Python Dictionaries: Key-Value Pairs for Efficient Data Mapping

Beginner

Python dictionaries are mutable, unordered collections that store data in key-value pairs. They are ideal for fast lookups and representing relationships between data.

Core Concept

Dictionaries use unique keys to access associated values. Keys must be immutable (e.g., strings, numbers, tuples), while values can be of any data type.

Basic Example

basics.py

# Creating a dictionary
student = {
  "name": "Alice",
  "age": 20,
  "major": "Computer Science"
}

# Accessing values
print(student["name"])
print(student.get("age"))

# Adding a new key-value pair
student["gpa"] = 3.8
print(student)

  

How It Works

When you create a dictionary, Python uses a hashing mechanism. This allows for nearly constant-time (O(1) on average) retrieval of values based on their keys, making dictionaries highly efficient for searching and data association.

Advanced Example

advanced.py

# Dictionary comprehensions
squares = {x: x**2 for x in range(1, 6)}
print(squares)

# Using dictionary methods
courses = {"CS101": "Intro to Programming", "MATH203": "Calculus III"}
print(courses.keys())
print(courses.values())
print(courses.items())

# Iterating and updating
for code, name in courses.items():
    if code == "CS101":
        courses[code] = "Introduction to Programming (Updated)"
print(courses)

  

Common Use Cases

• Representing JSON data.
• Storing configuration settings.
• Mapping unique identifiers to objects.
• Counting frequencies of items.

Common Pitfalls

• Using mutable objects (like lists) as keys, which raises a `TypeError`.
• Forgetting that dictionary keys are unique; duplicate keys will overwrite previous entries.
• Accessing a non-existent key directly without using `.get()` or checking for existence can raise a `KeyError`.

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FAQs

Conclusion

Dictionaries are fundamental for organizing and retrieving data efficiently in Python. Their key-value structure makes them versatile for numerous programming tasks.