character (alphanumeric or underscore).
- {count,} specifies that \w should occur at least count times or more consecutively.
- \b denotes another word boundary to ensure that only complete words are matched.
str(count) converts the count variable to a string so that it can be concatenated with the regular expression pattern.
string is the input string where we want to find the words.

The find_words function returns the list of matched words found by re.findall. These are the words in the string that are at least count characters long.

```python
print(find_words(4, “dog, cat, baby, balloon, me”))
# [‘baby’, ‘balloon’]
~~~

This line calls the find_words function with count equal to 4 and string equal to "dog, cat, baby, balloon, me". It then prints the returned result.

The expected output is ['baby', 'balloon']. This means that the function has successfully found the words “baby” and “balloon” in the input string that are at least 4 characters long.

In summary, the find_words function uses regular expressions to extract words from a string based on a minimum length criterion. It returns a list of those words, and when called with a specific input, it prints the list ['baby', 'balloon'] as the output.

Other ways to Solve this problem:
There are multiple ways to solve the challenge of finding words in a string that are count word characters long or longer. Here are a few alternative approaches:

Approach 1: Using a loop and string manipulation

```python
def find_words(count, user_string):
words = user_string.split()
result = []
for word in words:
if len(word) >= count:
result.append(word)
return result
~~~

In this approach, the input string is split into words using the split() function. Then, a loop iterates over each word, checking if its length is greater than or equal to the desired count. If so, the word is added to the result list. Finally, the function returns the result list.

Approach 2: Using list comprehension

```python
def find_words(count, user_string):
words = user_string.split()
return [word for word in words if len(word) >= count]
~~~

This approach utilizes list comprehension to achieve the same result as Approach 1. It creates a new list that contains only the words from the input string that satisfy the length condition.

Approach 3: Using regex word boundaries with a quantifier

```python
import re

def find_words(count, user_string):
pattern = r’\b\w{‘ + str(count) + r’,}\b’
return re.findall(pattern, user_string)
~~~

This approach, which was already provided in the original answer, uses regular expressions to match words based on the count. The regular expression pattern consists of word boundaries (\b) and a quantifier ({count,}) to specify the desired word length. The re.findall function is then used to find all matching words in the input string.

These are just a few examples of different approaches to solve the challenge. The choice of approach depends on factors such as personal preference, familiarity with the techniques, and the specific requirements of the project or problem at hand.

Question 15

Q

Code 1:

return re.findall(r'\b\w{' + str(count) + r',}\b', user_string)

Code 2:

return re.findall(r"\b\w{' + str(count) + r',}\b", user_string)

What is the difference between above two codes and why I cant use the one with “” and get correct output?

Answer

A

The difference between the two lines of code you provided is the quotation marks used for defining the regular expression pattern.

In the first line:

```python
return re.findall(r’\b\w{‘ + str(count) + r’,}\b’, user_string)
~~~

The regular expression pattern is enclosed in single quotation marks ('). This is the correct way to define a regular expression pattern in Python. The r prefix before the string indicates a raw string, which allows backslashes to be treated as literal characters.

In the second line:

```python
return re.findall(r”\b\w{‘ + str(count) + r’,}\b”, user_string)
~~~

The regular expression pattern is enclosed in double quotation marks ("). In Python, double quotation marks are used to define regular strings, and the backslash \ is interpreted as an escape character. Therefore, the backslashes in the regular expression pattern are treated as escape characters, not as literal backslashes.

To properly define the regular expression pattern, you should use single quotation marks (') as shown in the first line of code. Using single quotation marks ensures that the regular expression pattern is interpreted correctly and matches the desired word length.

Therefore, the correct code is:

```python
return re.findall(r’\b\w{‘ + str(count) + r’,}\b’, user_string)
~~~

Using the correct code, the function will return the expected list of words that are count word characters long or longer from the input string.

Question 16

Q

Teacher’s Notes:
Explain the following:
Counts: Exact matches and escape patterns give us flexibility for what we’re looking for, but when something occurs multiple times, we have to specify it multiple times. Counts makes Python do that work for us.

\w{3} - matches any three-word characters in a row.
\w{,3} - matches 0, 1, 2, or 3-word characters in a row.
\w{3,} - matches 3 or more word characters in a row. There’s no upper limit.
\w{3, 5} - matches 3, 4, or 5-word characters in a row.
\w? - matches 0 or 1-word characters.
\w* - matches 0 or more word characters. Since there is no upper limit, this is, effectively, infinite word characters.
\w+ - matches 1 or more word characters. Like *, it has no upper limit but must occur at least once.
.findall(pattern, text, flags) - Finds all non-overlapping occurrences of the pattern in the text.

Answer

A

Certainly! Here are examples for each term to help illustrate their usage:

\w{3}: Suppose we have the text “Hello, how are you?” The pattern \w{3} would match the three-word characters “Hel” in “Hello” and “are” in “are”, because they occur consecutively.
\w{,3}: Let’s consider the text “OpenAI is amazing!”. The pattern \w{,3} would match the following sequences of word characters: “Op” in “Open”, “AI” in “AI”, “is” in “is”, and “ing” in “amazing”.
\w{3,}: If we have the text “I love programming in Python!”, the pattern \w{3,} would match the following sequences of three or more word characters: “lov” in “love”, “programming” in “programming”, and “Python” in “Python”.
\w{3, 5}: Consider the text “The quick brown fox jumps over the lazy dog.” The pattern \w{3,5} would match the following sequences of 3, 4, or 5 word characters: “qui” in “quick”, “brow” in “brown”, “fox” in “fox”, “jump” in “jumps”, “over” in “over”, “lazy” in “lazy”, and “dog” in “dog”.
\w?: Suppose we have the text “OpenAI”. The pattern \w? would match each individual word character, resulting in the matches “O”, “p”, “e”, “n”, “A”, and “I”.
\w*: Let’s consider the text “Hello123”. The pattern \w* would match the entire sequence of word characters, “Hello123”, because it allows for zero or more occurrences of word characters.
\w+: Consider the text “I have 5 apples.”. The pattern \w+ would match the following sequences of one or more word characters: “I” in “I”, “have” in “have”, “5” in “5”, and “apples” in “apples”.
.findall(pattern, text, flags): Let’s assume we have the pattern \d+ (matches one or more digits) and the text “I bought 3 books and 2 pens.”. Calling .findall(pattern, text) would return a list with the matches [‘3’, ‘2’] as it finds all non-overlapping occurrences of one or more digits in the text.

These examples demonstrate how each term would be applied in practical scenarios, helping to clarify their usage in pattern matching and text searching.

Question 17

Q

What is counts in python?

Answer

A

In Python, count is a method that is available for certain data types, such as lists, tuples, and strings. It is used to count the occurrences of a specific element within the given object.

Here’s how you can use the count method for different data types:

Lists:

python
my_list = [1, 2, 3, 3, 4, 3]
count = my_list.count(3)
print(count)  # Output: 3

Tuples:

python
my_tuple = (1, 2, 3, 3, 4, 3)
count = my_tuple.count(3)
print(count)  # Output: 3

Strings:

python
my_string = "Hello, Hello, Hello"
count = my_string.count("Hello")
print(count)  # Output: 3

In each case, the count method returns the number of occurrences of the specified element within the object. If the element is not found, it returns 0.

Question 18

Q

What is sets in python?

Answer

A

In Python, a set is an unordered collection of unique elements. It is defined by enclosing a comma-separated list of elements within curly braces {} or by using the set() function. Sets are mutable, meaning their elements can be modified, and they are iterable.

Here’s an example of creating a set in Python:

```python
my_set = {1, 2, 3, 4, 5}
print(my_set) # Output: {1, 2, 3, 4, 5}
~~~

Alternatively, you can use the set() function to create a set:

```python
my_set = set([1, 2, 3, 4, 5])
print(my_set) # Output: {1, 2, 3, 4, 5}
~~~

Sets automatically remove duplicate elements, so if you try to create a set with duplicate values, they will be reduced to a single occurrence:

```python
my_set = {1, 2, 2, 3, 3, 4, 5, 5}
print(my_set) # Output: {1, 2, 3, 4, 5}
~~~

Sets support various operations and methods for common set operations, such as adding elements, removing elements, checking membership, performing set operations like union, intersection, difference, etc. Some of the common operations and methods on sets include:

Adding elements:

python
my_set.add(6)
print(my_set)  # Output: {1, 2, 3, 4, 5, 6}

Removing elements:

python
my_set.remove(3)
print(my_set)  # Output: {1, 2, 4, 5, 6}

Checking membership:

python
print(2 in my_set)  # Output: True
print(3 in my_set)  # Output: False

Set operations:
```python
set1 = {1, 2, 3}
set2 = {3, 4, 5}
union_set = set1.union(set2)
print(union_set) # Output: {1, 2, 3, 4, 5}intersection_set = set1.intersection(set2)
print(intersection_set) # Output: {3}difference_set = set1.difference(set2)
print(difference_set) # Output: {1, 2}
```

Sets are particularly useful when you need to store a collection of elements while ensuring uniqueness, or when you need to perform set operations efficiently.

Question 19

Q

Explain the following code in detail:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

print(re.findall(r'[-\w\d+.]+@[-\w\d.]+', data)) #finding email IDs
print(re.findall(r'\b[trehous]{9}\b', data, re.IGNORECASE))

Answer

A

Sure! Let’s break down the code step by step:

Importing the re module:
The code starts by importing the re module, which stands for regular expressions. This module provides functions and methods for working with regular expressions in Python.
Opening and reading a file:
The code opens a file named ‘names.txt’ using the open() function. The file is opened in text mode with the specified encoding of ‘utf-8’. The resulting file object is assigned to the variable names_file.The read() method is then called on the names_file object to read the entire contents of the file and store it in the variable data.Finally, the close() method is called on the names_file object to close the file and free up system resources.
Finding email IDs:
The code uses the re.findall() function to find all occurrences of email IDs in the data variable. The regular expression pattern r'[-\w\d+.]+@[-\w\d.]+' is used for this purpose.Breaking down the regular expression pattern:
- [-\w\d+.]+: Matches one or more occurrences of characters that can be either a hyphen, word characters (letters, digits, or underscore), plus symbol, or dot.
- @: Matches the ‘@’ symbol.
- [-\w\d.]+: Matches one or more occurrences of characters that can be either a hyphen, word characters (letters, digits, or underscore), or dot.The resulting email IDs are printed using the print() function.
Finding specific words:
The code uses the re.findall() function again to find all occurrences of specific words in the data variable. The regular expression pattern r'\b[trehous]{9}\b' is used, along with the re.IGNORECASE flag.Breaking down the regular expression pattern:
- \b: Represents a word boundary, ensuring that the match is a complete word.
- [trehous]{9}: Matches exactly nine occurrences of the characters ‘t’, ‘r’, ‘e’, ‘h’, ‘o’, ‘u’, or ‘s’. The square brackets [] denote a character class, and the curly braces {} specify the exact number of occurrences.
- \b: Represents a word boundary, ensuring that the match is a complete word.The re.IGNORECASE flag is used as the third argument to perform a case-insensitive search.The resulting matched words are printed using the print() function.

Overall, this code reads the contents of a file, searches for email IDs and specific words using regular expressions, and prints the results. Regular expressions provide a powerful and flexible way to search and manipulate text patterns in Python.

Yes, you are correct. The re.IGNORECASE flag in the re.findall() function can also be written as re.I. Both of these forms are used to perform a case-insensitive search when using regular expressions in Python.

So, in the given code, you can replace re.IGNORECASE with re.I without changing the functionality. Here’s the updated code snippet:

```python
import re

names_file = open(‘names.txt’, encoding=’utf-8’)
data = names_file.read()
names_file.close()

print(re.findall(r’[-\w\d+.]+@[-\w\d.]+’, data)) # Finding email IDs
print(re.findall(r’\b[trehous]{9}\b’, data, re.I)) # Finding specific words (case-insensitive)
~~~

Using re.I is a shorthand notation for the re.IGNORECASE flag and can make the code a bit more concise. Both forms achieve the same result of performing a case-insensitive search when using regular expressions with the re.findall() function.

Treehouse Notes:
New terms
* [abc] - this is a set of the characters ‘a’, ‘b’, and ‘c’. It’ll match any of those characters, in any order, but only once each.
* [a-z], [A-Z], or [a-zA-Z] - ranges that’ll match any/all letters in the English alphabet in lowercase, uppercase, or both upper and lowercases.
* [0-9] - range that’ll match any number from 0 to 9. You can change the ends to restrict the set.

Question 20

Q

Challenge: Create a function named find_emails that takes a string. Return a list of all of the email addresses in the string.

sets_email.py:
~~~
import re

Example:
#&raquo_space;> find_email(“kenneth.love@teamtreehouse.com, @support, ryan@teamtreehouse.com, test+case@example.co.uk”)
# [‘kenneth@teamtreehouse.com’, ‘ryan@teamtreehouse.com’, ‘test@example.co.uk’]
~~~

Answer

A

Answer to Challenge:

import re
def find_emails(string):
    return re.findall(r'[-\w\d+.]+@[-\w\d+.]+', string)

find_emails("kenneth.love@teamtreehouse.com, @support, ryan@teamtreehouse.com, test+case@example.co.uk")
# ['kenneth@teamtreehouse.com', 'ryan@teamtreehouse.com', 'test@example.co.uk']

Sure! Let’s break down the code line by line:

import re:
This line imports the re module, which stands for regular expressions. It provides functions and methods for working with regular expressions in Python.
def find_emails(string)::
This line defines a function named find_emails that takes a single parameter string. This function will be used to find email addresses within the given string.
return re.findall(r'[-\w\d+.]+@[-\w\d+.]+', string):
This line uses the re.findall() function from the re module to search for all occurrences of email addresses within the string parameter. The regular expression pattern r'[-\w\d+.]+@[-\w\d+.]+' is used for this purpose.Breaking down the regular expression pattern:
- [-\w\d+.]+: Matches one or more occurrences of characters that can be either a hyphen, word characters (letters, digits, or underscore), plus symbol, or dot.
- @: Matches the ‘@’ symbol.
- [-\w\d+.]+: Matches one or more occurrences of characters that can be either a hyphen, word characters (letters, digits, or underscore), plus symbol, or dot.The re.findall() function returns a list of all the matches found within the string.
find_emails("kenneth.love@teamtreehouse.com, @support, ryan@teamtreehouse.com, test+case@example.co.uk"):
This line calls the find_emails function and passes a string as an argument. The given string contains multiple email addresses separated by commas.
# ['kenneth@teamtreehouse.com', 'ryan@teamtreehouse.com', 'test@example.co.uk']:
This line is a comment that shows the expected output of the find_emails function call. It indicates that the function is expected to return a list containing three email addresses: ‘kenneth@teamtreehouse.com’, ‘ryan@teamtreehouse.com’, and ‘test@example.co.uk’.

Overall, this code defines a function named find_emails that uses regular expressions to search for email addresses within a given string. It then demonstrates the usage of this function by calling it with a sample string and prints the expected output as a comment.

Question 21

Q

Explain the following code in Detail:

import re

data = [
    {
        'name': 'Love, Kenneth',
        'email': 'kenneth@teamtreehouse.com',
        'phone': '(555) 555-5555',
        'position': 'Teacher, Treehouse',
        'twitter': '@kennethlove'
    },
    {
        'name': 'McFarland, Dave',
        'email': 'dave@teamtreehouse.com',
        'phone': '(555) 555-5554',
        'position': 'Teacher, Treehouse',
        'twitter': None
    },
    {
        'name': 'Arthur, King',
        'email': 'king_arthur@camelot.co.uk',
        'phone': None,
        'position': 'King, Camelot',
        'twitter': None
    },
    {
        'name': 'Österberg, Sven-Erik',
        'email': 'governor@norrbotten.co.se',
        'phone': None,
        'position': 'Governor, Norrbotten',
        'twitter': '@sverik'
    },
    {
        'name': ', Tim',
        'email': 'tim@killerrabbit.com',
        'phone': None,
        'position': 'Enchanter, Killer Rabbit Cave',
        'twitter': None
    },
    {
        'name': 'Carson, Ryan',
        'email': 'ryan@teamtreehouse.com',
        'phone': '(555) 555-5543',
        'position': 'CEO, Treehouse',
        'twitter': '@ryancarson'
    },
    {
        'name': 'Doctor, The',
        'email': 'doctor+companion@tardis.co.uk',
        'phone': None,
        'position': 'Time Lord, Gallifrey',
        'twitter': None
    },
    {
        'name': 'Exampleson, Example',
        'email': 'me@example.com',
        'phone': '555-555-5552',
        'position': 'Example, Example Co.',
        'twitter': '@example'
    },
    {
        'name': 'Obama, Barack',
        'email': 'president.44@us.gov',
        'phone': '555 555-5551',
        'position': 'President, United States of America',
        'twitter': '@potus44'
    },
    {
        'name': 'Chalkley, Andrew',
        'email': 'andrew@teamtreehouse.com',
        'phone': '(555) 555-5553',
        'position': 'Teacher, Treehouse',
        'twitter': '@chalkers'
    },
    {
        'name': 'Vader, Darth',
        'email': 'darth-vader@empire.gov',
        'phone': '(555) 555-4444',
        'position': 'Sith Lord, Galactic Empire',
        'twitter': '@darthvader'
    },
    {
        'name': 'Fernández de la Vega Sanz, María Teresa',
        'email': 'mtfvs@spain.gov',
        'phone': None,
        'position': 'First Deputy Prime Minister, Spanish Govt.',
        'twitter': None
    }
]

print(re.findall(r'''
		\b@[-\w\d.]* #Find a word boundary, an @ and any no of characters
    [^gov\t]+ #Ignore 1+ instance of the letters g, o, v and a tab
    \b #Match another word boundary
''', data, re.VERBOSE|re.I))

Answer

A

Certainly! Let’s break down the code snippet step by step:

Importing the re module:
The code starts by importing the re module, which stands for regular expressions. This module provides functions and methods for working with regular expressions in Python.
Defining the data list of dictionaries:
The data variable is a list that contains multiple dictionaries, where each dictionary represents information about a person. Each dictionary has keys like ‘name’, ‘email’, ‘phone’, ‘position’, and ‘twitter’, and their corresponding values represent the respective person’s information.
Printing email IDs leaving out IDs with “.gov”:
This is the main part of the code that searches for and prints the email IDs, excluding those with the “.gov” domain.
```
python
print(re.findall(r'''
    \b@[-\w\d.]*  # Find a word boundary, an "@" symbol, and any number of characters (alphanumeric, underscore, hyphen, or dot)
    [^gov\t]+     # Ignore one or more instances of the letters "g", "o", "v", and a tab character
    \b            # Match another word boundary
''', data, re.VERBOSE | re.I))
```
Breaking down the regular expression pattern:
- \b@[-\w\d.]*: Matches a word boundary, followed by an “@” symbol, and then matches any number of characters that can be alphanumeric, underscore, hyphen, or dot.
- [^gov\t]+: Matches one or more occurrences of any character that is not “g”, “o”, “v”, or a tab character.
- \b: Represents a word boundary.The re.findall() function is used to find all matches of the pattern within the data variable. It returns a list of all the matched email IDs that meet the specified criteria.The re.VERBOSE flag is used to enable verbose mode, allowing the regular expression pattern to be written in a more readable and multiline format.The re.I flag, also known as re.IGNORECASE, is used for case-insensitive matching, meaning it will match email IDs regardless of whether the letters are uppercase or lowercase.The resulting list of email IDs is printed using the print() function.

In summary, this code uses regular expressions to search for email IDs within the data list of dictionaries and prints only the email IDs that do not have a “.gov” domain.

In the regular expression pattern [^gov\t]+, the ^ symbol has a special meaning inside square brackets []. It acts as a negation or negated character class.

Here’s what the pattern [^gov\t]+ means:

[^gov\t]: Matches any single character that is not “g”, “o”, “v”, or a tab character (\t).
+: Matches one or more occurrences of the preceding pattern.

So, in the context of the code, [^gov\t]+ is used to match one or more characters that are not “g”, “o”, “v”, or a tab character.

For example, in the email domain “teamtreehouse.com”, the characters “t”, “e”, “a”, “m”, “t”, “r”, “e”, and “h” would match this pattern because they are not “g”, “o”, or “v”. However, the characters “g”, “o”, and “v” in the domain “example.gov” would not match this pattern because they are included in the negation character class.

This pattern is used to exclude email IDs with the “.gov” domain from being matched, as specified in the code.

Question 22

Q

Explain the following code in detail:

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

print(re.findall(r"""
    \b[-\w]+,  # find a word boundary, 1+ hyphen or characters, and a comma
    \s         # find 1 whitespace
    [-\w ]+    # 1+ hyphens and characters and explicit spaces
    [^\t\n]    # ignore tabs and newlines
""", data, re.X))

Answer

A

Certainly! Let’s break down the code step by step and explain each part in detail:

import re: This line imports the regular expression (regex) module from the Python standard library. It allows us to use regex functions and patterns for text matching and manipulation.
names_file = open('names.txt', encoding='utf-8'): This line opens a file named “names.txt” in the current directory. The open() function is used to open a file in Python. The file is opened in read mode ('r'), and the encoding='utf-8' parameter specifies that the file should be read using the UTF-8 character encoding, which supports a wide range of characters.
data = names_file.read(): This line reads the contents of the file opened in the previous step and assigns it to the variable data. The read() method is called on the file object (names_file) to retrieve the entire contents of the file as a string.
names_file.close(): This line closes the file that was opened in step 2. It is good practice to close files after you’re done working with them to free up system resources and ensure data integrity.
re.findall(r""" ... """, data, re.X): This line performs a regex search using the re.findall() function. It searches for matches of the provided regex pattern in the data string.
- r""" ... """: This is a multiline string that contains the regex pattern. The r prefix indicates that it is a raw string, which means backslashes are treated as literal characters and not escape characters.
- re.X: This is an optional flag that allows for more readable regex patterns by ignoring whitespace and allowing comments within the pattern. It’s also known as the re.VERBOSE flag.
print(...): This line prints the result of the re.findall() function. The findall() function returns a list of all non-overlapping matches of the regex pattern found in the data string.
- The regex pattern itself is explained in the comments within the multiline string. It consists of several components:
  - \b[-\w]+,: Matches a word boundary (\b), followed by 1 or more hyphens or word characters ([-\w]+), and a comma (,).
  - \s: Matches a single whitespace character.
  - [-\w ]+: Matches 1 or more hyphens, word characters, or explicit spaces.
  - [^\t\n]: Matches any character except tabs and newlines.
- The print() function outputs the list of matches to the console.

Overall, this code reads the contents of a file named “names.txt,” performs a regex search using the provided pattern, and prints the resulting matches.

Treehouse Notes:
New terms
* [^abc] - a set that will not match, and exclude, the letters ‘a’, ‘b’, and ‘c’.
* re.IGNORECASE or re.I - flag to make a search case-insensitive. re.match(‘A’, ‘apple’, re.I) would find the ‘a’ in ‘apple’.
* re.VERBOSE or re.X - flag that allows regular expressions to span multiple lines and contain (ignored) whitespace and comments.

Question 23

Q

Challenge: Create a variable named good_numbers that is an re.findall() where the pattern matches anything in string except the numbers 5, 6, and 7.

negate.py:
~~~
import re

string = ‘1234567890’

~~~

Answer

A

Answer to the Challenge:

import re

string = '1234567890'

good_numbers = re.findall(r'[^567]+', string)

Explanation:
Certainly! Let’s break down the code step by step and explain each part in detail:

import re: This line imports the regular expression (regex) module from the Python standard library. It allows us to use regex functions and patterns for text matching and manipulation.
string = '1234567890': This line creates a string variable named string and assigns it the value '1234567890'. It’s a sequence of numbers from 1 to 0.
good_numbers = re.findall(r'[^567]+', string): This line uses the re.findall() function to find all matches of the provided regex pattern in the string.
- r'[^567]+': This is the regex pattern used for matching. It consists of:
  - [^567]: This is a character class that matches any single character that is not “5”, “6”, or “7”. The ^ symbol at the beginning of the character class negates it, meaning it matches anything that is not within the specified characters.
  - +: This quantifier matches one or more occurrences of the preceding character class.
- re.findall() searches for all non-overlapping matches of the regex pattern in the string. It returns a list of all the matches found.
- The resulting matches are stored in the good_numbers variable.
The code block ends here, and no further actions are taken with the good_numbers variable. If you want to see the contents of good_numbers, you can add a print() statement:
```
python
print(good_numbers)
```

Overall, this code takes the string '1234567890', uses a regex pattern to find all the numbers in the string that are not “5”, “6”, or “7”, and stores the matches in the good_numbers variable.

Question 24

Q

Explain the following code in Detail (Groups):

import re

names_file = open('names.txt', encoding='utf-8')
data = names_file.read()
names_file.close()

last_name = r'Love'
first_name = r'Kenneth'

line = re.search(r'''
		^(?P<name>[-\w ]*,\s[-\w ]+)\t		# Last and first names
    (?P<email>[-\w\d.+]+@[-\w\d.]+)\t		# Email
    (?P<phone>\(?\d{3}\)?-?\s?\d{3}-\d{4})?\t		# Phone number
    (?P<job>[\w\s]+,\s[\w\s.]+)\t?		#Job & company
    (?P<twitter>@[\w\d]+)?$		# Twitter
''', data, re.X|re.MULTILINE)

print(line)
print(line.groupdict())

Answer

A

Certainly! Let’s break down the code line by line, explaining each part:

```python
import re
~~~

This line imports the re module, which provides support for regular expressions in Python.

```python
names_file = open(‘names.txt’, encoding=’utf-8’)
~~~

This line opens a file named ‘names.txt’ in read mode with the specified encoding ‘utf-8’ and assigns it to the variable names_file. It assumes that there is a file named ‘names.txt’ in the current directory.

```python
data = names_file.read()
~~~

This line reads the contents of the file stored in names_file and assigns it to the variable data. It reads the entire contents of the file as a single string.

```python
names_file.close()
~~~

This line closes the file that was opened earlier with open(). It’s good practice to close files after you’re done with them to free up system resources.

```python
last_name = r’Love’
first_name = r’Kenneth’
~~~

These lines assign the string ‘Love’ to the variable last_name and the string ‘Kenneth’ to the variable first_name. The r prefix before the strings denotes that they are raw strings, which means backslashes within them are treated as literal characters.

```python
line = re.search(r’’’
^(?P<name>[-\w ]*,\s[-\w ]+)\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? # Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
''', data, re.X|re.MULTILINE)
~~~</twitter></job></phone></email></name>

This line uses the re.search() function to search for a pattern in the data string. The pattern is specified as a multi-line raw string enclosed within triple quotes. It contains multiple named groups denoted by (?P<name>), (?P<email>), etc., which capture specific parts of the pattern. Each named group corresponds to a particular piece of information (name, email, phone, job, and twitter) in the data.

```python
print(line)
~~~

This line prints the result of the re.search() function, which returns a match object. It represents the first occurrence of the pattern in the data string.

```python
print(line.groupdict())
~~~

This line prints the named groups captured by the regular expression pattern in the form of a dictionary. It displays the captured information from the data string, organized by the named groups (name, email, phone, job, and twitter).

Overall, this code reads the contents of a file, searches for a specific pattern in the data, and prints the results. The regular expression pattern captures various pieces of information such as names, emails, phone numbers, job positions, and Twitter handles from the data string.

Certainly! Let’s break down the regular expression pattern used in the code and provide a detailed explanation of each part:

```python
r’’’
^(?
P<name>[-\w ]*,\s[-\w ]+)\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? # Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
'''
~~~</twitter></job></phone></email></name>

Let’s go through each part of the regular expression pattern and provide a thorough explanation:

^ - The caret symbol is an anchor that matches the start of a line. It ensures that the pattern starts matching from the beginning of a line in the input string.
(?P<name>[-\w ]*,\s[-\w ]+) - This named group is denoted by (?P<name>) and captures the last name and first name. Here’s a breakdown of the pattern:
- [-\w ]* matches zero or more occurrences of letters, digits, underscores, or spaces (hyphens are included for potential last names with hyphens).
- , matches a comma character.
- \s matches a whitespace character.
- [-\w ]+ matches one or more occurrences of letters, digits, underscores, or spaces (hyphens are included for potential first names with hyphens).
- The captured result is assigned the name ‘name’ using the ?P<name> syntax.
(?P<email>[-\w\d.+]+@[-\w\d.]+) - This named group captures the email address. Here’s a breakdown of the pattern:
- [-\w\d.+]+ matches one or more occurrences of letters, digits, underscores, hyphens, dots, or plus signs. It represents the username part of the email address.
- @ matches the ‘@’ symbol.
- [-\w\d.]+ matches one or more occurrences of letters, digits, underscores, hyphens, or dots. It represents the domain part of the email address.
- The captured result is assigned the name ‘email’ using the ?P<email> syntax.
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})? - This named group captures the phone number. Here’s a breakdown of the pattern:
- $? matches an optional opening parenthesis.
- \d{3} matches exactly three digits.
- $? matches an optional closing parenthesis.
- -? matches an optional hyphen.
- \s? matches an optional whitespace character.
- \d{3} matches exactly three digits.
- - matches a hyphen.
- \d{4} matches exactly four digits.
- The entire phone number is optional due to the ? at the end.
- The captured result is assigned the name ‘phone’ using the ?P<phone> syntax.
(?P<job>[\w\s]+,\s[\w\s.]+)\t? - This named group captures the job position and company. Here’s a breakdown of the pattern:
- [\w\s]+ matches one or more

occurrences of letters, digits, underscores, or spaces. It represents the job position.
- , matches a comma character.
- \s matches a whitespace character.
- [\w\s.]+ matches one or more occurrences of letters, digits, underscores, spaces, or dots. It represents the company name.
- The tab character \t after the pattern is optional due to the \t? at the end.
- The captured result is assigned the name ‘job’ using the ?P<job> syntax.

(?P<twitter>@[\w\d]+)?$ - This named group captures the Twitter handle. Here’s a breakdown of the pattern:
- @ matches the ‘@’ symbol.
- [\w\d]+ matches one or more occurrences of letters or digits. It represents the Twitter handle username.
- The entire Twitter handle is optional due to the ? at the end.
- The captured result is assigned the name ‘twitter’ using the ?P<twitter> syntax.

The regular expression pattern is designed to match specific patterns of data in each line of the input string. Each named group captures different pieces of information such as names, emails, phone numbers, job positions, and Twitter handles. The captured information can be accessed using the groupdict() method on the resulting match object, providing a dictionary with the named groups as keys and their corresponding captured values.

It’s important to note that the regular expression pattern assumes a certain format and structure in the input data. Adjustments may be necessary if the actual data differs significantly from the expected pattern.

Treehouse Notes:

([abc]) - creates a group that contains a set for the letters ‘a’, ‘b’, and ‘c’. This could be later accessed from the Match object as .group(1)
**(?P<name>[abc])** - creates a named group that contains a set for the letters 'a', 'b', and 'c'. This could later be accessed from the Match object as .group('name').</name>
.groups() - method to show all of the groups on a Match object.
re.MULTILINE or re.M - flag to make a pattern regard lines in your text as the beginning or end of a string.
^ - specifies, in a pattern, the beginning of the string.
$ - specifies, in a pattern, the end of the string.

Now you have the important points listed in a more readable format.

Question 25

Q

Challenge: Create a variable names that is an re.match() against string. The pattern should provide two groups, one for a last name match and one for a first name match. The name parts are separated by a comma and a space.

import re

string = 'Perotto, Pier Giorgio'

Answer

A

Answer to Challenge:

import re

string = 'Perotto, Pier Giorgio'

names = re.match(r'^([\w]*),\s([\w ]*)$', string)

Explanation:
Certainly! Let’s break down the code and explain each part in detail:

```python
import re
~~~

This line imports the re module, which provides support for regular expressions in Python.

```python
string = ‘Perotto, Pier Giorgio’
~~~

This line assigns the string 'Perotto, Pier Giorgio' to the variable string. It represents the input string containing a name in the format “Last Name, First Name”.

```python
names = re.match(r’^([\w]),\s([\w ])$’, string)
~~~

This line uses the re.match() function to search for a match of the regular expression pattern in the string. The pattern is specified as a raw string using r'...'. Here’s the breakdown of the pattern:

^ - Specifies the start of the string.
([\w]*) - This part creates a capturing group denoted by (...). It matches zero or more word characters (letters, digits, or underscores) and captures them. This group represents the last name.
, - Matches a comma character.
\s - Matches a whitespace character.
([\w ]*) - This part creates another capturing group. It matches zero or more word characters or spaces and captures them. This group represents the first name.
$ - Specifies the end of the string.

The re.match() function attempts to match the pattern against the string. If the pattern matches, a match object is returned. If the pattern doesn’t match, None is returned.

The resulting match object, if successful, is assigned to the names variable. It contains information about the match, including the captured groups.

To access the captured groups, you can use the .group() method on the names match object. For example, names.group(1) would give you the captured last name, and names.group(2) would give you the captured first name.

Overall, the code uses regular expressions to extract the last name and first name from the given string using the specified pattern.

Question 26

Q

Challenge 1: Create a new variable named contacts that is an re.search() where the pattern catches the email address and phone number from string. Name the email pattern email and the phone number pattern phone. The comma and spaces * should not* be part of the groups.

import re

string = '''Love, Kenneth, kenneth+challenge@teamtreehouse.com, 555-555-5555, @kennethlove
Chalkley, Andrew, andrew@teamtreehouse.co.uk, 555-555-5556, @chalkers
McFarland, Dave, dave.mcfarland@teamtreehouse.com, 555-555-5557, @davemcfarland
Kesten, Joy, joy@teamtreehouse.com, 555-555-5558, @joykesten'''

Challenge 2:
Great! Now, make a new variable, twitters that is an re.search() where the pattern catches the Twitter handle for a person. Remember to mark it as being at the end of the string. You’ll also want to use the re.MULTILINE flag.

Answer

A

Solution to Challenge 1:

import re

string = '''Love, Kenneth, kenneth+challenge@teamtreehouse.com, 555-555-5555, @kennethlove
Chalkley, Andrew, andrew@teamtreehouse.co.uk, 555-555-5556, @chalkers
McFarland, Dave, dave.mcfarland@teamtreehouse.com, 555-555-5557, @davemcfarland
Kesten, Joy, joy@teamtreehouse.com, 555-555-5558, @joykesten'''

contacts = re.search(r'''
        (?P<email>[-\w\d.+]+@[-\w\d.]+) 
        ,\s
        (?P<phone>\d{3}-\d{3}-\d{4})
''', string, re.X | re.M)

Solution to Challenge 2:

import re

string = '''Love, Kenneth, kenneth+challenge@teamtreehouse.com, 555-555-5555, @kennethlove
Chalkley, Andrew, andrew@teamtreehouse.co.uk, 555-555-5556, @chalkers
McFarland, Dave, dave.mcfarland@teamtreehouse.com, 555-555-5557, @davemcfarland
Kesten, Joy, joy@teamtreehouse.com, 555-555-5558, @joykesten'''

contacts = re.search(r'''
        (?P<email>[-\w\d.+]+@[-\w\d.]+) 
        ,\s
        (?P<phone>\d{3}-\d{3}-\d{4})
''', string, re.X | re.M) 

twitters = re.search(r'''
        (?P<twitter>@[^t][\w\d]+)
''', string, re.X | re.M)

Certainly! Here’s a detailed explanation of the solutions for Challenge 1 and Challenge 2:

Solution to Challenge 1:

```python
import re

string = ‘'’Love, Kenneth, kenneth+challenge@teamtreehouse.com, 555-555-5555, @kennethlove
Chalkley, Andrew, andrew@teamtreehouse.co.uk, 555-555-5556, @chalkers
McFarland, Dave, dave.mcfarland@teamtreehouse.com, 555-555-5557, @davemcfarland
Kesten, Joy, joy@teamtreehouse.com, 555-555-5558, @joykesten’’’

contacts = re.search(r’’’
(?P<email>[-\w\d.+]+@[-\w\d.]+)
,\s
(?P<phone>\d{3}-\d{3}-\d{4})
''', string, re.X | re.M)
~~~</phone></email>

In this solution, we are using re.search() to find a match in the string based on the provided pattern.

Let’s break down the regular expression pattern:

(?P<email>[-\w\d.+]+@[-\w\d.]+) captures the email address. Here’s a detailed breakdown:
- (?P<email>) is a named group denoted by (?). It captures the email address and assigns it the name ‘email’.
- [-\w\d.+]+ matches one or more occurrences of letters, digits, hyphens, underscores, or dots. It represents the username part of the email.
- @ matches the ‘@’ symbol.
- [-\w\d.]+ matches one or more occurrences of letters, digits, hyphens, or dots. It represents the domain part of the email.
,\s matches a comma followed by a whitespace character.
(?P<phone>\d{3}-\d{3}-\d{4}) captures the phone number. Here’s a detailed breakdown:
- (?P<phone>) is a named group that captures the phone number and assigns it the name ‘phone’.
- \d{3}-\d{3}-\d{4} matches three digits, followed by a hyphen, three more digits, another hyphen, and four digits. It represents the phone number format.

The re.X | re.M flags are used to enable verbose mode (re.X) and multiline mode (re.M). The verbose mode allows us to write the pattern with comments and ignore whitespaces. The multiline mode allows the pattern to match the start and end of lines in the input string.

The re.search() function searches for the first occurrence of the pattern in the string. If a match is found, a match object is returned. If no match is found, it returns None.

The resulting match object is assigned to the contacts variable.

Solution to Challenge 2:

```python
import re

string = ‘'’Love, Kenneth, kenneth+challenge@teamtreehouse.com, 555-555-5555, @kennethlove
Chalkley, Andrew, andrew@teamtreehouse.co.uk, 555-555-5556, @chalkers
McFarland, Dave, dave.mcfarland@teamtreehouse.com, 555-555-5557, @davemcfarland
Kesten, Joy, joy@teamtreehouse.com, 555-555-5558, @joykesten’’’

contacts = re.search(r’’’
(?P<email>[-\w\d.+]+@[-\w\d.]+)
,\s
(?P<phone>\d{3}-\d{3}-\d{4})
''', string, re.X | re.M)</phone></email>

twitters = re.search(r’’’
(?P<twitter>@[^t][\w\d]+)
''', string, re.X | re.M)
~~~</twitter>

In this solution, we are again using re.search() to find matches in the string based on the provided pattern.

Let’s break down the regular expression pattern for capturing the Twitter handles:

(?P<twitter>@[^t][\w\d]+) captures the Twitter handle. Here’s a detailed breakdown:
- (?P<twitter>) is a named group that captures the Twitter handle and assigns it the name ‘twitter’.
- @ matches the ‘@’ symbol.
- [^t] matches any character that is not ‘t’. This ensures that the Twitter handle does not start with ‘t’, excluding potential matches like ‘the’ or ‘to’.
- [\w\d]+ matches one or more occurrences of letters or digits. It represents the remaining part of the Twitter handle.

The re.X | re.M flags are used again to enable verbose mode (re.X) and multiline mode (re.M).

The re.search() function searches for the first occurrence of the pattern in the string. If a match is found, a match object is returned. If no match is found, it returns None.

The resulting match objects are assigned to the contacts and twitters variables.

Keep in mind that the patterns provided assume a specific format and structure in the string. Adjustments may be necessary if the actual data differs significantly from the expected pattern.

Question 27

Q

What does .groupdict() do in Python? Print .groupdict() directly.

Answer

A

In Python, the .groupdict() method is used in conjunction with regular expressions and is specifically available on objects returned by the re.Match class. It returns a dictionary containing all the named groups of a regular expression match.

Here’s how it works:

You start by applying a regular expression pattern to a string using the re module in Python.
When a match is found, you obtain a match object (re.Match) that represents the matched substring.
The .groupdict() method is then called on this match object.
If the regular expression pattern contains named groups (defined using the (?P<name>pattern) syntax), the .groupdict() method returns a dictionary where the keys are the names of the groups, and the values are the corresponding matched substrings.

Here’s a simple example to illustrate its usage:

```python
import re

pattern = r”(?P<first_name>\w+) (?P<last_name>\w+)"
text = "John Doe"</last_name></first_name>

match = re.match(pattern, text)
if match:
groups = match.groupdict()
print(groups)
~~~

Output:

{'first_name': 'John', 'last_name': 'Doe'}

In the example above, the regular expression pattern (?P<first_name>\w+) (?P<last_name>\w+) matches a string with two words separated by a space. The groupdict() method is then used to retrieve the named groups (first_name and last_name) and their corresponding matched substrings (John and Doe).

This method is particularly useful when you have a regular expression pattern with multiple named groups, and you want to extract the matched substrings associated with each named group conveniently.

Yes, you can print the result of groupdict() directly. Here’s an example:

```python
import re

pattern = r”(?P<first_name>\w+) (?P<last_name>\w+)"
text = "John Doe"</last_name></first_name>

match = re.match(pattern, text)
if match:
print(match.groupdict())
~~~

Output:

{'first_name': 'John', 'last_name': 'Doe'}

In this example, the groupdict() method is called directly within the print() function, which outputs the dictionary containing the named groups and their matched substrings.

Question 28

Q

Explain the following code in detail:
~~~
import re

data = ‘'’Love, Kenneth kenneth@teamtreehouse.com (555) 555-5555 Teacher, Treehouse @kennethlove
McFarland, Dave dave@teamtreehouse.com (555) 555-5554 Teacher, Treehouse
Arthur, King king_arthur@camelot.co.uk King, Camelot
Österberg, Sven-Erik governor@norrbotten.co.se Governor, Norrbotten @sverik
, Tim tim@killerrabbit.com Enchanter, Killer Rabbit Cave
Carson, Ryan ryan@teamtreehouse.com (555) 555-5543 CEO, Treehouse @ryancarson
Doctor, The doctor+companion@tardis.co.uk Time Lord, Gallifrey
Exampleson, Example me@example.com 555-555-5552 Example, Example Co. @example
Obama, Barack president.44@us.gov 555 555-5551 President, United States of America @potus44
Chalkley, Andrew andrew@teamtreehouse.com (555) 555-5553 Teacher, Treehouse @chalkers
Vader, Darth darth-vader@empire.gov (555) 555-4444 Sith Lord, Galactic Empire @darthvader
Fernández de la Vega Sanz, María Teresa mtfvs@spain.gov First Deputy Prime Minister, Spanish Govt.’’’

line = re.compile(r’’’
^(?P<name>[-\w ]*,\s[-\w ]+)\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? #Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
''', re.X|re.MULTILINE)</twitter></job></phone></email></name>

print(re.search(line, data).groupdict())
print(line.search(data).groupdict()) #gives same result as above print-line, instead of re, search can be done directly

~~~

Answer

A

Certainly! Let’s break down the code step by step:

```python
import re

data = ‘'’Love, Kenneth kenneth@teamtreehouse.com (555) 555-5555 Teacher, Treehouse @kennethlove
McFarland, Dave dave@teamtreehouse.com (555) 555-5554 Teacher, Treehouse
Arthur, King king_arthur@camelot.co.uk King, Camelot
Österberg, Sven-Erik governor@norrbotten.co.se Governor, Norrbotten @sverik
, Tim tim@killerrabbit.com Enchanter, Killer Rabbit Cave
Carson, Ryan ryan@teamtreehouse.com (555) 555-5543 CEO, Treehouse @ryancarson
Doctor, The doctor+companion@tardis.co.uk Time Lord, Gallifrey
Exampleson, Example me@example.com 555-555-5552 Example, Example Co. @example
Obama, Barack president.44@us.gov 555 555-5551 President, United States of America @potus44
Chalkley, Andrew andrew@teamtreehouse.com (555) 555-5553 Teacher, Treehouse @chalkers
Vader, Darth darth-vader@empire.gov (555) 555-4444 Sith Lord, Galactic Empire @darthvader
Fernández de la Vega Sanz, María Teresa mtfvs@spain.gov First Deputy Prime Minister, Spanish Govt.’’’

line = re.compile(r’’’
^(?
P<name>[-\w ]*,\s[-\w ]+)\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? # Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
''', re.X|re.MULTILINE)</twitter></job></phone></email></name>

print(re.search(line, data).groupdict())
print(line.search(data).groupdict()) # Gives the same result as the above print line; instead of using re, search can be done directly
~~~

The code starts by importing the re module, which provides support for regular expressions in Python.
The data variable contains a multi-line string with various lines of data, where each line represents information about a person.
The line variable is defined as a compiled regular expression pattern using the re.compile() function. The pattern is written using a raw string (r'...') to avoid escaping backslashes.
The regular expression pattern inside line is defined using a verbose mode (re.X) and allows multi-line matching (re.MULTILINE). It is divided into several parts, each enclosed in parentheses and preceded by a named group.
- (?P<name>[-\w ]*,\s[-\w ]+)\t matches the last name and first name separated by a comma and a space. The names can contain alphanumeric characters, hyphens, and spaces.
- (?P<email>[-\w\d.+]+@[-\w\d.]+)\t matches the email address, allowing alphanumeric characters, hyphens, plus signs, and

dots.
- (?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t matches an optional phone number, which can have different formats: (555) 555-5555 or 555-555-5555.
- (?P<job>[\w\s]+,\s[\w\s.]+)\t? matches the job and company, separated by a comma and a space. Both can contain alphanumeric characters, spaces, and dots. The company can be followed by an optional tab character.
- (?P<twitter>@[\w\d]+)?$ matches an optional Twitter handle starting with ‘@’ followed by alphanumeric characters.

The re.search() function is called with the line pattern and the data string as arguments. It searches for the first occurrence of the pattern in the data.
The .groupdict() method is called on the match object returned by re.search(). This method extracts the matched groups from the regular expression pattern and returns them as a dictionary.
Finally, the resulting dictionary is printed using print(). This dictionary contains the captured information from the data, such as name, email, phone number, job, and Twitter handle.

The code demonstrates how to extract structured data from a text using regular expressions and retrieve the captured information using the .groupdict() method.

Question 29

Q

What is re.compile() do? and how is it different from re.search() and re.findall()?

Answer

A

The re.compile() function in Python is used to compile a regular expression pattern into a pattern object. This pattern object can then be used for matching operations with functions like re.search() and re.findall().

Here’s a breakdown of the differences between re.compile(), re.search(), and re.findall():

re.compile(): This function is used to compile a regular expression pattern into a pattern object. It takes a regular expression pattern as a string argument and returns a pattern object that can be reused for multiple matching operations. Compiling the pattern upfront can improve performance if you need to use the same pattern repeatedly.Example:
```python
import repattern = re.compile(r’\d{3}-\d{3}-\d{4}’)
```
re.search(): This function searches for the first occurrence of a pattern in a given string and returns a match object if a match is found. It scans the entire string and stops at the first occurrence of a match. The match object contains information about the matched substring and provides methods like .group() and .groupdict() to retrieve the matched content.Example:
```python
import retext = ‘The phone number is 123-456-7890’
match = re.search(r’\d{3}-\d{3}-\d{4}’, text)
```
re.findall(): This function searches for all occurrences of a pattern in a given string and returns a list of matched substrings. It scans the entire string and collects all non-overlapping matches. It does not provide match objects like re.search(), but only returns the matched substrings as a list.Example:
```python
import retext = ‘The numbers are 123 and 456’
matches = re.findall(r’\d{3}’, text)
```

In summary, re.compile() is used to precompile a regular expression pattern into a pattern object, which can be reused for efficient matching operations. re.search() is used to find the first occurrence of a pattern in a string and returns a match object. re.findall() is used to find all non-overlapping occurrences of a pattern in a string and returns a list of matched substrings.

Question 30

Q

Explain the following code in detail:

import re

data = '''Love, Kenneth	kenneth@teamtreehouse.com	(555) 555-5555	Teacher, Treehouse	@kennethlove
McFarland, Dave	dave@teamtreehouse.com	(555) 555-5554	Teacher, Treehouse
Arthur, King	king_arthur@camelot.co.uk		King, Camelot
Österberg, Sven-Erik	governor@norrbotten.co.se		Governor, Norrbotten	@sverik
, Tim	tim@killerrabbit.com		Enchanter, Killer Rabbit Cave
Carson, Ryan	ryan@teamtreehouse.com	(555) 555-5543	CEO, Treehouse	@ryancarson
Doctor, The	doctor+companion@tardis.co.uk		Time Lord, Gallifrey
Exampleson, Example	me@example.com	555-555-5552	Example, Example Co.	@example
Obama, Barack	president.44@us.gov	555 555-5551	President, United States of America	@potus44
Chalkley, Andrew	andrew@teamtreehouse.com	(555) 555-5553	Teacher, Treehouse	@chalkers
Vader, Darth	darth-vader@empire.gov	(555) 555-4444	Sith Lord, Galactic Empire	@darthvader
Fernández de la Vega Sanz, María Teresa	mtfvs@spain.gov		First Deputy Prime Minister, Spanish Govt.'''

line = re.compile(r'''
		^(?P<name>[-\w ]*,\s[-\w ]+)\t		# Last and first names
    (?P<email>[-\w\d.+]+@[-\w\d.]+)\t		# Email
    (?P<phone>\(?\d{3}\)?-?\s?\d{3}-\d{4})?\t		# Phone number
    (?P<job>[\w\s]+,\s[\w\s.]+)\t?		#Job & company
    (?P<twitter>@[\w\d]+)?$		# Twitter
''', re.X|re.MULTILINE)

for match in line.finditer(data):
    print(match.group('name'))

Answer

A

The code you provided uses regular expressions to extract and print the names from a given dataset. Let’s break it down step by step:

```python
import re

data = ‘'’Love, Kenneth kenneth@teamtreehouse.com (555) 555-5555 Teacher, Treehouse @kennethlove
McFarland, Dave dave@teamtreehouse.com (555) 555-5554 Teacher, Treehouse
Arthur, King king_arthur@camelot.co.uk King, Camelot
Österberg, Sven-Erik governor@norrbotten.co.se Governor, Norrbotten @sverik
, Tim tim@killerrabbit.com Enchanter, Killer Rabbit Cave
Carson, Ryan ryan@teamtreehouse.com (555) 555-5543 CEO, Treehouse @ryancarson
Doctor, The doctor+companion@tardis.co.uk Time Lord, Gallifrey
Exampleson, Example me@example.com 555-555-5552 Example, Example Co. @example
Obama, Barack president.44@us.gov 555 555-5551 President, United States of America @potus44
Chalkley, Andrew andrew@teamtreehouse.com (555) 555-5553 Teacher, Treehouse @chalkers
Vader, Darth darth-vader@empire.gov (555) 555-4444 Sith Lord, Galactic Empire @darthvader
Fernández de la Vega Sanz, María Teresa mtfvs@spain.gov First Deputy Prime Minister, Spanish Govt.’’’

line = re.compile(r’’’
^(?
P<name>[-\w ]*,\s[-\w ]+)\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? # Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
''', re.X|re.MULTILINE)</twitter></job></phone></email></name>

for match in line.finditer(data):
print(match.group(‘name’))
~~~

The code begins by importing the re module, which provides support for regular expressions in Python.
The data variable holds a multiline string containing the dataset with various lines of information.
The line variable is defined as a compiled regular expression pattern using the re.compile() function. It uses a raw string (r'...') to specify the pattern and includes named groups to capture specific information.
The regular expression pattern within line is divided into multiple lines for readability using the re.X flag. It consists of several named groups defined by (?P<name>...), (?P<email>...), (?P<phone>...), (?P<job>...), and (?P<twitter>...).
- (?P<name>[-\w ]*,\s[-\w ]+)\t captures the last name and first name separated by a comma and space.
- (?P<email>[-\w\d.+]+@[-\w\d.]+)\t captures the email address.
- `(?P<phone>\(?\d{</phone>

3})?-?\s?\d{3}-\d{4})?\tcaptures an optional phone number in various formats. -(?P<job>[\w\s]+,\s[\w\s.]+)\t?` captures the job and company separated by a comma and space.
- `(?P<twitter>@[\w\d]+)?$` captures an optional Twitter handle starting with '@'.</twitter></job>

The line.finditer(data) method is used to iterate over the dataset and find all matches that adhere to the regular expression pattern.
Inside the for loop, match represents a match object containing the captured information for each iteration.
The match.group('name') retrieves the captured name from each match object.
Finally, the name is printed using print(match.group('name')).

The code will iterate over the dataset, extract the names using regular expressions, and print them one by one. Each printed name corresponds to the captured last name and first name from the dataset.

The code will extract the names from the given dataset using regular expressions and print them. Specifically, it will print the captured names from the dataset, line by line.

Here’s the expected output based on the provided dataset:

Love, Kenneth
McFarland, Dave
Arthur, King
Österberg, Sven-Erik
, Tim
Carson, Ryan
Doctor, The
Exampleson, Example
Obama, Barack
Chalkley, Andrew
Vader, Darth
Fernández de la Vega Sanz, María Teresa

Each line corresponds to a name extracted from the dataset using the regular expression pattern specified in the code.

Question 31

Q

Explain .finditer method in Python?

Answer

A

In Python, the .finditer() method is used to search for matches of a regular expression pattern within a given string. It returns an iterator yielding match objects for all non-overlapping matches found in the string.

Here’s how the .finditer() method works:

You start by importing the re module, which provides support for regular expressions in Python.
You compile a regular expression pattern using the re.compile() function, or directly use a regular expression pattern as a string.
The .finditer() method is then called on the compiled pattern or the regular expression pattern itself, passing the string you want to search as an argument.
The .finditer() method scans the string and finds all non-overlapping matches of the regular expression pattern.
It returns an iterator that yields match objects for each match found. Each match object contains information about the matched substring and provides methods like .group() and .groupdict() to retrieve the captured content.
You can iterate over the match objects using a for loop or any other method that works with iterators.

Here’s an example to illustrate the usage of .finditer():

```python
import re

text = “Hello there, how are you today?”

pattern = r”\b\w{3}\b” # Matches three-letter words

matches = re.finditer(pattern, text)
for match in matches:
print(match.group())
~~~

Output:

how
are
you

In the example above, the regular expression pattern \b\w{3}\b matches three-letter words. The .finditer() method is used to find all occurrences of this pattern in the given text. The resulting match objects are then iterated over, and the .group() method is called to print each matched word.

The .finditer() method is useful when you need to find multiple occurrences of a regular expression pattern within a string and retrieve information about each match. It allows you to work with match objects individually, accessing the captured content and performing further processing or analysis as needed.

Question 32

Q

Explain the following code in detail:

import re

data = '''Love, Kenneth	kenneth@teamtreehouse.com	(555) 555-5555	Teacher, Treehouse	@kennethlove
McFarland, Dave	dave@teamtreehouse.com	(555) 555-5554	Teacher, Treehouse
Arthur, King	king_arthur@camelot.co.uk		King, Camelot
Österberg, Sven-Erik	governor@norrbotten.co.se		Governor, Norrbotten	@sverik
, Tim	tim@killerrabbit.com		Enchanter, Killer Rabbit Cave
Carson, Ryan	ryan@teamtreehouse.com	(555) 555-5543	CEO, Treehouse	@ryancarson
Doctor, The	doctor+companion@tardis.co.uk		Time Lord, Gallifrey
Exampleson, Example	me@example.com	555-555-5552	Example, Example Co.	@example
Obama, Barack	president.44@us.gov	555 555-5551	President, United States of America	@potus44
Chalkley, Andrew	andrew@teamtreehouse.com	(555) 555-5553	Teacher, Treehouse	@chalkers
Vader, Darth	darth-vader@empire.gov	(555) 555-4444	Sith Lord, Galactic Empire	@darthvader
Fernández de la Vega Sanz, María Teresa	mtfvs@spain.gov		First Deputy Prime Minister, Spanish Govt.'''

line = re.compile(r'''
		^(?P<name>(?P<last>[-\w ]*),\s(?P<first>[-\w ]+))\t		# Last and first names
    (?P<email>[-\w\d.+]+@[-\w\d.]+)\t		# Email
    (?P<phone>\(?\d{3}\)?-?\s?\d{3}-\d{4})?\t		# Phone number
    (?P<job>[\w\s]+,\s[\w\s.]+)\t?		#Job & company
    (?P<twitter>@[\w\d]+)?$		# Twitter
''', re.X|re.MULTILINE)

for match in line.finditer(data):
    print('{first} {last} <{email}>'.format(**match.groupdict()))

Answer

A

Certainly! Let’s go through the code step by step and explain each line:

```python
import re
~~~

The code begins by importing the re module, which provides support for regular expressions in Python.

```python
data = ‘'’Love, Kenneth kenneth@teamtreehouse.com (555) 555-5555 Teacher, Treehouse @kennethlove
McFarland, Dave dave@teamtreehouse.com (555) 555-5554 Teacher, Treehouse
Arthur, King king_arthur@camelot.co.uk King, Camelot
Österberg, Sven-Erik governor@norrbotten.co.se Governor, Norrbotten @sverik
, Tim tim@killerrabbit.com Enchanter, Killer Rabbit Cave
Carson, Ryan ryan@teamtreehouse.com (555) 555-5543 CEO, Treehouse @ryancarson
Doctor, The doctor+companion@tardis.co.uk Time Lord, Gallifrey
Exampleson, Example me@example.com 555-555-5552 Example, Example Co. @example
Obama, Barack president.44@us.gov 555 555-5551 President, United States of America @potus44
Chalkley, Andrew andrew@teamtreehouse.com (555) 555-5553 Teacher, Treehouse @chalkers
Vader, Darth darth-vader@empire.gov (555) 555-4444 Sith Lord, Galactic Empire @darthvader
Fernández de la Vega Sanz, María Teresa mtfvs@spain.gov First Deputy Prime Minister, Spanish Govt.’’’
~~~

The data variable contains a multiline string that represents a dataset with various lines of information about individuals.

```python
line = re.compile(r’’’
^(?
P<name>(?P<last>[-\w ]*),\s(?P<first>[-\w ]+))\t # Last and first names
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t # Email
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t # Phone number
(?P<job>[\w\s]+,\s[\w\s.]+)\t? # Job & company
(?P<twitter>@[\w\d]+)?$ # Twitter
''', re.X|re.MULTILINE)
~~~</twitter></job></phone></email></first></last></name>

The code creates a compiled regular expression pattern using the re.compile() function. The pattern is defined as a raw string (r'...') and includes multiple named groups for capturing specific information.

^(?P<name>(?P<last>[-\w ]*),\s(?P<first>[-\w ]+))\t captures the last name and first name separated by a comma and space.
(?P<email>[-\w\d.+]+@[-\w\d.]+)\t captures the email address.
(?P<phone>$?\d{3}$?-?\s?\d{3}-\d{4})?\t captures an optional phone number in different formats.
(?P<job>[\w\s]+,\s[\w\s.]+)\t? captures the job and company separated by a comma and space.
(?P<twitter>@[\w\d]+)?$

captures an optional Twitter handle starting with ‘@’.

The re.X flag allows multiline regular expressions with added whitespace for readability, and the re.MULTILINE flag enables multiline matching.

```python
for match in line.finditer(data):
print(‘{first} {last} <{email}>’.format(**match.groupdict()))
~~~

The code then uses the .finditer() method on the compiled regular expression pattern line to search for all non-overlapping matches within the data string. It returns an iterator that yields match objects.

Inside the for loop, each match represents a match object for a particular individual’s information. The .groupdict() method retrieves the captured content as a dictionary.

The print() statement uses string formatting to display the first name ({first}), last name ({last}), and email ({email}) from the captured content. The **match.groupdict() syntax unpacks the dictionary elements as keyword arguments for the format() method.

Finally, the code will output the formatted string for each match, displaying the first name, last name, and email address for each individual:

Kenneth Love <kenneth@teamtreehouse.com>
Dave McFarland <dave@teamtreehouse.com>
King Arthur <king_arthur@camelot.co.uk>
Sven-Erik Österberg <governor@norrbotten.co.se>
Tim , <tim@killerrabbit.com>
Ryan Carson <ryan@teamtreehouse.com>
The Doctor <doctor+companion@tardis.co.uk>
Example Exampleson <me@example.com>
Barack Obama <president.44@us.gov>
Andrew Chalkley <andrew@teamtreehouse.com>
Darth Vader <darth-vader@empire.gov>
María Teresa Fernández de la Vega Sanz <mtfvs@spain.gov>

Each line corresponds to an individual’s information extracted from the dataset, displaying their first name, last name, and email address.

Question 33

Q

Explain the following terms with examples:

re.compile(pattern, flags) - method to pre-compile and save a regular expression pattern, and any associated flags, for later use.
.groupdict() - method to generate a dictionary from a Match object’s groups. The keys will be the group names. The values will be the results of the patterns in the group.
re.finditer() - method to generate an iterable from the non-overlapping matches of a regular expression. Very handy for for loops.
.group() - method to access the content of a group. 0 or none is the entire match. 1 through however many groups you have will get that group. Or use a group’s name to get it if you’re using named groups.

Answer

A

Certainly! Let’s explain each term with examples:

re.compile(pattern, flags):
The re.compile() method is used to pre-compile and save a regular expression pattern, along with any associated flags, for later use. It returns a compiled pattern object that can be used for matching operations. Here’s an example:```python
import repattern = re.compile(r’\d{3}-\d{3}-\d{4}’, re.IGNORECASE)
text = ‘Phone numbers: 123-456-7890, 555-555-5555’matches = pattern.findall(text)
print(matches)
```Output:
```
['123-456-7890', '555-555-5555']
```
In the example, the re.compile() method is used to compile the regular expression pattern \d{3}-\d{3}-\d{4}, which matches phone numbers in the format xxx-xxx-xxxx. The re.IGNORECASE flag is passed to make the pattern case-insensitive. The compiled pattern is then used with the findall() method to find all phone numbers in the given text.
.groupdict():
The .groupdict() method is used to generate a dictionary from a Match object’s groups. The keys in the dictionary will be the group names, and the values will be the matched results of the patterns in those groups. Here’s an example:```python
import retext = ‘John Doe (30) Jane Smith (25)’pattern = re.compile(r’(?P<name>\w+)\s+(?P<age>\d+)')
matches = pattern.finditer(text)</age></name>for match in matches:
print(match.groupdict())
```Output:
```
{'name': 'John', 'age': '30'}
{'name': 'Jane', 'age': '25'}
```
In this example, the regular expression pattern (?P<name>\w+)\s+(?P<age>\d+) is used to match names followed by ages. The .finditer() method returns an iterator yielding match objects for each match found. The .groupdict() method is called on each match object to retrieve a dictionary with the matched names and ages.
re.finditer():
The re.finditer() method is used to generate an iterable from the non-overlapping matches of a regular expression. It’s particularly useful for for loops, allowing you to iterate over each match individually. Here’s an example:```python
import retext = ‘Hello there, how are you?’pattern = re.compile(r’\b\w{3}\b’)
matches = pattern.finditer(text)for match in matches:
print(match.group())
```Output:
```
Hello
how
you
```
In the example, the regular expression pattern \b\w{3}\b is used to match three-letter words. The re.finditer() method returns an iterator yielding match objects for each three-letter word found in the text. The for loop iterates over the matches, and match.group() retrieves and prints each matched word.
.group():
The .group() method is used to access the content of a group within a match object. You can specify the group number (starting from 0 for the entire match) or the group name if named groups are used. Here’s an example:```python
import retext = ‘Hello World

’

pattern = re.compile(r’(\w+)\s+(\w+)’)
match = pattern.search(text)

print(match.group(0)) # Entire match
print(match.group(1)) # First group
print(match.group(2)) # Second group
```

Output:

   Hello World
   Hello
   World

In this example, the regular expression pattern (\w+)\s+(\w+) is used to match two words separated by whitespace. The .search() method finds the first occurrence of the pattern in the text, and match.group(0) retrieves the entire match. match.group(1) and match.group(2) retrieve the first and second groups, respectively, which correspond to the individual words in this case.

These examples demonstrate how each term is used and provide output to illustrate their functionality.

Question 34

Q

Challenge 1: Create a variable named players that is an re.search() or re.match() to capture three groups: last_name, first_name, and score. It should include re.MULTILINE.

import re

string = '''Love, Kenneth: 20
Chalkley, Andrew: 25
McFarland, Dave: 10
Kesten, Joy: 22
Stewart Pinchback, Pinckney Benton: 18'''

Challenge 2:
Wow! OK, now, create a class named Player that has those same three attributes, last_name, first_name, and score. I should be able to set them through \_\_init\_\_.

Answer

A

Answer to Challenge:

import re

string = '''Love, Kenneth: 20
Chalkley, Andrew: 25
McFarland, Dave: 10
Kesten, Joy: 22
Stewart Pinchback, Pinckney Benton: 18'''

players = re.findall(r''' 
    ^(?P<last_name>[-\w\s*\w*]+), 
    \s(?P<first_name>[-\w\s*\w]+): 
    \s(?P<score>[\d]+)$ 
''', string, re.X | re.M)  # challenge 1

class Player:
    def \_\_init\_\_(self, last_name, first_name, score):
        self.last_name = last_name
        self.first_name = first_name
        self.score = score # challenge 2

Explanation:
Certainly! Let’s provide a more detailed and thorough explanation, line by line:

```python
import re
~~~

This line imports the re module, which provides support for regular expressions in Python.

```python
string = ‘'’Love, Kenneth: 20
Chalkley, Andrew: 25
McFarland, Dave: 10
Kesten, Joy: 22
Stewart Pinchback, Pinckney Benton: 18’’’
~~~

Here, the variable string is assigned a multiline string containing the provided input data. Each line represents a player’s information, including their last name, first name, and score.

```python
players = re.findall(r’’’
^(?P<last_name>[-\w\s*\w*]+),
\s(?P<first_name>[-\w\s*\w]+):
\s(?P<score>[\d]+)$
''', string, re.X | re.M)
~~~</score></first_name></last_name>

The re.findall() function is used to search for all occurrences of the regular expression pattern within the string. The pattern is defined as a multiline raw string (r''' ... ''') to allow for better readability and maintain line-by-line correspondence with the provided input data.

Let’s break down the regular expression pattern:
- ^ asserts the start of a line.
- (?P<last_name>[-\w\s*\w*]+) captures the last name as a named group (last_name). It matches one or more word characters, spaces, or asterisks within square brackets.
- , matches the comma character.
- \s matches a single whitespace character.
- (?P<first_name>[-\w\s*\w]+) captures the first name as a named group (first_name). It matches one or more word characters, spaces, or asterisks within square brackets.
- : matches the colon character.
- \s matches a single whitespace character.
- (?P<score>[\d]+) captures the score as a named group (score). It matches one or more digits.
- $ asserts the end of a line.

The re.X flag is used to enable multiline regular expressions with added whitespace for readability. The re.M flag enables multiline matching.

The players variable will contain a list of tuples, where each tuple represents a match, and each element of the tuple corresponds to the captured values of last name, first name, and score.

```python
class Player:
def __init__(self, last_name, first_name, score):
self.last_name = last_name
self.first_name = first_name
self.score = score
~~~

Here, a Player class is defined. It has an \_\_init\_\_ method, which acts as the constructor for creating Player objects. The self parameter refers to the instance being created, and the method accepts last_name, first_name, and score as arguments.

Inside the \_\_init\_\_ method, the instance variables self.last_name, self.first_name, and self.score are assigned the corresponding argument values, allowing each Player object to hold its own last name, first name, and score.

```python
player_objects = []
for player in players:
player_objects.append(Player(player[0], player[1], player[2]))
~~~

An empty list named player_objects is created to store instances of the Player class.

A for loop is used to iterate over each tuple in the players list, representing each match found in the input data. For each player, a new Player object is created using the

Player(player[0], player[1], player[2]) syntax, passing the captured values from the tuple as arguments to the Player constructor.

The newly created Player object is appended to the player_objects list.

```python
for player in player_objects:
print(“Last Name:”, player.last_name)
print(“First Name:”, player.first_name)
print(“Score:”, player.score)
print()
~~~

Another for loop is used to iterate over each Player object in the player_objects list.

For each player, the attributes last_name, first_name, and score are accessed using dot notation (player.last_name, player.first_name, player.score).

The print() function is used to display the last name, first name, and score of each player.

An empty print() statement is added to create an empty line after printing each player’s information for better readability.

The output will be as follows:

Last Name: Love
First Name: Kenneth
Score: 20

Last Name: Chalkley
First Name: Andrew
Score: 25

Last Name: McFarland
First Name: Dave
Score: 10

Last Name: Kesten
First Name: Joy
Score: 22

Last Name: Stewart Pinchback
First Name: Pinckney Benton
Score: 18

This code effectively creates a Player class with the ability to set the last_name, first_name, and score attributes through the \_\_init\_\_ method. It utilizes regular expressions to parse the provided string and create instances of the Player class with the captured values. Finally, it displays the information of each player using the attributes of the Player objects.

Question 35

Q

Explain all of the following with examples: re.X, re.MULTILINE, re.DOTALL, re.VERBOSE

Answer

A

Certainly! Let’s explain each of the following flags used in regular expressions with examples:

re.X or re.VERBOSE:
- This flag allows you to write regular expressions in a more readable and organized format by ignoring whitespace and comments within the pattern.
- It enables multiline regular expressions, where you can break the pattern across multiple lines.
- It ignores leading and trailing whitespace in the pattern.
- It ignores comments starting with # within the pattern.
Example:
```python
import repattern = re.compile(r’’’
\d+ # Match one or more digits
\s # Match a whitespace character
\w+ # Match one or more word characters
‘’’, re.X)match = pattern.search(‘123 abc’)
print(match.group()) # Output: 123 abc
```
re.MULTILINE:
- This flag enables multiline matching in regular expressions.
- By default, the ^ and $ anchors match the start and end of the entire string. With re.MULTILINE, they also match the start and end of each line within the string.
Example:
```python
import repattern = re.compile(r’^\w+’, re.MULTILINE)text = ‘'’First line
Second line
Third line’’’matches = pattern.findall(text)
print(matches) # Output: [‘First’, ‘Second’, ‘Third’]
```
re.DOTALL:
- This flag enables the dot (.) metacharacter to match any character, including newline characters (\n).
- By default, the dot metacharacter does not match newline characters.
Example:
```python
import repattern = re.compile(r’Hello.+World’, re.DOTALL)text = ‘'’Hello
This is a multiline
text.
World’’’match = pattern.search(text)
print(match.group()) # Output: Hello\nThis is a multiline\ntext.\nWorld
```
re.VERBOSE (alternative to re.X):
- This flag has the same functionality as re.X but is commonly referred to as re.VERBOSE.
- It allows you to write more readable and organized regular expressions by ignoring whitespace and comments within the pattern.
- It enables multiline regular expressions and ignores leading and trailing whitespace and comments starting with #.
Example:
```python
import repattern = re.compile(r’’’
\d+ # Match one or more digits
\s # Match a whitespace character
\w+ # Match one or more word characters
‘’’, re.VERBOSE)match = pattern.search(‘123 abc’)
print(match.group()) # Output: 123 abc
```

These flags provide additional control and flexibility when working with regular expressions in Python, allowing you to write more readable and expressive patterns.

Question 36

Q

Explain the following with examples: .finditer(), .get_all(), .as_iterable(), .findall()

Answer

A

Certainly! Let’s explain each of the methods you mentioned with examples:

.finditer():
- The .finditer() method is used to find all non-overlapping matches of a regular expression pattern in a string and return an iterator of match objects.
- It provides a way to iterate over multiple matches, accessing each match object individually.
Example:
```python
import repattern = re.compile(r’\d+’)
text = ‘I have 123 apples and 456 oranges.’matches = pattern.finditer(text)
for match in matches:
print(match.group())
```
Output:
```
123
456
```
In this example, .finditer() finds all occurrences of one or more digits in the text string. The resulting match objects are then iterated over, and .group() is called to print each match.
.get_all() (No such method in regular expression module):
- The .get_all() method is not a method available in the regular expression module.
.as_iterable() (No such method in regular expression module):
- The .as_iterable() method is not a method available in the regular expression module.
.findall():
- The .findall() method is used to find all non-overlapping matches of a regular expression pattern in a string and return them as a list of strings.
- It returns a list of all matching substrings, ignoring the capturing groups if present.
Example:
```python
import repattern = re.compile(r’\d+’)
text = ‘I have 123 apples and 456 oranges.’matches = pattern.findall(text)
print(matches)
```
Output:
```
[‘123’, ‘456’]
```
In this example, .findall() finds all occurrences of one or more digits in the text string and returns them as a list. The resulting list contains the matching substrings.

Please note that .get_all() and .as_iterable() are not methods available in the regular expression module. It’s possible you might be referring to methods specific to a particular library or framework, but without further context, I cannot provide specific explanations for them.

Question 37

Q

What would I use to match 5 or more occurrences of a pattern?

A. +,
B. *,
C. {5,},
D. {5}

Answer

A

The correct answer to match 5 or more occurrences of a pattern would be option C: {5,}.

Let’s explain the options:

A. + - This quantifier matches one or more occurrences of the preceding pattern. It is equivalent to {1,}. So, it would match 1 or more occurrences but not specifically 5 or more occurrences.

B. * - This quantifier matches zero or more occurrences of the preceding pattern. It is equivalent to {0,}. So, it would match any number of occurrences including zero occurrences but not specifically 5 or more occurrences.

C. {5,} - This quantifier specifies a minimum number of occurrences. It matches 5 or more occurrences of the preceding pattern. The absence of an upper limit allows it to match any number of occurrences greater than or equal to 5.

D. {5} - This quantifier specifies an exact number of occurrences. It matches exactly 5 occurrences of the preceding pattern. It does not allow for more or fewer occurrences.

Therefore, to specifically match 5 or more occurrences of a pattern, you would use {5,} as the correct option.