1. Regular Expressions in Python (110m) Flashcards
r means raw string
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()
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))
~~~
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.
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?
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 toopen(). 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 byopen(). 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.”
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.
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
What is unicode word character?
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.
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.
~~~
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
remodule, 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
datavariable using theread()method. - The file is closed with the
close()method called on thenames_fileobject to release system resources. - The variable
last_nameis assigned the value ‘Love’ as a raw string. - The variable
first_nameis 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 thedatastring using there.match()function. - The output will be
Nonebecause the pattern ‘\w, \w’ does not match any portion of thedatastring.
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
remodule is imported, and the ‘names.txt’ file is opened and read into thedatavariable. - The
last_nameandfirst_namevariables 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 thedatastring using there.search()function. - The output will be the first occurrence of a matching pattern in the
datastring, 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
remodule and opens the ‘names.txt’ file. - The file contents are read into the
datavariable, and the file is closed. - The variables
last_nameandfirst_nameare 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 thedatastring using there.search()function. The\(and\)escape sequences are used to match the literal parentheses, while\drepresents a digit. - The output will be the first occurrence of a matching pattern in the
datastring, 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.”
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?
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.
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))
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 therprefix. It successfully matches a phone number pattern of the form(###) ###-####within thedatastring, 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 therprefix. 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 therprefix. 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 thedatastring, 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.
I want to match the 5 in the string. What escape character should I use?
re.search(r'\_\_\_\_', 'Johnny-5')
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’.
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".
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.
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
~~~
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.
What is the difference between re.findall and re.search?
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.
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 to Challenge:
~~~
import re
def phone_numbers(user_string):
return re.findall(r’\d{3}-\d{3}-\d{4}’, user_string)
~~~
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']
»> 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']
[‘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.findallis a function from theremodule 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 thestring. Let’s analyze this pattern step by step:-
\bdenotes a word boundary. It matches the empty string at the beginning or end of a word. -
\wmatches any word character (alphanumeric or underscore). -
{count,}specifies that\wshould occur at leastcounttimes or more consecutively. -
\bdenotes another word boundary to ensure that only complete words are matched.
-
-
str(count)converts thecountvariable to a string so that it can be concatenated with the regular expression pattern. -
stringis 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.
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?
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.
