Python dictionaries store key-value pairs that let you organize and access data efficiently. These versatile data structures use curly braces {}
and colons to map unique keys to their corresponding values.
This guide covers essential dictionary techniques, real-world applications, and debugging tips, with code examples created using Claude, an AI assistant built by Anthropic.
student = {'name': 'Alice', 'age': 22, 'courses': ['Math', 'CS']}
print(student['name'])
print(student['courses'][1])
Alice
CS
Square bracket notation provides direct access to dictionary values through their keys. In the example, student['name']
retrieves 'Alice' by using the exact key match. This method is fast and efficient since dictionaries use hash tables internally for O(1) lookup time.
The square brackets also enable nested access for compound data structures. When accessing student['courses'][1]
, Python first retrieves the courses list, then indexes into position 1 to get 'CS'. This chaining capability makes dictionaries ideal for:
Beyond basic square bracket access, Python dictionaries offer powerful methods and techniques that make data manipulation more robust and flexible.
get()
method for safer accessstudent = {'name': 'Alice', 'age': 22}
print(student.get('name'))
print(student.get('grade', 'Not Found')) # Provides default value if key doesn't exist
Alice
Not Found
The get()
method provides a safer alternative to square bracket notation when accessing dictionary values. Unlike square brackets which raise a KeyError for missing keys, get()
gracefully handles non-existent keys by returning None
or a specified default value.
student.get('name')
retrieves 'Alice' just like square brackets wouldstudent.get('grade', 'Not Found')
demonstrates error prevention by returning 'Not Found' instead of crashing when accessing the missing 'grade' keyThis approach proves especially valuable when working with user input, API responses, or any scenario where dictionary keys might be uncertain. You can seamlessly handle missing data without wrapping code in try-except blocks.
student = {'name': 'Alice', 'age': 22, 'major': 'Computer Science'}
print(list(student.keys()))
print(list(student.values()))
print(list(student.items()))
['name', 'age', 'major']
['Alice', 22, 'Computer Science']
[('name', 'Alice'), ('age', 22), ('major', 'Computer Science')]
Python dictionaries provide three essential view methods that let you examine their contents in different ways. The keys()
method returns all dictionary keys in a list-like format, while values()
gives you just the stored values. For a complete picture, items()
returns key-value pairs as tuples.
student.keys()
outputs ['name', 'age', 'major']
. This view updates automatically when you modify the dictionarystudent.values()
shows ['Alice', 22, 'Computer Science']
. It's useful when you need to process values independentlystudent.items()
creates pairs like ('name', 'Alice')
. This format works perfectly with Python's for loops for iterationConverting these views to lists with the list()
function creates a static snapshot of the dictionary's current state. This proves helpful when you need to store or manipulate the data separately.
person = {
'name': 'Bob',
'address': {
'city': 'New York',
'zip': 10001
}
}
print(person['address']['city'])
print(f"{person['name']} lives in {person['address']['city']}")
New York
Bob lives in New York
Nested dictionaries embed one dictionary inside another, creating hierarchical data structures. The example shows a person
dictionary containing both direct values and a nested address
dictionary.
person['address']['city']
first gets the address dictionary, then retrieves 'New York' from itf-strings
makes it easy to combine values from different nesting levels into readable outputNested dictionaries excel at representing real-world relationships where objects contain other objects. Common examples include user profiles, configuration settings, and JSON data from APIs.
Building on these foundational techniques, Python dictionaries offer powerful features like defaultdict
, dictionary comprehensions, and the |
operator that streamline data manipulation and error handling.
prices = {'apple': 0.5, 'banana': 0.25, 'orange': 0.75}
doubled_prices = {fruit: price * 2 for fruit, price in prices.items()}
filtered_prices = {k: v for k, v in prices.items() if v > 0.3}
print(doubled_prices)
print(filtered_prices)
{'apple': 1.0, 'banana': 0.5, 'orange': 1.5}
{'apple': 0.5, 'orange': 0.75}
Dictionary comprehensions provide a concise way to create new dictionaries by transforming or filtering existing ones. The syntax mirrors list comprehensions but uses curly braces and requires both a key and value expression.
doubled_prices
example creates a new dictionary where each fruit's price is multiplied by 2. It maintains the original keys while transforming only the valuesfiltered_prices
, the comprehension includes a conditional statement if v > 0.3
that selectively includes only fruits with prices above 30 centsThis approach eliminates the need for explicit loops and temporary variables. You can combine transformations and filtering in a single line while maintaining readable code that clearly expresses your intent.
defaultdict
to handle missing keysfrom collections import defaultdict
fruit_count = defaultdict(int)
fruits = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
for fruit in fruits:
fruit_count[fruit] += 1
print(dict(fruit_count))
{'apple': 3, 'banana': 2, 'orange': 1}
defaultdict
automatically handles missing dictionary keys by creating a default value when you access a non-existent key. In this example, defaultdict(int)
initializes new keys with zero, making it perfect for counting occurrences.
fruit_count[fruit] += 1
either increments an existing count or starts from zero for new fruitsThe final output shows how many times each fruit appears in the list. defaultdict
streamlines frequency counting tasks by removing boilerplate key existence checks from your code.
|
operatoruser_info = {'name': 'Charlie', 'age': 30}
additional_info = {'job': 'Developer', 'city': 'San Francisco'}
complete_info = user_info | additional_info
print(complete_info)
{'name': 'Charlie', 'age': 30, 'job': 'Developer', 'city': 'San Francisco'}
The |
operator, introduced in Python 3.9, combines two dictionaries into a new one. This merge operator creates a fresh dictionary containing all key-value pairs from both sources, with later dictionaries taking precedence for duplicate keys.
user_info
with additional_info
, Python creates a new dictionary that preserves the original data structuresdict.update()
or unpacking with double asterisksThe merged result contains all four key-value pairs, making it ideal for combining user data from different sources or building complete configuration objects.
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Building on the advanced techniques we've explored, Python dictionaries power real-world applications from text analysis to interactive menu systems that streamline development workflows.
Dictionaries provide an elegant way to track how often words appear in text by using the words as keys and their frequencies as values, enabling efficient text analysis and natural language processing tasks.
text = "the quick brown fox jumps over the lazy dog"
word_count = {}
for word in text.lower().split():
word_count[word] = word_count.get(word, 0) + 1
print(word_count)
print(f"Most frequent word: {max(word_count, key=word_count.get)}")
This code creates a dictionary to count word occurrences in a text string. The text.lower().split()
converts the string to lowercase and breaks it into individual words. For each word, the code uses get()
to safely retrieve its current count from the dictionary, defaulting to 0 if the word isn't found yet. It then increments that count by 1.
The final line finds the most frequent word using max()
with a key function. The key=word_count.get
parameter tells max()
to compare words based on their frequency values rather than the words themselves.
get()
prevents errors when encountering new wordsdict
as a command dispatcherDictionaries excel as command dispatchers by mapping user inputs directly to functions, enabling clean and maintainable menu-driven interfaces that eliminate complex if-elif
chains.
def show_help():
return "Displaying help information"
def process_order():
return "Processing your order"
commands = {
'help': show_help,
'order': process_order
}
user_input = 'help'
result = commands.get(user_input, lambda: "Invalid command")()
print(result)
This code demonstrates a flexible command pattern implementation using a dictionary as a function lookup table. The commands
dictionary maps string keys to their corresponding function objects, creating a direct relationship between user inputs and actions.
show_help
and process_order
functions serve as simple command handlerscommands.get(user_input)
, Python retrieves the matching function based on the input stringlambda: "Invalid command"
provides a fallback response for unknown commandsThe trailing parentheses ()
immediately call the retrieved function. This approach scales elegantly as you add more commands. Simply define new functions and add them to the dictionary without modifying the execution logic.
Python dictionaries can trigger subtle errors that impact code reliability when you access keys incorrectly, modify data during loops, or misunderstand object references.
KeyError
when accessing non-existent keysAccessing dictionary keys directly with square bracket notation can trigger a KeyError
when the key doesn't exist. This common pitfall affects Python developers who assume a key's presence without verification. The following code demonstrates how this error manifests when trying to access a non-existent phone
key.
user_data = {'name': 'John', 'email': 'john@example.com'}
# This will raise a KeyError
phone = user_data['phone']
print(f"User's phone: {phone}")
The code attempts to directly access a dictionary key that doesn't exist in user_data
. Since Python can't find a matching 'phone'
key, it immediately halts execution with a KeyError
. The following code demonstrates a robust solution to this common challenge.
user_data = {'name': 'John', 'email': 'john@example.com'}
# Using get() method with a default value
phone = user_data.get('phone', 'Not provided')
print(f"User's phone: {phone}")
The get()
method provides a safer way to access dictionary values by returning a default value when a key doesn't exist. Instead of crashing with a KeyError
, the code gracefully handles missing data by returning 'Not provided' as a fallback.
get()
whenever you're unsure if a key exists in your dictionaryThis pattern proves especially valuable in production environments where robust error handling prevents application crashes and improves user experience.
Modifying a dictionary while iterating through it can trigger a RuntimeError
. Python raises this error to prevent unpredictable behavior when you add or remove dictionary items during a for
loop. The code below demonstrates this common pitfall when trying to remove low-scoring students.
scores = {'Alice': 85, 'Bob': 45, 'Charlie': 92, 'Dave': 38}
# This will raise RuntimeError: dictionary changed size during iteration
for name, score in scores.items():
if score < 50:
del scores[name]
print(scores)
The for
loop attempts to modify the dictionary's structure by deleting entries while Python actively uses that same structure for iteration. This creates an unstable state that Python prevents by raising an error. Let's examine a safer approach in the code below.
scores = {'Alice': 85, 'Bob': 45, 'Charlie': 92, 'Dave': 38}
# Create a new dictionary instead of modifying during iteration
passing_scores = {name: score for name, score in scores.items() if score >= 50}
print(passing_scores)
Dictionary comprehension offers a safer alternative to modifying dictionaries during iteration. Instead of deleting items from the original dictionary, passing_scores
creates a new dictionary that includes only the entries meeting our criteria. This approach prevents runtime errors and produces cleaner, more maintainable code.
Python dictionaries use reference semantics when assigned to new variables. This means simply assigning a dictionary to another variable creates a reference to the same data structure instead of a separate copy. The code below demonstrates how modifying one reference affects all others pointing to the same dictionary.
original = {'name': 'Alice', 'scores': [85, 90, 78]}
duplicate = original # This doesn't create a new copy
duplicate['name'] = 'Bob' # Modifies both dictionaries!
print(f"Original: {original}")
print(f"Duplicate: {duplicate}")
When you assign duplicate = original
, Python creates a new reference to the same dictionary in memory. Any changes to either variable affect both because they point to identical data. The code below demonstrates how to properly create independent copies.
import copy
original = {'name': 'Alice', 'scores': [85, 90, 78]}
duplicate = copy.deepcopy(original) # Creates independent copy
duplicate['name'] = 'Bob' # Only modifies the duplicate
print(f"Original: {original}")
print(f"Duplicate: {duplicate}")
The copy.deepcopy()
function creates a completely independent copy of a dictionary and its nested objects. Unlike shallow copying or direct assignment, deep copying ensures that modifying the duplicate won't affect the original data structure. This proves essential when working with complex dictionaries containing nested lists, dictionaries, or custom objects.
deepcopy()
when you need to create truly independent copies for data processing or state management=
only creates a new reference to the same dictionaryThis pattern becomes particularly important in larger applications where dictionaries pass between functions or store in data structures. Proper copying prevents subtle bugs from propagating through your system.
Claude combines expert Python knowledge with intuitive teaching abilities to guide you through dictionary challenges and complex data structures. This AI assistant from Anthropic understands your code's context and provides targeted suggestions to improve your implementations.
get()
or defaultdict
|
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