In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and more—covering all common scenarios like dynamic data manipulation, queues, or stacks. Below is a complete breakdown of all list methods, each with syntax, an example, and output, plus key built-in functions for comprehensive use.
📚 Adding Elements
⦁ append(x): Adds a single element to the end.
⦁ extend(iterable): Adds all elements from an iterable to the end.
⦁ insert(i, x): Inserts x at index i (shifts elements right).
📚 Removing Elements
⦁ remove(x): Removes the first occurrence of x (raises ValueError if not found).
⦁ pop(i=-1): Removes and returns the element at index i (default: last).
⦁ clear(): Removes all elements.
📚 Searching and Counting
⦁ count(x): Returns the number of occurrences of x.
⦁ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
📚 Ordering and Copying
⦁ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
⦁ reverse(): Reverses the elements in place.
⦁ copy(): Returns a shallow copy of the list.
📚 Built-in Functions for Lists (Common Cases)
⦁ len(lst): Returns the number of elements.
⦁ min(lst): Returns the smallest element (raises ValueError if empty).
⦁ max(lst): Returns the largest element.
⦁ sum(lst[, start=0]): Sums the elements (start adds an offset).
⦁ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
#python #lists #datastructures #methods #examples #programming
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📚 Adding Elements
⦁ append(x): Adds a single element to the end.
lst = [1, 2]
lst.append(3)
print(lst) # Output: [1, 2, 3]
⦁ extend(iterable): Adds all elements from an iterable to the end.
lst = [1, 2]
lst.extend([3, 4])
print(lst) # Output: [1, 2, 3, 4]
⦁ insert(i, x): Inserts x at index i (shifts elements right).
lst = [1, 3]
lst.insert(1, 2)
print(lst) # Output: [1, 2, 3]
📚 Removing Elements
⦁ remove(x): Removes the first occurrence of x (raises ValueError if not found).
lst = [1, 2, 2]
lst.remove(2)
print(lst) # Output: [1, 2]
⦁ pop(i=-1): Removes and returns the element at index i (default: last).
lst = [1, 2, 3]
item = lst.pop(1)
print(item, lst) # Output: 2 [1, 3]
⦁ clear(): Removes all elements.
lst = [1, 2, 3]
lst.clear()
print(lst) # Output: []
📚 Searching and Counting
⦁ count(x): Returns the number of occurrences of x.
lst = [1, 2, 2, 3]
print(lst.count(2)) # Output: 2
⦁ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
lst = [1, 2, 3, 2]
print(lst.index(2)) # Output: 1
📚 Ordering and Copying
⦁ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
lst = [3, 1, 2]
lst.sort()
print(lst) # Output: [1, 2, 3]
⦁ reverse(): Reverses the elements in place.
lst = [1, 2, 3]
lst.reverse()
print(lst) # Output: [3, 2, 1]
⦁ copy(): Returns a shallow copy of the list.
lst = [1, 2]
new_lst = lst.copy()
print(new_lst) # Output: [1, 2]
📚 Built-in Functions for Lists (Common Cases)
⦁ len(lst): Returns the number of elements.
lst = [1, 2, 3]
print(len(lst)) # Output: 3
⦁ min(lst): Returns the smallest element (raises ValueError if empty).
lst = [3, 1, 2]
print(min(lst)) # Output: 1
⦁ max(lst): Returns the largest element.
lst = [3, 1, 2]
print(max(lst)) # Output: 3
⦁ sum(lst[, start=0]): Sums the elements (start adds an offset).
lst = [1, 2, 3]
print(sum(lst)) # Output: 6
⦁ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
lst = [3, 1, 2]
print(sorted(lst)) # Output: [1, 2, 3]
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
lst[start:end:step] for advanced extraction, like lst[1:3] outputs ``.#python #lists #datastructures #methods #examples #programming
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💡 {{Python Exam}}
Python dictionaries are a fundamental data structure used to store data as key-value pairs. They are mutable (can be changed), dynamic, and since Python 3.7, they maintain the order of insertion. Keys must be unique and of an immutable type (like strings or numbers), while values can be of any type.
1. Creating and Accessing Dictionaries
• A dictionary is created using curly braces
•
•
•
2. Modifying a Dictionary
• A new key-value pair is added using simple assignment
• The value of an existing key is updated by assigning a new value to it.
• The
3. Looping Through Dictionaries
•
•
•
#Python #DataStructures #Dictionaries #Programming #PythonBasics
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By: @CodeProgrammer ✨
Python dictionaries are a fundamental data structure used to store data as key-value pairs. They are mutable (can be changed), dynamic, and since Python 3.7, they maintain the order of insertion. Keys must be unique and of an immutable type (like strings or numbers), while values can be of any type.
1. Creating and Accessing Dictionaries
# Creating a dictionary
student = {
"name": "Alex",
"age": 21,
"courses": ["Math", "CompSci"]
}
# Accessing values
print(f"Name: {student['name']}")
print(f"Age: {student.get('age')}")
# Safe access for a non-existent key
print(f"Major: {student.get('major', 'Not specified')}")
# --- Sample Output ---
# Name: Alex
# Age: 21
# Major: Not specified
• A dictionary is created using curly braces
{} with key: value pairs.•
student['name'] accesses the value using its key. This will raise a KeyError if the key doesn't exist.•
student.get('age') is a safer way to access a value, returning None if the key is not found.•
.get() can also take a second argument as a default value to return if the key is missing.2. Modifying a Dictionary
user_profile = {
"username": "coder_01",
"level": 5
}
# Add a new key-value pair
user_profile["email"] = "[email protected]"
print(f"After adding: {user_profile}")
# Update an existing value
user_profile["level"] = 6
print(f"After updating: {user_profile}")
# Remove a key-value pair
del user_profile["email"]
print(f"After deleting: {user_profile}")
# --- Sample Output ---
# After adding: {'username': 'coder_01', 'level': 5, 'email': '[email protected]'}
# After updating: {'username': 'coder_01', 'level': 6, 'email': '[email protected]'}
# After deleting: {'username': 'coder_01', 'level': 6}• A new key-value pair is added using simple assignment
dict[new_key] = new_value.• The value of an existing key is updated by assigning a new value to it.
• The
del keyword completely removes a key-value pair from the dictionary.3. Looping Through Dictionaries
inventory = {
"apples": 430,
"bananas": 312,
"oranges": 525
}
# Loop through keys
print("--- Keys ---")
for item in inventory.keys():
print(item)
# Loop through values
print("\n--- Values ---")
for quantity in inventory.values():
print(quantity)
# Loop through key-value pairs
print("\n--- Items ---")
for item, quantity in inventory.items():
print(f"{item}: {quantity}")
# --- Sample Output ---
# --- Keys ---
# apples
# bananas
# oranges
#
# --- Values ---
# 430
# 312
# 525
#
# --- Items ---
# apples: 430
# bananas: 312
# oranges: 525•
.keys() returns a view object of all keys, which can be looped over.•
.values() returns a view object of all values.•
.items() returns a view object of key-value tuple pairs, allowing you to easily access both in each loop iteration.#Python #DataStructures #Dictionaries #Programming #PythonBasics
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By: @CodeProgrammer ✨
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