Comprehensive Notes on Python Dictionaries for Emancipation Edutech Students

Introduction to Python Dictionaries

Python dictionaries are an essential data structure in Python that store data in key-value pairs. They are highly versatile and widely used in various programming tasks, from simple data storage to complex data manipulation and retrieval. This guide will provide in-depth knowledge about Python dictionaries, including their usage, advantages, and comparison with other iterables. We will also explore real-world examples and industry applications.

What is a Python Dictionary?

A dictionary in Python is a collection of key-value pairs where each key is unique. Dictionaries are mutable, meaning they can be changed after creation. They are defined using curly braces {} with the syntax:

my_dict = {
    "key1": "value1",
    "key2": "value2",
    "key3": "value3"

Key Characteristics of Dictionaries

  1. Unordered: The elements in a dictionary are not stored in a specific order.
  2. Mutable: Dictionaries can be modified after creation.
  3. Indexed by Keys: Elements are accessed via their keys, not indices.

Creating a Dictionary

# Creating an empty dictionary
empty_dict = {}

# Creating a dictionary with initial values
person = {
    "name": "John Doe",
    "age": 30,
    "city": "Ranchi"

Accessing Dictionary Elements

To access values in a dictionary, use the key inside square brackets [] or the get() method.

print(person["name"])  # Output: John Doe
print(person.get("age"))  # Output: 30

Modifying a Dictionary

You can add or update key-value pairs using the assignment operator =.

person["age"] = 31  # Update existing key
person["profession"] = "Software Developer"  # Add new key-value pair

Removing Elements

Use del to remove a key-value pair, or use methods like pop() or popitem().

del person["city"]
print(person)  # {'name': 'John Doe', 'age': 31, 'profession': 'Software Developer'}

profession = person.pop("profession")
print(profession)  # Output: Software Developer
print(person)  # {'name': 'John Doe', 'age': 31}

Dictionary Methods

Here are some common dictionary methods:

  • keys(): Returns a view object of all the keys.
  • values(): Returns a view object of all the values.
  • items(): Returns a view object of all key-value pairs.
print(person.keys())  # dict_keys(['name', 'age'])
print(person.values())  # dict_values(['John Doe', 31])
print(person.items())  # dict_items([('name', 'John Doe'), ('age', 31)])

Comparison with Other Iterables

Dictionaries vs. Lists:

  • Lists: Ordered, accessed via indices, duplicates allowed.
  • Dictionaries: Unordered, accessed via keys, no duplicate keys.

Dictionaries vs. Tuples:

  • Tuples: Immutable, ordered, accessed via indices.
  • Dictionaries: Mutable, unordered, accessed via keys.

Dictionaries vs. Sets:

  • Sets: Unordered, mutable, no duplicates, only values (no key-value pairs).
  • Dictionaries: Unordered, mutable, no duplicate keys, store key-value pairs.

Industry Use of Dictionaries

Dictionaries are extensively used in various fields:

  • Web Development: Storing session data, form data, and configuration settings.
  • Data Science: Managing data in JSON-like formats, storing results from data analysis.
  • Machine Learning: Storing model parameters, hyperparameters, and training data.
  • Automation: Managing configuration files and scripts.

Real Projects Examples

Example 1: Storing Configuration Settings

config = {
    "host": "localhost",
    "port": 8080,
    "debug": True

# Accessing configuration settings
print(f"Server running on {config['host']}:{config['port']} with debug={config['debug']}")

Example 2: JSON Data Handling

import json

# Sample JSON data
json_data = '{"name": "Alice", "age": 25, "city": "New York"}'

# Convert JSON to dictionary
data = json.loads(json_data)

Latest Updates

With Python’s continual development, dictionaries have seen optimizations and new features:

  • Python 3.7+: Dictionaries maintain insertion order, making them act like ordered dictionaries.
  • PEP 584: Introduced union operators | and |= for dictionaries in Python 3.9.
# Example of dictionary union
dict1 = {"a": 1, "b": 2}
dict2 = {"b": 3, "c": 4}

# Union operation
result = dict1 | dict2
print(result)  # {'a': 1, 'b': 3, 'c': 4}

Myths About Dictionaries

  • Myth: Dictionaries are slow because they are unordered.
  • Fact: Dictionaries use a hash table for fast lookups, making them very efficient.
  • Myth: Dictionaries are not as versatile as lists.
  • Fact: Dictionaries are extremely versatile and can be used in many contexts where lists are not suitable.

Fun Facts

  • The concept of dictionaries is similar to “hash maps” in other programming languages.
  • Python’s dictionary implementation is highly optimized, making it a go-to choice for many applications.

Visual Representation

Dictionary vs. List Access Speed

Here’s a chart comparing the access speed of dictionaries and lists:

import matplotlib.pyplot as plt
import time

# Sample data
sizes = [10**i for i in range(1, 6)]
list_times = []
dict_times = []

for size in sizes:
    sample_list = list(range(size))
    sample_dict = {i: i for i in range(size)}

    # Timing list access
    start = time.time()
    _ = sample_list[-1]
    list_times.append(time.time() - start)

    # Timing dictionary access
    start = time.time()
    _ = sample_dict[size - 1]
    dict_times.append(time.time() - start)

plt.plot(sizes, list_times, label='List')
plt.plot(sizes, dict_times, label='Dictionary')
plt.ylabel('Time (seconds)')
plt.title('Access Time: List vs Dictionary')


Understanding Python dictionaries is crucial for any aspiring programmer. Their flexibility, efficiency, and powerful features make them indispensable in various applications, from web development to data science. Emancipation Edutech in Ranchi is committed to providing comprehensive training on Python and other programming languages, ensuring students are well-equipped with the necessary skills to excel in the tech industry.

For more information on our courses and offerings, visit our website or contact us at

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