Python Dictionary Comprehension

Dictionary comprehension provides a concise way to create dictionaries from an iterable.

It works similarly to list comprehension, but instead of producing a list, it produces a dictionary containing key-value pairs.

Basic Syntax

{key: value for item in iterable}

For example:

numbers = [1, 2, 3, 4, 5]

squares = {
    x: x ** 2
    for x in numbers
}

print(squares)

Output:

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Here:

x → item from the iterable
x → dictionary key
x ** 2 → dictionary value
{} → creates a dictionary

1. Basic Dictionary Comprehension

Let's create a dictionary containing numbers and their squares.

numbers = [1, 2, 3, 4, 5]

squares = {
    x: x ** 2
    for x in numbers
}

print(squares)

Output:

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

The equivalent normal for loop is:

numbers = [1, 2, 3, 4, 5]

squares = {}

for x in numbers:
    squares[x] = x ** 2

print(squares)

Dictionary comprehension provides a shorter way to write this.

2. Create Number and Cube Dictionary

numbers = [1, 2, 3, 4, 5]

cubes = {
    x: x ** 3
    for x in numbers
}

print(cubes)

Output:

{1: 1, 2: 8, 3: 27, 4: 64, 5: 125}

3. Create Number and Double Dictionary

numbers = [10, 20, 30, 40]

doubled = {
    x: x * 2
    for x in numbers
}

print(doubled)

Output:

{10: 20, 20: 40, 30: 60, 40: 80}

4. Dictionary from Two Lists

Suppose we have separate lists of names and marks:

names = ["Ram", "Sita", "Hari", "Gita"]
marks = [75, 85, 65, 90]

We can combine them using zip():

students = {
    name: mark
    for name, mark in zip(names, marks)
}

print(students)

Output:

{'Ram': 75, 'Sita': 85, 'Hari': 65, 'Gita': 90}

This is a very common use of dictionary comprehension.

5. Dictionary Comprehension with a Condition

Just like list comprehension, dictionary comprehension can use an if condition.

Syntax

{key: value for item in iterable if condition}

Example:

numbers = [1, 2, 3, 4, 5, 6]

even_numbers = {
    x: x ** 2
    for x in numbers
    if x % 2 == 0
}

print(even_numbers)

Output:

{2: 4, 4: 16, 6: 36}

Only even numbers are included.

6. Filter Students by Marks

students = {
    "Ram": 75,
    "Sita": 85,
    "Hari": 35,
    "Gita": 90
}

passed_students = {
    name: marks
    for name, marks in students.items()
    if marks >= 40
}

print(passed_students)

Output:

{'Ram': 75, 'Sita': 85, 'Gita': 90}

Here, students with marks below 40 are excluded.

7. Get Students with Distinction

students = {
    "Ram": 75,
    "Sita": 85,
    "Hari": 35,
    "Gita": 90
}

distinction = {
    name: marks
    for name, marks in students.items()
    if marks >= 80
}

print(distinction)

Output:

{'Sita': 85, 'Gita': 90}

8. Modify Dictionary Values

Dictionary comprehension can be used to create a new dictionary with modified values.

students = {
    "Ram": 75,
    "Sita": 85,
    "Hari": 65
}

updated_marks = {
    name: marks + 5
    for name, marks in students.items()
}

print(updated_marks)

Output:

{'Ram': 80, 'Sita': 90, 'Hari': 70}

The original dictionary is not modified.

9. Convert Values to Uppercase

students = {
    "ram": 75,
    "sita": 85,
    "hari": 65
}

updated = {
    name.upper(): marks
    for name, marks in students.items()
}

print(updated)

Output:

{'RAM': 75, 'SITA': 85, 'HARI': 65}

10. Convert Values to Lowercase

students = {
    "RAM": 75,
    "SITA": 85,
    "HARI": 65
}

updated = {
    name.lower(): marks
    for name, marks in students.items()
}

print(updated)

Output:

{'ram': 75, 'sita': 85, 'hari': 65}

11. Create Dictionary of Even and Odd Numbers

We can use an expression with if-else.

numbers = [1, 2, 3, 4, 5]

result = {
    x: "Even" if x % 2 == 0 else "Odd"
    for x in numbers
}

print(result)

Output:

{1: 'Odd', 2: 'Even', 3: 'Odd', 4: 'Even', 5: 'Odd'}

Notice that all numbers are included because we are using if-else, not a filter if.

12. Create Number Classification Dictionary

numbers = [-3, -2, -1, 0, 1, 2, 3]

result = {
    x: "Positive" if x > 0
    else "Negative" if x < 0
    else "Zero"
    for x in numbers
}

print(result)

Output:

{-3: 'Negative', -2: 'Negative', -1: 'Negative', 0: 'Zero', 1: 'Positive', 2: 'Positive', 3: 'Positive'}

13. Dictionary Comprehension with `range()`

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

print(squares)

Output:

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

14. Create Multiplication Dictionary

table = {
    x: x * 5
    for x in range(1, 11)
}

print(table)

Output:

{1: 5, 2: 10, 3: 15, 4: 20, 5: 25, 6: 30, 7: 35, 8: 40, 9: 45, 10: 50}

15. Dictionary Comprehension from a String

Strings are iterable, so they can also be used.

word = "Python"

result = {
    char: ord(char)
    for char in word
}

print(result)

Output:

{'P': 80, 'y': 121, 't': 116, 'h': 104, 'o': 111, 'n': 110}

Here, ord() returns the Unicode code point of a character.

16. Count Characters in a String

A simple dictionary comprehension can create a character-frequency dictionary when combined with count().

word = "banana"

result = {
    char: word.count(char)
    for char in set(word)
}

print(result)

Output:

{'b': 1, 'a': 3, 'n': 2}

The order can vary because set is unordered.

For large strings, there are more efficient approaches, which we will cover later when discussing dictionaries and data processing.

17. Swap Dictionary Keys and Values

Suppose we have:

students = {
    "Ram": 75,
    "Sita": 85,
    "Hari": 65
}

We can swap the keys and values:

result = {
    marks: name
    for name, marks in students.items()
}

print(result)

Output:

{75: 'Ram', 85: 'Sita', 65: 'Hari'}

This only works safely when the values are unique and suitable as dictionary keys.

18. Practical Example : Product Prices

products = {
    "Laptop": 80000,
    "Mouse": 1500,
    "Keyboard": 3000,
    "Monitor": 25000
}

discounted = {
    product: price * 0.9
    for product, price in products.items()
}

print(discounted)

Output:

{'Laptop': 72000.0, 'Mouse': 1350.0, 'Keyboard': 2700.0, 'Monitor': 22500.0}

Here, every product receives a 10% discount.

19. Filter Expensive Products

products = {
    "Laptop": 80000,
    "Mouse": 1500,
    "Keyboard": 3000,
    "Monitor": 25000
}

expensive = {
    product: price
    for product, price in products.items()
    if price > 20000
}

print(expensive)

Output:

{'Laptop': 80000, 'Monitor': 25000}

20. Practical Example : Employee Salaries

employees = {
    "Ram": 35000,
    "Sita": 55000,
    "Hari": 45000,
    "Gita": 70000
}

high_salary = {
    name: salary
    for name, salary in employees.items()
    if salary >= 50000
}

print(high_salary)

Output:

{'Sita': 55000, 'Gita': 70000}

21. Practical Example : Increase Salaries

employees = {
    "Ram": 35000,
    "Sita": 55000,
    "Hari": 45000
}

updated_salaries = {
    name: salary * 1.10
    for name, salary in employees.items()
}

print(updated_salaries)

Output:

{'Ram': 38500.0, 'Sita': 60500.00000000001, 'Hari': 49500.00000000001}

The values are increased by 10%.

22. Dictionary Comprehension with Nested Data

Consider a list of students:

students = [
    {"name": "Ram", "marks": 75},
    {"name": "Sita", "marks": 85},
    {"name": "Hari", "marks": 65}
]

We can convert it into a dictionary:

result = {
    student["name"]: student["marks"]
    for student in students
}

print(result)

Output:

{'Ram': 75, 'Sita': 85, 'Hari': 65}

This is very useful when converting structured data.

23. Practical Example : Student Status

students = {
    "Ram": 75,
    "Sita": 35,
    "Hari": 65,
    "Gita": 90
}

status = {
    name: "Pass" if marks >= 40 else "Fail"
    for name, marks in students.items()
}

print(status)

Output:

{'Ram': 'Pass', 'Sita': 'Fail', 'Hari': 'Pass', 'Gita': 'Pass'}

24. Dictionary Comprehension vs Normal Loop

Normal Loop

numbers = [1, 2, 3, 4, 5]

squares = {}

for x in numbers:
    squares[x] = x ** 2

print(squares)

Dictionary Comprehension

numbers = [1, 2, 3, 4, 5]

squares = {
    x: x ** 2
    for x in numbers
}

print(squares)

Both produce:

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Dictionary comprehension is more compact.

25. Dictionary Comprehension vs `map()`

We can sometimes use map() to transform dictionary data, but dictionary comprehension is usually more direct when we need to construct key-value pairs.

students = {
    "Ram": 75,
    "Sita": 85,
    "Hari": 65
}

updated = {
    name: marks + 5
    for name, marks in students.items()
}

print(updated)

Output:

{'Ram': 80, 'Sita': 90, 'Hari': 70}

26. Combining Dictionary and List Comprehension

We can use comprehensions together.

students = {
    "Ram": [75, 80, 85],
    "Sita": [85, 90, 95],
    "Hari": [60, 65, 70]
}

averages = {
    name: sum(marks) / len(marks)
    for name, marks in students.items()
}

print(averages)

Output:

{'Ram': 80.0, 'Sita': 90.0, 'Hari': 65.0}

This is a useful example for processing structured data.

Important Points to Remember

Dictionary comprehension creates a new dictionary concisely.
Basic syntax:

{key: value for item in iterable}

It can include a filtering condition:

{key: value for item in iterable if condition}

It can use if-else:

{key: value_if_true if condition else value_if_false for item in iterable}

dict.items() is commonly used when processing an existing dictionary.
zip() can combine multiple lists into key-value pairs.
Dictionary comprehension creates a new dictionary.
It is useful for filtering, transforming, and restructuring data.

Easy way to remember

List Comprehension       → []
Dictionary Comprehension → {}
Set Comprehension        → {}

Basic dictionary comprehension:

{
    key: value
    for item in iterable
}

With filtering:

{
    key: value
    for item in iterable
    if condition
}

With if-else:

{
    key: value_if_true if condition else value_if_false
    for item in iterable
}

Basic Syntax

1. Basic Dictionary Comprehension

2. Create Number and Cube Dictionary

3. Create Number and Double Dictionary

4. Dictionary from Two Lists

5. Dictionary Comprehension with a Condition

Syntax

6. Filter Students by Marks

7. Get Students with Distinction

8. Modify Dictionary Values

9. Convert Values to Uppercase

10. Convert Values to Lowercase

11. Create Dictionary of Even and Odd Numbers

12. Create Number Classification Dictionary

13. Dictionary Comprehension with range()

14. Create Multiplication Dictionary

15. Dictionary Comprehension from a String

16. Count Characters in a String

17. Swap Dictionary Keys and Values

18. Practical Example : Product Prices

19. Filter Expensive Products

20. Practical Example : Employee Salaries

21. Practical Example : Increase Salaries

22. Dictionary Comprehension with Nested Data

23. Practical Example : Student Status

24. Dictionary Comprehension vs Normal Loop

Normal Loop

Dictionary Comprehension

25. Dictionary Comprehension vs map()

26. Combining Dictionary and List Comprehension

Important Points to Remember

Easy way to remember

SkillMantra

13. Dictionary Comprehension with `range()`

25. Dictionary Comprehension vs `map()`