Strings
Overview
In the previous lesson, you learned how to write functions to organize and reuse code. Now you'll take a closer look at one of Python's most important data types: strings. Text data is everywhere in data science — from survey responses and product reviews to social media posts and patient notes. In this lesson, you'll learn how to work with and transform string data in Python.
Learning Objective
By the end of this lesson, you will have learned how to:
- Manipulate strings (concatenation, slicing).
- Understand common string methods (replace, split, strip).
Key terms
String: A sequence of characters enclosed in single or double quotes, used to represent text.
Concatenation: Joining two or more strings together end-to-end using the + operator.
Slicing: Extracting a portion of a string by specifying a start and end index.
Index: The numeric position of a character in a string, starting at 0 for the first character.
String method: A built-in function that operates on a string, such as .upper(), .replace(), or .split().
Starter Code
This lesson encourages experimentation. Use the included Colaboratory notebook to run the code as you learn, or read the lesson and then freely experiment with the notebook's code.
Introduction
In data science, a significant portion of real-world data comes in the form of text. Before you can analyze text — whether you're counting words, cleaning survey responses, or preparing data for a machine learning model — you need to be comfortable working with strings. Python provides a rich set of tools to create, combine, and transform string data with minimal code.
Creating Strings
You can create a string by enclosing characters in single quotes, double quotes, or triple quotes (for multi-line strings):
name = "Alex"
city = 'Chicago'
bio = """Alex is a data science student
who lives in Chicago."""
print(name) # Output: Alex
print(bio)
# Output:
# Alex is a data science student
# who lives in Chicago.
String Concatenation
Joining Strings with +
Concatenation lets you combine strings into one:
first_name = "Data"
last_name = "Science"
full_name = first_name + " " + last_name
print(full_name) # Output: Data Science
f-Strings (Formatted String Literals)
A cleaner and more readable way to embed variables inside strings is using f-strings, introduced in Python 3.6:
name = "Alex"
age = 25
message = f"My name is {name} and I am {age} years old."
print(message) # Output: My name is Alex and I am 25 years old.
Wrapping a string with f before the opening quote lets you insert any expression inside {}.
String Indexing and Slicing
Accessing Individual Characters
Each character in a string has an index starting at 0. You can access a character by placing its index in square brackets:
word = "Python"
print(word[0]) # Output: P
print(word[1]) # Output: y
print(word[-1]) # Output: n (negative index counts from the end)
Slicing a String
Use slicing to extract a substring. The syntax is string[start:end], where start is inclusive and end is exclusive:
word = "Python"
print(word[0:3]) # Output: Pyt (characters at index 0, 1, 2)
print(word[2:]) # Output: thon (from index 2 to the end)
print(word[:4]) # Output: Pyth (from start to index 3)
print(word[::2]) # Output: Pto (every 2nd character)
A practical example with text data:
date = "2024-03-15"
year = date[:4]
month = date[5:7]
day = date[8:]
print(year, month, day) # Output: 2024 03 15
Common String Methods
Python strings come with many built-in methods. A method is called using dot notation: string.method().
Changing Case
text = "Hello, World!"
print(text.upper()) # Output: HELLO, WORLD!
print(text.lower()) # Output: hello, world!
print(text.title()) # Output: Hello, World!
strip() — Removing Whitespace
The strip() method removes leading and trailing whitespace (spaces, tabs, newlines). This is extremely useful when cleaning raw data:
raw = " data science "
clean = raw.strip()
print(clean) # Output: data science
print(len(raw)) # Output: 18
print(len(clean)) # Output: 12
Use lstrip() to strip only the left side, or rstrip() for only the right side.
replace() — Substituting Text
replace(old, new) returns a new string with all occurrences of old replaced by new:
sentence = "I love cats. Cats are great."
updated = sentence.replace("cats", "dogs")
print(updated) # Output: I love dogs. Cats are great.
Note that replace() is case-sensitive — "cats" was replaced but "Cats" was not.
split() — Breaking a String into Parts
split(separator) divides a string at every occurrence of the separator and returns a list of the resulting pieces:
csv_row = "Alex,25,Chicago,Data Science"
fields = csv_row.split(",")
print(fields)
# Output: ['Alex', '25', 'Chicago', 'Data Science']
print(fields[0]) # Output: Alex
print(fields[2]) # Output: Chicago
Splitting on whitespace (the default) is useful for tokenizing sentences:
sentence = "Python is great for data science"
words = sentence.split()
print(words)
# Output: ['Python', 'is', 'great', 'for', 'data', 'science']
print(len(words)) # Output: 6
Checking String Contents
text = "Hello123"
print(text.startswith("Hello")) # Output: True
print(text.endswith("456")) # Output: False
print("123" in text) # Output: True
print(text.isdigit()) # Output: False
Putting It Together: Cleaning Text Data
Here's a realistic example that combines several string techniques to clean a messy data entry:
raw_entry = " john DOE , data scientist , new york "
# Strip whitespace, then split on comma
fields = raw_entry.strip().split(",")
# Clean each field
name = fields[0].strip().title()
role = fields[1].strip().title()
location = fields[2].strip().title()
print(f"Name: {name}") # Output: Name: John Doe
print(f"Role: {role}") # Output: Role: Data Scientist
print(f"Location: {location}")# Output: Location: New York
Conclusion
In this lesson, you learned how to work with strings in Python — how to combine them with concatenation and f-strings, extract portions using indexing and slicing, and transform them using methods like strip(), replace(), and split(). These skills are fundamental to cleaning and preparing text data, a task you'll encounter constantly in data science. In the next lesson, you'll learn how to control the flow of your programs using conditional logic.
Practice
Coding Assessment
Practice the concepts from this lesson using this Colaboratory notebook. After completing the exercises, save your notebook to GitHub and submit the link for grading.
Knowledge Check
word = "Python"
print(word[1:4])text = "apple,banana,cherry"
items = text.split(",")
print(items[2])