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python programming control structures.pptx
Problem Solving Using
Python
Unit II
By
K.VIMALA
M.Sc(IT).,M.Phil.,MBA(Sys).,B.Ed.,
Mastering Python
Control Structures
Dive into the world of Python's powerful control structures,
where logical decisions and iterative processes come to life.
Unlock the full potential of your programs with these essential
programming concepts.
Boolean Expressions
1 Logical Operators
Use and, or, and not to combine conditions and create
complex expressions.
2 Comparison Operators
Employ ==, !=, >, <, >=, and <= to evaluate relationships
between values.
3 Truthiness
Understand how Python interprets values as True or
False based on their inherent characteristics.
Selection Control
1
If Statement
Make decisions based on conditions using the if
statement.
2
Elif
Add more conditions to the decision-making
process with elif.
3
Else
Provide a default action when none of the
previous conditions are met using else.
Indentation in Python
Code Blocks
Python uses indentation to define code blocks, unlike other
languages that rely on curly braces or keywords.
Consistent Spacing
Maintain a consistent indentation style, usually 4 spaces, to
ensure your code is readable and maintainable.
Nested Structures
Properly indent nested statements, such as if blocks within
while loops, to preserve the logical structure of your code.
Multi-Way Selection
If-Elif-Else
Use this structure when you
have multiple mutually
exclusive conditions to evaluate.
Nested If Statements
Combine if statements to create
more complex decision-making
logic.
Match-Case
In Python 3.10 and later, the
match-case statement provides
a concise way to handle
multiple conditions.
Iterative Control
1 While Loop
Repeat a block of code as long as a certain condition
is true.
2 Break and Continue
Use break to exit a loop and continue to skip the
current iteration.
3 Infinite Loops
Accidentally creating a loop that never ends can be a
common pitfall, so be careful with your conditions.
Definite vs. Indefinite Loops
Definite Loops
Use for loops when you know
the exact number of iterations
or have a sequence to iterate
over.
Indefinite Loops
Use while loops when the
number of iterations is
unknown or dependent on a
certain condition.
Choosing the Right Loop
Select the loop type that best
fits the problem you're trying to
solve for more efficient and
readable code.
Boolean Flags and Indefinite
Loops
Boolean Flags
Use boolean variables as flags to track the state of your program
and control the execution of indefinite loops.
Sentinel Values
Another approach is to use a sentinel value, such as a specific
input, to signal the end of the loop.
Combining Techniques
Combine boolean flags and sentinel values to create more
complex and robust control structures for your programs.
Nested Loops
Outer Loop
The outer loop controls the
overall number of iterations and
nesting.
Inner Loop
The inner loop is executed for
each iteration of the outer loop.
Matrix Manipulation
Nested loops are often used to
work with multidimensional data
structures, such as matrices.
Optimization Techniques
Technique Description
Avoid Unnecessary Loops Look for ways to combine or eliminate loops to
improve performance.
Use Generator Expressions Leverage generator expressions for more efficient
memory usage.
Parallelize Loops Utilize tools like concurrent.futures to parallelize loop
execution.
Mastering Python Lists
Python lists are powerful data structures that allow you to store
and manipulate collections of items. Dive into the world of lists
and unlock their full potential for your programming projects.
Constructing a List
To create a list in Python, we can assign a sequence of elements to a variable, such as:
my_list = [1, 2, 3, 'four', 5.6]
Accessing List Elements
We can retrieve individual elements from the list using index notation. The first element has
an index of 0, and we can also use negative indices to access elements from the end of the
list:
print(my_list[0]) # Output: 1
print(my_list[-1]) # Output: 5.6
Modifying a List
Python's built-in list methods allow us to easily add, insert, and remove elements from the list:
my_list.append(7)
my_list.insert(2, 'new')
del my_list[1]
List Operations
Concatenation
You can combine lists using the
+ operator to create a new list
containing all the elements.
Repetition
The * operator allows you to
repeat a list a specified number
of times.
Membership
The in and not in operators
check if an element is present in
a list.
Looping through Lists
1 For Loop
Iterate over a list using a for loop to perform
actions on each element.
2 Enumerate()
Simultaneously access both the index and value of
each item in the list.
3 While Loop
Use a while loop to iterate over a list until a
specific condition is met.
List Comprehensions
Concise Syntax
List comprehensions provide a compact way to create new
lists based on existing ones.
Conditional Logic
You can add conditional statements to filter and transform
elements in the list.
Nested Loops
List comprehensions can even handle nested loops, making
them a powerful tool.
Common List Methods
Append()
Add an item to the end
of the list.
Insert()
Insert an item at a
specific index.
Remove()
Remove the first
occurrence of an item.
Sort()
Arrange the items in the
list in ascending order.
Nested Lists
2D Structures
Lists can contain other lists, creating a multi-
dimensional data structure.
Accessing Elements
Use double indexing to access specific elements
within the nested lists.
Iterating Nested Lists
Nested loops allow you to traverse and manipulate
the elements in a nested list.
List Slicing
Extracting Subsets
Slice a list to create a new list
containing a subset of the
original elements.
Specifying Ranges
Use start, stop, and step
parameters to control the
elements included in the slice.
Negative Indexing
Slice a list using negative indices
to access elements from the end
of the list.
List vs. Other Data Structures
Lists Tuples Sets Dictionaries
Ordered Ordered Unordered Key-Value
Pairs
Mutable Immutable Mutable Mutable
Duplicates
Allowed
Duplicates
Allowed
Duplicates
Not
Allowed
Keys
Unique,
Values
Can
Repeat
Mastering Python Lists
Python lists are a fundamental data structure that unlock a world of possibilities in your programming. By
understanding their features and mastering their use, you can write more efficient, flexible, and powerful
code.

python programming control structures.pptx

  • 1.
    Problem Solving Using Python UnitII By K.VIMALA M.Sc(IT).,M.Phil.,MBA(Sys).,B.Ed.,
  • 2.
    Mastering Python Control Structures Diveinto the world of Python's powerful control structures, where logical decisions and iterative processes come to life. Unlock the full potential of your programs with these essential programming concepts.
  • 3.
    Boolean Expressions 1 LogicalOperators Use and, or, and not to combine conditions and create complex expressions. 2 Comparison Operators Employ ==, !=, >, <, >=, and <= to evaluate relationships between values. 3 Truthiness Understand how Python interprets values as True or False based on their inherent characteristics.
  • 4.
    Selection Control 1 If Statement Makedecisions based on conditions using the if statement. 2 Elif Add more conditions to the decision-making process with elif. 3 Else Provide a default action when none of the previous conditions are met using else.
  • 5.
    Indentation in Python CodeBlocks Python uses indentation to define code blocks, unlike other languages that rely on curly braces or keywords. Consistent Spacing Maintain a consistent indentation style, usually 4 spaces, to ensure your code is readable and maintainable. Nested Structures Properly indent nested statements, such as if blocks within while loops, to preserve the logical structure of your code.
  • 6.
    Multi-Way Selection If-Elif-Else Use thisstructure when you have multiple mutually exclusive conditions to evaluate. Nested If Statements Combine if statements to create more complex decision-making logic. Match-Case In Python 3.10 and later, the match-case statement provides a concise way to handle multiple conditions.
  • 7.
    Iterative Control 1 WhileLoop Repeat a block of code as long as a certain condition is true. 2 Break and Continue Use break to exit a loop and continue to skip the current iteration. 3 Infinite Loops Accidentally creating a loop that never ends can be a common pitfall, so be careful with your conditions.
  • 8.
    Definite vs. IndefiniteLoops Definite Loops Use for loops when you know the exact number of iterations or have a sequence to iterate over. Indefinite Loops Use while loops when the number of iterations is unknown or dependent on a certain condition. Choosing the Right Loop Select the loop type that best fits the problem you're trying to solve for more efficient and readable code.
  • 9.
    Boolean Flags andIndefinite Loops Boolean Flags Use boolean variables as flags to track the state of your program and control the execution of indefinite loops. Sentinel Values Another approach is to use a sentinel value, such as a specific input, to signal the end of the loop. Combining Techniques Combine boolean flags and sentinel values to create more complex and robust control structures for your programs.
  • 10.
    Nested Loops Outer Loop Theouter loop controls the overall number of iterations and nesting. Inner Loop The inner loop is executed for each iteration of the outer loop. Matrix Manipulation Nested loops are often used to work with multidimensional data structures, such as matrices.
  • 11.
    Optimization Techniques Technique Description AvoidUnnecessary Loops Look for ways to combine or eliminate loops to improve performance. Use Generator Expressions Leverage generator expressions for more efficient memory usage. Parallelize Loops Utilize tools like concurrent.futures to parallelize loop execution.
  • 12.
    Mastering Python Lists Pythonlists are powerful data structures that allow you to store and manipulate collections of items. Dive into the world of lists and unlock their full potential for your programming projects.
  • 13.
    Constructing a List Tocreate a list in Python, we can assign a sequence of elements to a variable, such as: my_list = [1, 2, 3, 'four', 5.6] Accessing List Elements We can retrieve individual elements from the list using index notation. The first element has an index of 0, and we can also use negative indices to access elements from the end of the list: print(my_list[0]) # Output: 1 print(my_list[-1]) # Output: 5.6 Modifying a List Python's built-in list methods allow us to easily add, insert, and remove elements from the list: my_list.append(7) my_list.insert(2, 'new') del my_list[1]
  • 14.
    List Operations Concatenation You cancombine lists using the + operator to create a new list containing all the elements. Repetition The * operator allows you to repeat a list a specified number of times. Membership The in and not in operators check if an element is present in a list.
  • 15.
    Looping through Lists 1For Loop Iterate over a list using a for loop to perform actions on each element. 2 Enumerate() Simultaneously access both the index and value of each item in the list. 3 While Loop Use a while loop to iterate over a list until a specific condition is met.
  • 16.
    List Comprehensions Concise Syntax Listcomprehensions provide a compact way to create new lists based on existing ones. Conditional Logic You can add conditional statements to filter and transform elements in the list. Nested Loops List comprehensions can even handle nested loops, making them a powerful tool.
  • 17.
    Common List Methods Append() Addan item to the end of the list. Insert() Insert an item at a specific index. Remove() Remove the first occurrence of an item. Sort() Arrange the items in the list in ascending order.
  • 18.
    Nested Lists 2D Structures Listscan contain other lists, creating a multi- dimensional data structure. Accessing Elements Use double indexing to access specific elements within the nested lists. Iterating Nested Lists Nested loops allow you to traverse and manipulate the elements in a nested list.
  • 19.
    List Slicing Extracting Subsets Slicea list to create a new list containing a subset of the original elements. Specifying Ranges Use start, stop, and step parameters to control the elements included in the slice. Negative Indexing Slice a list using negative indices to access elements from the end of the list.
  • 20.
    List vs. OtherData Structures Lists Tuples Sets Dictionaries Ordered Ordered Unordered Key-Value Pairs Mutable Immutable Mutable Mutable Duplicates Allowed Duplicates Allowed Duplicates Not Allowed Keys Unique, Values Can Repeat
  • 21.
    Mastering Python Lists Pythonlists are a fundamental data structure that unlock a world of possibilities in your programming. By understanding their features and mastering their use, you can write more efficient, flexible, and powerful code.