Mastering Python Dictionaries: Class 11 One-Shot Complete Guide & Revision Notes

1. Introduction: Why Dictionaries are Your New Best Friend

Hello, future coders! After two decades of teaching Python, I’ve seen students struggle with “finding” data in large lists. Imagine having a school with 1,000 lockers. If you had to check every single locker starting from number 1 just to find your bag, you’d be exhausted! Instead, you use a unique Locker Key.

In Python, most collections like Strings and Lists are Sequences—they rely on numeric positions (0, 1, 2…). Dictionaries, however, are Mappings. They link a unique “Key” to a specific “Value.” This chapter is a scoring powerhouse for Class 11 exams and competitive tests like CUET because it introduces you to the logic used in real-world databases.

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2. The Anatomy of a Dictionary: Key-Value Pairs

A dictionary stores data as key:value pairs. We call one such pair an item.

Technical Term	What it Means in Simple English
Key	The unique “index” you create (like a Roll No. or Word).
Value	The data associated with that key (like a Student Name or Meaning).
Item	The complete pair of Key : Value.
Mapping	The internal link Python creates to connect a key to its value.

The Syntax Checklist:

• Curly Braces: Dictionaries are always enclosed in {}.
• Colons: A colon : separates the key from its value.
• Commas: Each item (pair) is separated by a comma ,.

Example: emp = {"empno": 1, "name": "Shahrukh", "fee": 15000}

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3. The “Golden Rules” of Keys and Values

To score well in “Error Finding” questions, you must memorize these rules. They are the most common “traps” in exams.

Feature	Dictionary Keys	Dictionary Values
Data Type	Must be Immutable (String, Number, or Tuple).	Can be Any data type (List, Dict, etc.).
Uniqueness	Must be Unique. No duplicates allowed.	Can be repeated.
Constraint	If a key is duplicated, the last value overwrites.	No constraints.

⚠️ Teacher’s Warning (The TypeError Trap): If you try to use a List as a key (e.g., {[1,2]: "Error"}), Python will throw a TypeError: unhashable type: 'list'. This is because lists can change (mutable), and keys must be set in stone.

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4. Characteristics of Dictionaries: The Big Four

1. Mappings, Not Sequences: Unlike lists, they are not accessed by numeric indices. They are indexed by keys.
2. Mutable: You can change values or add new items “in-place” without creating a new dictionary.
3. Modern Ordering: While traditionally unordered, Python 3.7+ maintains the order in which items are inserted.
4. Dictionary vs. Sequence Table:

Feature	Sequence (List/String)	Mapping (Dictionary)
Access Method	Numeric Index (0, 1, 2…)	User-defined Keys
Order	Positional	Insertion Order (3.7+)
Internal Logic	Offset-based	Hash-function based

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5. How to Create Dictionaries (Multiple Methods)

There are five ways to build a dictionary. Note the syntax differences carefully!

1. Empty Dictionary: d = {} or d = dict()
2. Standard Initialization: Student = {"roll": 1, "name": "Scott", "per": 90}
3. Using dict() Constructor: Student = dict(roll=1, name='Scott', per=89) (Note: No quotes around keys when used as keyword arguments!)
4. Using zip() to Pair Sequences: Emp = dict(zip(('empno', 'name'), (1, 'Scott')))
5. Using a List of Tuples: Emp = dict([('name', 'Victor'), ('dept', 'sales')])

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6. Accessing and Modifying Elements

• To Retrieve: Use DictionaryName[key].
  • print(emp['name']) → Output: Shahrukh
  • Exam Alert: Accessing a non-existent key results in a KeyError.
• To Add/Modify: Both use the same syntax: DictionaryName[key] = value.
  • If the key exists, the value is updated.
  • If the key is new, a new item is added to the dictionary.

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7. Membership Testing and the ‘in’ Keyword

The in and not in operators are used to check existence.

• Rule: By default, they only check Keys.
• 'name' in emp → True
• 15000 in emp → False (because 15000 is a value, not a key).
• Workaround: To search values, use if 15000 in emp.values():.

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8. Traversing a Dictionary: The ‘for’ Loop

Traversing means visiting every item.

1. Looping through Keys (Default):
2. Accessing Values:
3. Looping through Items:

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9. Removing Items: The “Delete” Toolkit

Think of these as different ways to clean out your “lockers.”

Method	Behavior	Returns
del dict[key]	Deletes the specific key-value pair.	Nothing
pop(key, msg)	Removes key and returns its value.	The Value
popitem()	Removes the last inserted item (LIFO).	(Key, Value) Tuple
clear()	Removes all items, leaving {}.	Nothing
del dict	Deletes the entire object from memory.	N/A (Error if accessed)

Pro Tip: pop() is safer than del because you can provide a default message: emp.pop('salary', 'Not Found'). This prevents a KeyError if the key is missing.

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10. Essential Built-in Functions & Methods

The Dictionary Power-User Table

Method	Description	Example	Return Type
len()	Returns number of items.	len(d)	Integer
get(k, d)	The Safe Accessor: Returns value of k. If k is missing, returns d (or None). No Exception raised!	d.get('age', 'NA')	Value / Message
keys()	Returns all keys as a sequence.	d.keys()	dict_keys view
values()	Returns all values as a sequence.	d.values()	dict_values view
items()	Returns (key, value) pairs as tuples.	d.items()	dict_items view
update()	Merges two dictionaries. Overwrites existing keys with new values.	d1.update(d2)	None
fromkeys()	Creates a new dict from a sequence of keys.	dict.fromkeys(seq, 0)	Dictionary
max() / min()	Returns highest/lowest Value (if all types match).	max(d.values())	Matching Type
sorted()	Returns a sorted list of Keys by default.	sorted(d)	List

⚠️ Student Trap (The fromkeys Sharing Error): If you use a mutable object like a list as a default value in fromkeys(), all keys will point to the same list object. If you update one, they all change! d = dict.fromkeys(['a', 'b'], []) d['a'].append(1) print(d) → {'a': [1], 'b': [1]}

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11. Advanced Concept: Nesting Dictionaries

A value inside a dictionary can itself be another dictionary.

Visitor = {
    'Scott': {'City': 'Kanpur', 'Pin': '208004'},
    'Peter': {'City': 'Delhi', 'Pin': '110001'}
}

Accessing Logic: To get Peter’s City, use double brackets: Visitor['Peter']['City'].

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12. Pretty Printing and the JSON Module

Standard print() output is messy. For large dictionaries, use the json module.

• Before: {'a': 1, 'b': 2, 'c': 3}
• After (Pretty):

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13. Logical Programming with Dictionaries

1. Word Frequency Counter (Exam Favorite!)

Using split() to break a sentence into words and count occurrences.

sentence = "Python is great and Python is fun"
words = sentence.split() # Breaks into list of words
d = {}
for w in words:
    if w not in d:
        d[w] = words.count(w)
print(d) 
# Output: {'Python': 2, 'is': 2, 'great': 1, 'and': 1, 'fun': 1}

2. Employee Management System

emp_dict = {'empname': [], 'salary': []}

# Adding data
name = input("Enter name: ")
sal = int(input("Enter salary: "))
emp_dict['empname'].append(name)
emp_dict['salary'].append(sal)

# Searching data
search = input("Enter name to search: ")
if search in emp_dict['empname']:
    pos = emp_dict['empname'].index(search)
    print("Salary is:", emp_dict['salary'][pos])
else:
    print("Record not found")

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14. Quick Revision Sheet

• Definition: Unordered collection of key:value pairs (Mappings).
• Mutability: Dictionary is Mutable; Keys must be Immutable (String/Number/Tuple).
• Duplicate Keys: Overwritten by the last value assigned.
• Key Search: in and not in check keys only.
• Key Method Trio: get() (Safe access), pop() (Remove & return), update() (Merge).

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15. Common Mistakes Students Make

• Index Error: Trying to use dict[0] when 0 isn’t a key.
• KeyError: Forgetting to check if a key exists before using del or pop.
• Mutable Key Error: Trying to use a list as a key.
• Clear vs Del: clear() empties the container; del destroys the whole container.
• Value Search: Forgeting that 'val' in d returns False even if the value exists.

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16. Previous Year Questions (PYQs)

• 1 Mark: What is the output of len({1: "A", 2: "B", 1: "C"})? (Ans: 2, duplicate key overwrites).
• 2 Marks: Differentiate between pop() and popitem(). (Ans: pop() needs a key; popitem() removes the last item and returns a tuple).
• Assertion-Reason: Assertion: d = {[1]: "A"} is valid. Reason: Keys must be immutable. (Ans: A is False, R is True).

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17. Practice MCQs

1. Which is a valid key? A) [1] B) (1, [2]) C) (1, 2) D) {1:2}.
   • Ans: C. Tuples are immutable ONLY if all elements are immutable.
2. What does dict.get('x', 5) return if ‘x’ is not present? A) Error B) None C) 5 D) 0.
   • Ans: C. It returns the default value provided.
3. How is popitem() different in Python 3.7+? A) Random removal B) LIFO removal C) FIFO removal D) Doesn’t exist.
   • Ans: B. It removes the last inserted item.

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18. Student FAQ

1. Can a dictionary have a list as a value? Yes! Values can be anything—lists, tuples, or even other dictionaries.
2. Is a dictionary a sequence? No. It is a Mapping. Sequences are ordered by position; mappings are linked by keys.
3. How do I sort a dictionary by values? You can’t sort the dictionary itself in-place, but you can use sorted(d.values()) to get a sorted list of the values.
4. Why use get() instead of []? get() prevents your program from crashing with a KeyError if the key is missing.
5. Is dict a keyword? No, it is a built-in type/constructor, but you should avoid using it as a variable name.

Keep coding and remember: in Python, the right “Key” opens every door!