How to create dictionary from another

Introduction

In Python programming, creating dictionaries from existing data structures is a fundamental skill that enables developers to efficiently transform and manipulate data. This tutorial explores various techniques for generating new dictionaries, providing practical insights into Python's powerful dictionary creation methods and comprehension strategies.

Skills Graph

Dictionary Fundamentals

What is a Dictionary?

A dictionary in Python is a versatile and powerful data structure that stores key-value pairs. Unlike lists that use numeric indices, dictionaries use unique keys to access and manage data efficiently.

Key Characteristics

Creating a Dictionary

## Empty dictionary
empty_dict = {}
empty_dict_constructor = dict()

## Dictionary with initial values
student = {
    "name": "Alice",
    "age": 22,
    "courses": ["Python", "Data Science"]
}

Dictionary Key Types

Dictionaries support various key types, but keys must be immutable:

• Strings
• Numbers
• Tuples
• Immutable objects

## Valid dictionary keys
mixed_dict = {
    "name": "John",
    42: "Answer",
    (1, 2): "Coordinate"
}

Basic Dictionary Operations

Adding Elements

student["grade"] = "A"

Accessing Elements

name = student["name"]
age = student.get("age", 0)  ## Default value if key not found

Modifying Elements

student["age"] = 23

Removing Elements

del student["courses"]
student.pop("grade")

Dictionary Methods

Performance Considerations

Dictionaries in Python are implemented as hash tables, providing:

• O(1) average time complexity for insertion, deletion, and lookup
• Efficient for large datasets
• Memory-efficient storage

Best Practices

1. Use meaningful, descriptive keys
2. Handle potential KeyError exceptions
3. Prefer .get() method for safe access
4. Consider using defaultdict for complex scenarios

At LabEx, we recommend mastering dictionary fundamentals to enhance your Python programming skills efficiently.

Creating New Dictionaries

Dictionary Construction Methods

1. Literal Initialization

## Direct initialization
person = {
    "name": "John",
    "age": 30,
    "city": "New York"
}

2. Dictionary Constructor

## Using dict() constructor
student = dict(
    name="Alice", 
    age=22, 
    major="Computer Science"
)

Advanced Dictionary Creation Techniques

Comprehension Method

## Dictionary comprehension
squares = {x: x**2 for x in range(6)}
## Result: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Transformation Strategies

Conversion from Lists

keys = ['a', 'b', 'c']
values = [1, 2, 3]
converted_dict = dict(zip(keys, values))
## Result: {'a': 1, 'b': 2, 'c': 3}

Nested Dictionary Creation

users = {
    'user1': {'name': 'Alice', 'age': 30},
    'user2': {'name': 'Bob', 'age': 25}
}

Specialized Dictionary Creation

Using fromkeys() Method

## Create dictionary with default value
default_dict = dict.fromkeys(['a', 'b', 'c'], 0)
## Result: {'a': 0, 'b': 0, 'c': 0}

Dictionary Copying Techniques

Copy Examples

import copy

original = {'a': [1, 2, 3]}
shallow_copy = original.copy()
deep_copy = copy.deepcopy(original)

Dynamic Dictionary Generation

## Generate dictionary from function
def generate_dict(start, end):
    return {x: x**2 for x in range(start, end)}

power_dict = generate_dict(1, 5)
## Result: {1: 1, 2: 4, 3: 9, 4: 16}

Performance Considerations

• Dictionary comprehensions are faster than multiple .update() calls
• Use appropriate method based on data source
• Consider memory usage for large dictionaries

Best Practices

1. Choose the most readable method
2. Use comprehensions for simple transformations
3. Be cautious with nested dictionary creation
4. Prefer explicit initialization for complex structures

At LabEx, we recommend mastering these dictionary creation techniques to write more efficient and expressive Python code.

Practical Transformation Techniques

Dictionary Transformation Overview

1. Filtering Dictionaries

Basic Filtering

## Filter dictionary by condition
original = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
filtered = {k: v for k, v in original.items() if v > 2}
## Result: {'c': 3, 'd': 4}

Filtering with Functions

def is_even(item):
    return item[1] % 2 == 0

original = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
even_dict = dict(filter(is_even, original.items()))
## Result: {'b': 2, 'd': 4}

2. Mapping Transformations

Value Mapping

## Transform dictionary values
prices = {'apple': 0.5, 'banana': 0.3, 'orange': 0.6}
tax_prices = {k: v * 1.1 for k, v in prices.items()}
## Result: Adds 10% tax to each price

Key and Value Transformation

## Uppercase keys, square values
original = {'a': 1, 'b': 2, 'c': 3}
transformed = {k.upper(): v**2 for k, v in original.items()}
## Result: {'A': 1, 'B': 4, 'C': 9}

3. Dictionary Merging Techniques

Merging Examples

## Merge dictionaries
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}

## Method 1: update()
dict1.update(dict2)

## Method 2: Merge Operator (Python 3.9+)
merged = dict1 | dict2

## Method 3: Unpacking
merged = {**dict1, **dict2}

4. Dictionary Inversion

Inverting Key-Value Pairs

## Swap keys and values
original = {'a': 1, 'b': 2, 'c': 3}
inverted = {v: k for k, v in original.items()}
## Result: {1: 'a', 2: 'b', 3: 'c'}

Handling Duplicate Values

## Manage duplicate values during inversion
original = {'a': 1, 'b': 2, 'c': 2}
inverted = {}
for k, v in original.items():
    inverted.setdefault(v, []).append(k)
## Result: {1: ['a'], 2: ['b', 'c']}

5. Advanced Transformation Techniques

Nested Dictionary Transformation

## Transform nested dictionary
users = {
    'user1': {'age': 25, 'city': 'New York'},
    'user2': {'age': 30, 'city': 'San Francisco'}
}

young_users = {
    k: v for k, v in users.items() if v['age'] < 30
}

Performance Considerations

• Dictionary comprehensions are generally faster
• Use built-in methods for simple transformations
• Be mindful of memory usage with large dictionaries

Best Practices

1. Choose the most readable transformation method
2. Handle potential key conflicts
3. Use type hints for complex transformations
4. Consider performance for large datasets

At LabEx, we recommend mastering these dictionary transformation techniques to write more flexible and efficient Python code.

Summary

By mastering these dictionary transformation techniques in Python, developers can write more concise, readable, and efficient code. Understanding how to create dictionaries from other data structures empowers programmers to handle complex data manipulation tasks with ease and elegance, ultimately improving their Python programming skills.