Python - Maps

Introduction to Maps in Python:

Maps, often known as dictionaries or associative arrays in other programming languages, are an important data structure in Python. They allow you to store key-value pairs, with each key being unique within the map. Maps are extremely useful and efficient for a variety of purposes, including storing setups, caching results, and organizing data.

In Python, maps are implemented via the 'dict' class. They enable the efficient retrieval and manipulation of data by associating keys with their values. Here's an explanation of fundamental concepts about maps in Python:

Key-Value Pairs:

Each element in a map consists of a key and its value. Keys are unique to the map and are used to access linked values rapidly.

Declaration:

To declare a map, use curly braces '{}' and supply key-value pairs separated by colons. For example:

Syntax:

Accessing Values:

You can retrieve the value associated with a key by enclosing it in square brackets [] and giving the key. For example:

Syntax:

Adding and Updating Entries:

To add new key-value pairs to a map or change existing ones, assign a value to a key:

Syntax:

my_map['grape'] = 9  # Adding a new entry
my_map['apple'] = 8  # Updating the value of an existing entry

Removing Entries:

To remove entries from a map, use the 'del' keyword or the 'pop()' method:

Syntax:

del my_map['banana']  # Removing an entry using del
my_map.pop('orange')  # Removing an entry using pop()

Iterating Over Entries:

Use a 'for' loop to traverse over the key-value pairs in a map:

Syntax:

for key, value in my_map.items():
  print(key, value)

Checking for Key Existence:

The 'in' keyword can be used to determine whether or not a key exists in a map:

Syntax:

if 'apple' in my_map:
  print("Apple is in the map")

Code:

my_map = {'apple': 5, 'banana': 7, 'orange': 3}
print(my_map['apple'])  
my_map['grape'] = 9  # Adding a new entry
my_map['apple'] = 8  # Updating the value of an existing entry
del my_map['banana']   # Removing an entry using del
my_map.pop('orange')  # Removing an entry using pop()
for key, value in my_map.items():
    print(key, value)
if 'apple' in my_map:
    print("Apple is in the map")
print(len(my_map))  
keys = my_map.keys()      # Retrieve keys
values = my_map.values()  # Retrieve values
items = my_map.items()    # Retrieve key-value pairs
my_map.clear()  # Remove all key-value pairs
copied_map = my_map.copy()   # Shallow copy using copy()
new_map = dict(my_map)       # Shallow copy using dict()
nested_map = {'outer_key': {'inner_key': 'value'}}
print(my_map.get('nonexistent_key', 'default_value'))  
another_map = {'pear': 6, 'kiwi': 4}
my_map.update(another_map)  # Merge another_map into my_map
keys_list = ['a', 'b', 'c']
values_list = [1, 2, 3]
my_map = {key: value for key, value in zip(keys_list, values_list)}
print(my_map)

Output:

grape 9
apple 8
1
default_value
{'a': 1, 'b': 2, 'c': 3}

This output shows the many actions performed on the dictionary' my_map', such as adding, updating, and removing entries, reading, iterating over, verifying existence, and copying the dictionary.

Length:

The 'len()' function can be used to find out how many key-value pairs are in a map:

Syntax:

Copying a Map:

To make a shallow copy of a map, use the 'copy()' method or the built-in 'dict()' constructor:

Syntax:

copied_map = my_map.copy()
new_map = dict(my_map)

Nested Maps:

In Python, maps can contain other maps or any other data types as values, which allows for nested data structures:

Syntax:

Merging Maps:

You can merge two maps using the 'update()' method, which adds all key-value pairs from one map to another:

Syntax:

another_map = {'pear': 6, 'kiwi': 4}
my_map.update(another_map)  # Merge another_map into my_map

Dictionary Comprehension:

Dictionary comprehension, like list comprehension, can be used to construct maps compactly.

Syntax:

These operations give a comprehensive toolkit for working with maps in Python, empowering you to productively store, control, and recover information based on keys.

In conclusion, maps, also referred to as dictionaries, are essential Python data structures that offer a flexible and effective means of storing key-value pairs. The basics of working with maps were covered throughout this talk, including declaration, value access, adding, updating, and deleting entries, iterating over items, determining whether a key exists, and a variety of utility functions like length, values, keys, retrieving items, clearing, copying, merging, and dictionary comprehension. Maps are useful tools for organizing and manipulating data in Python because of their performance and flexibility. It allows for a variety of applications in data processing, algorithm creation, and software development. Maps provide a reliable solution for effective key-based data management in Python programs, whether handling setups, caching results, or organizing large data structures.

Next TopicPython positional only arguments

← prev next →