DEV Community: Praneeth

Day 5: Python Lists Demystified: Tips, Tricks, and Best Practices

Praneeth — Tue, 21 Jan 2025 06:29:38 +0000

A list is a built-in data structure that represents an ordered, mutable and indexable collection of items. It is similar to the arrays in c. but in the case of lists, they can be dynamically allocated.
In python, the list is represented with with values separated by ",".

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

Dynamically Allocated: a data structure can grow (or shrink) in memory as needed.
Mutable: An object can be modified after it has been created.
Indexability: the ability of a data structure to allow access to its elements using an index.

List Assignment:

There are two main scenarios to consider: direct assignment and copying.

1. Direct Assignment

When you directly assign one list to another variable, both variables point to the same list object in memory. Changes made via one variable will affect the other.

a = [1 , 2 , 3 , 4]
b = a
b[3] = 10
print(b) #[1,2,3,10]
print(a) #[1,2,3,10]

2.Copying a List

To create an independent copy of a list, you need to explicitly copy it using methods like:

1. Using List():

list2 = list(list1)

2. Using copy():

List2 = List1.copy()

List Slicing:

List slicing is a technique used to extract a subset of elements from a list. It allows you to create a new list that includes a specific range of elements from an existing list.

syntax

List2 = List1[start : stop : step]

Example:

c = [10, 2, 3, 4]
d = c[0 : 3 : 1]
print(d) ##[10, 2, 3]

Nested Lists:

In python, a List can consist of another lists as elements

list1 = [[1,2,3,4],[5,6,7,8]]

Built-In Functions:

1. append()

Adds an element to the end of the list.

List1 = [1, 2, 3]
List1.append(4)
print(List1)  #[1, 2, 3, 4]

2. pop()

Removes and returns an element at a specific index (defaults to the last element).

my_list = [1, 2, 3]
removed_element = my_list.pop()
print(removed_element)  # 3
print(my_list)  #[1, 2]

3. remove()

Removes the first occurrence of a specific element.

my_list = [1, 2, 3, 2]
my_list.remove(2)
print(my_list)  # [1, 3, 2]

4. clear()

Removes all elements from the list, making it empty.

my_list = [1, 2, 3]
my_list.clear()
print(my_list)  # Output: []

5. index()

Returns the index of the first occurrence of a specified element.

my_list = [1, 2, 3]
print(my_list.index(2))  # Output: 1

6. count()

Returns the number of occurrences of a specified element in the list.

my_list = [1, 2, 2, 3, 2]
print(my_list.count(2))  # Output: 3

7. sort()

Sorts the elements of the list in ascending order (modifies the list in place).

my_list = [3, 1, 2]
my_list.sort()
print(my_list)  # Output: [1, 2, 3]

8. reverse()

Reverses the order of the elements in the list (modifies the list in place).

my_list = [1, 2, 3]
my_list.reverse()
print(my_list)  # Output: [3, 2, 1]

9. min() and max()

min() returns the smallest element in the list.
max() returns the largest element in the list.

my_list = [1, 2, 3, 4]
print(min(my_list))  # Output: 1
print(max(my_list))  # Output: 4

10. sum()

Returns the sum of all elements in the list (useful for lists of numbers).

my_list = [1, 2, 3, 4]
print(sum(my_list))  # Output: 10

Day 4 :Everything You Need to Know About Functions in Python

Praneeth — Wed, 08 Jan 2025 12:27:53 +0000

Definition and Defining Functions

A function represents some part of the code which can be executed only when required .In python, user defines the function by using def.

def sum(a,b):
  print(a+b)

in this example we have performed a sum of two integers a,b. so.. whenever we require to sum two numbers, we can directly use the function sum(a,b).

Calling a Function

In Python, calling a function means executing the code defined inside a function block by using the function's name followed by parentheses. Here is an example for how calling is done in python:

def sum(a,b):
  print(a+b)

sum(1,3)

here,

a and b are the parameters in the function definition.
1, 3 are the arguments passed to the function.
sum is the name of the function

Generally, there are 4 types of Arguments:

1.Required Arguments

Required arguments are the parameters that a function must receive when it is called. If you do not provide the required arguments in a function call, Python will raise a TypeError because the function will not have the necessary information to execute.

sum(a,b):
  print(a+b)
sum(1,3)

sum()

Output: 4
TypeError

Here:

a and b are required arguments because they do not have a default value.
So, when you call sum without an argument, Python raises an error.

2. Keyword Arguments

Keyword arguments allow you to pass arguments to a function by explicitly specifying the parameter names.

new_print(a,b):
   print("{a} is a friend of {b}")
new_print(b="Alice" ,a="Bob")

Output: Bob is a friend of Alice
Here:

Since the Arguments are mentioned beforehand, in spite of wrong order, this code will produce this kind of result.

3. Default Arguments

Default arguments in Python allow you to define a function with default values for certain parameters. If a value for a parameter with a default is not provided during the function call, Python automatically assigns the default value. This makes functions more flexible and reduces the need to specify every parameter explicitly.

sum(a=0, b=0):
   print(a+b)
sum()
sum(1,3)

Output: 0\n 4
In this example, even though, we have not assigned any variable to a, b since the a and b are assigned to a default value, it will give an output 0

4. Variable Length Arguments

Python provides a way to define functions that can accept a variable number of arguments. Variable-length arguments are handled using:

*args (for non-keyword arguments)
**kwargs (for keyword arguments)

1. *args:

*args allows a function to accept any number of positional arguments. Inside the function, these arguments are accessible as a tuple.

sum (*numbers):
   sum=0
   for i in numbers:
     sum+=i
   print(sum)
sum(1,2,3,4)

Output: 10

2. **Kwargs:

**kwargs allows a function to accept any number of keyword arguments (arguments passed as key=value pairs). These are stored in a dictionary.

sum(**numbers):
  sum=0
  for keys,values in numbers:
     print("{keys}={numbers}")
     sum+= values

💡Trick of the Day: 4 ways to swap the numbers

way 1:

P=5
Q=4
temp = P  
P = Q  
Q = temp

way 2:

P=5
Q=4
P,Q=Q,P

way 3:

P=5
Q=4
P = P ^ Q  
Q = P ^ Q  
P = P ^ Q

way 4:

P=5
Q=4
P = P + Q    
Q = P - Q   
P = P - Q

Day 3: Mastering the Art of Conditional Statements and Loops

Praneeth — Thu, 02 Jan 2025 16:34:31 +0000

Conditional Statements

Like other programming languages, Python also includes conditional statements. But the only difference is that instead of else if, we have elif.
Conditional statements control the flow of a program based on specific conditions. They enable decision-making by allowing the program to execute different blocks of code depending on whether a condition evaluates to True or False.
instead of explaining the if,elif and else individually let us cover them all in a single example.

if a%2==0:
   print("The Number is an Even Composite")
elif not_prime(a):
   print("The Number is an Odd Composite")
else:
   print("The Number is a Prime")

here, let the number be 3.
First the program will check whether the number is divisible by 2 (if a%2==0)
since it is not even, it goes to elif satement(if not_prime(a))
since neither the if, nor the elif are not true, the program will go to the else part and it will print:
The Number is a Prime

Key Features:

1. Logical Operators for Conditions

age=19
if age>18 and age<25:
   print("the person is an Young Adult")

2. Nested Conditional Statements

You can nest conditional statements within one another to evaluate complex conditions.

age = 20
if age >= 18:
    if age < 25:
        print("You are a young adult.")
    else:
        print("You are an adult.")
else:
    print("You are not an adult yet.")

3. Ternary Conditional Statements

bob_score=87
alen_score=92
answer=bob_score if bob_score>alen_score else alen_score
print(answer)

Answer:92

💡 Trick of the Day:

startswith() and endswith()

The startswith() and endswith() are string methods that return True if a specified string starts with or ends with a specified value.
Let’s say you want to return all the names in a list that starts with
"a."
here is how you would use startswith() to accomplish that.
Using startswith():

listl = ['lemon','Orange','apple', 'apricot']
new_list = [i for i in listl if i.startswith('a')]
pri nt(new_li st)

Answer: ['apple', 'apricot']

Using endwith():


listl = ['lemon','Orange','apple', 'apricot']
new_list = [i for i in listl if i.endswith('e')]]
pri nt(new_li st)

Answer: ['apple', 'Orange']

Loops

In Addition to decision-making statements, Python programming also supports looping statements. There are

1. while
2. for

1. For Loop:

The for loop in Python iterates over a sequence (such as a list, tuple, string, or range) and performs an operation for each item in that sequence.

a=[1,2,3,4]
for i in a:
   print(a)

Answer: 0\n 1\n 2\n 3\n 4\n

Here, the for loop iterates through all the elements in the list a and prints them.
Using range() with for:
You can use the range() function to generate a sequence of numbers.

for i in range(4):
    print(i)

Answer: 0\n 1 \n 2\n 3\n

Range():
The basic syntax of the range() function is:

range(start, end, steps)

here start=0 and step=1 by default.

for i range(1,3):
   print(i)
for j range(1,4,2):
   print(j)

Answer:1\n 2\n
1\n 3\n

While Loop:

The while loop continues to execute the block of code as long as the condition evaluates to True.

number=4
while(number!=0):
   print(number)
   number--

Answer: 4\n 3\n 2\n 1\n

1. break Statement

The break statement is used to terminate a loop prematurely, regardless of its condition. Once the break statement is executed, the control exits the loop.

for i in range(10):
    if i == 5:
        break  # Exit the loop when i equals 5
    print(i)

Answer: 10\n 9\n 8\n 7\n 6\n

2. continue Statement

The continue statement is used to skip the rest of the code in the current iteration and proceed to the next iteration of the loop.

for i in range(10):
   if i%2==0:
     continue
   print(i)

Answer: 1\n 3\n 5\n 7\n 9\n

3. pass Statement

The pass statement is a placeholder used when a block of code is syntactically required but you don't want to execute any code. It literally does nothing.

for i in range(5):
    if i == 3:
        pass  # Do nothing when i equals 3
    else:
        print(i)

Answer: 0\n 1\n 2\n 4\n

Day -2 : Mastering basics of Python

Praneeth — Tue, 03 Dec 2024 17:11:00 +0000

Operators in python

1. Arithmetic Operators

Used for basic mathematical calculations.

+ (Addition): Adds two numbers.

Example: 5 + 3 → 8
- (Subtraction): Subtracts the second number from the first.

Example: 5 - 3 → 2
* (Multiplication): Multiplies two numbers.

Example: 5 * 3 → 15
/ (Division): Divides the first number by the second, returning a float.

Example: 5 / 2 → 2.5
// (Floor Division): Divides and rounds down to the nearest integer.

Example: 5 // 2 → 2
% (Modulus): Returns the remainder of the division.

Example: 5 % 2 → 1
`(Exponentiation):** Raises the first number to the power of the second. Example:5 ** 2 → 25`

# Arithmetic Operators
a = 10
b = 3

print("Addition:", a + b)        # 13
print("Subtraction:", a - b)     # 7
print("Multiplication:", a * b)  # 30
print("Division:", a / b)        # 3.333...
print("Floor Division:", a // b) # 3
print("Modulus:", a % b)         # 1
print("Exponentiation:", a ** b) # 1000

2. Assignment Operators

Used to assign values to variables.

= (Assign): Assigns a value to a variable.

Example: x = 5
+= (Add and Assign): Adds a value and reassigns.

Example: x += 3 → x = x + 3
-= (Subtract and Assign): Subtracts a value and reassigns.

Example: x -= 3 → x = x - 3
*= (Multiply and Assign): Multiplies a value and reassigns.

Example: x *= 3 → x = x * 3
/= (Divide and Assign): Divides a value and reassigns.

Example: x /= 3 → x = x / 3
//= (Floor Divide and Assign): Floor divides and reassigns.

Example: x //= 3 → x = x // 3
%= (Modulus and Assign): Applies modulus and reassigns.

Example: x %= 3 → x = x % 3
`=(Exponentiate and Assign):** Raises to a power and reassigns. Example:x *= 2 → x = x * 2`

# Assignment Operators
x = 5
print("Initial value:", x)       # 5

x += 3
print("After += 3:", x)          # 8

x -= 2
print("After -= 2:", x)          # 6

x *= 4
print("After *= 4:", x)          # 24

x /= 3
print("After /= 3:", x)          # 8.0

x //= 2
print("After //= 2:", x)         # 4.0

x %= 3
print("After %= 3:", x)          # 1.0

x **= 2
print("After **= 2:", x)         # 1.0

3. Comparison Operators

Used to compare two values.

== (Equal to): Checks if two values are equal.

Example: 5 == 5 → True
!= (Not Equal to): Checks if two values are not equal.

Example: 5 != 3 → True
> (Greater than): Checks if the first value is greater than the second.

Example: 5 > 3 → True
< (Less than): Checks if the first value is less than the second.

Example: 3 < 5 → True
>= (Greater than or Equal to): Checks if the first value is greater than or equal to the second.

Example: 5 >= 5 → True
<= (Less than or Equal to): Checks if the first value is less than or equal to the second.

Example: 3 <= 5 → True

# Comparison Operators
a = 10
b = 5

print("Equal:", a == b)          # False
print("Not Equal:", a != b)      # True
print("Greater than:", a > b)    # True
print("Less than:", a < b)       # False
print("Greater or Equal:", a >= b) # True
print("Less or Equal:", a <= b)  # False

4. Logical Operators

Used for combining conditions.

and: Returns True if both conditions are true.

Example: (5 > 3) and (4 > 2) → True
or: Returns True if at least one condition is true.

Example: (5 > 3) or (2 > 4) → True
not: Reverses the truth value.

Example: not(5 > 3) → False

# Logical Operators
a = True
b = False

print("AND:", a and b)           # False
print("OR:", a or b)             # True
print("NOT a:", not a)           # False
print("NOT b:", not b)           # True

5. Bitwise Operators

Operate on binary representations of numbers.

&: Performs bitwise AND.

Example: 5 & 3 → 1
|: Performs bitwise OR.

Example: 5 | 3 → 7
^: Performs bitwise XOR.

Example: 5 ^ 3 → 6
~: Performs bitwise NOT.

Example: ~5 → -6
<<: Shifts bits to the left.

Example: 5 << 1 → 10
>>: Shifts bits to the right.

Example: 5 >> 1 → 2

# Bitwise Operators
a = 5  # Binary: 0101
b = 3  # Binary: 0011

print("Bitwise AND:", a & b)     # 1 (Binary: 0001)
print("Bitwise OR:", a | b)      # 7 (Binary: 0111)
print("Bitwise XOR:", a ^ b)     # 6 (Binary: 0110)
print("Bitwise NOT (~a):", ~a)   # -6
print("Left Shift:", a << 1)     # 10 (Binary: 1010)
print("Right Shift:", a >> 1)    # 2 (Binary: 0010)

6. Membership Operators

Check if a value is present in a sequence.

in: Returns True if the value is in the sequence.

Example: 'a' in 'apple' → True
not in: Returns True if the value is not in the sequence.

Example: 'z' not in 'apple' → True

# Membership Operators
sequence = [1, 2, 3, 4, 5]

print("3 in sequence:", 3 in sequence)       # True
print("6 in sequence:", 6 in sequence)       # False
print("6 not in sequence:", 6 not in sequence) # True

7. Identity Operators

Check if two variables reference the same object.

is: Returns True if two variables point to the same object.

Example: x is y
is not: Returns True if two variables point to different objects.

Example: x is not y

# Identity Operators
a = [1, 2, 3]
b = a
c = [1, 2, 3]

print("a is b:", a is b)         # True (same object)
print("a is c:", a is c)         # False (different objects)
print("a is not c:", a is not c) # True

8. Ternary Operator

The ternary operator allows a shorthand for if-else statements.

# Ternary Operator
a, b = 10, 20

max_value = a if a > b else b
print("Maximum value:", max_value)  # 20

9. Walrus Operator (:=)

Introduced in Python 3.8, the walrus operator allows assignment inside expressions.

# Walrus Operator (Python 3.8+)
data = [1, 2, 3, 4, 5]

if (n := len(data)) > 3:
    print("Length is greater than 3:", n)  # 5

10. Enumerate with Loops

The enumerate() function returns both the index and the value of each item in an iterable.

# Enumerate with Loops
colors = ["red", "green", "blue"]
for index, color in enumerate(colors):
    print(f"{index}: {color}")

Day 1: Mastering the Basics of Python

Praneeth — Sun, 01 Dec 2024 19:06:37 +0000

For the sake of simplicity, I am dividing this into 3 parts:

Variable Declaration
Taking input and Declaring Output
Operators and Expressions

1.Variable Declaration

Unlike other programming languages like c, cpp and java ,you don’t need to declare the type of a variable explicitly. This feature is called Dynamic Typing.
in C,

int a = 6;

here, the variable is declared as integer.

but in Python,

a = 6

and the variable a can be redeclared as

a = "hello world"

🖋️NOTE:
Variable names are case-sensitive., so a and A are considered different variables.

✨ Features and Rules In Declaration

Variable names must begin with an alphabet or an underscore (_)

a = 6    #valid
_a = 6   #valid
-a = 6   #invalid

The "_" variable stores the result of the last expression in interactive Python mode. This can be visible in Jupiter Notebook

a = 5
b = 6
a + b     #11
print(_)  #11

In python, multiple variables can be declared in one line

a, b, c = 5, 6, 7

Use the global keyword to modify a global variable inside a function

global x

def print():
  return x   #there will be no error

Unlike c and cpp ,Python does not have a built-in way to declare constants. By convention, variable names in all caps are treated as constants.

PI=3.14

2.Taking input and Declaring Output

print()

The print() function is a built-in Python function used to display output to the console.

a=10000
print("hello world")   #hello world
print("hello", "world")#hello world
print("hello world",a) #hello world 10000

In print function, there are two main parameters.

sep : It determines how multiple objects are separated when printed. It is usually preset to " ".
end : It defines what is printed at the end of the output.It is usually preset to "\n".

example:

print("hello world")
print("hi")    # hello world
               # hi

print("hello", "world", sep="-", end = " ")
print("hi")    # hello-world hi

for the complete documentation of print, click here

✨ Features of Print()

You can use escape sequences (e.g., \n for a newline, \t for a tab, and _\_ for a backslash) to include special characters in the printed output.

print("hello\nworld")   #hello
                        #world
print("hello\tworld")   #hello   world
print("happy\\trecking")#happy\trecking

print() is often used for debugging and tracing code execution because it provides a quick way to output variable values and program state.

x = 5
y = 10
print(f"x: {x}, y: {y}") # x: 5, y: 10

input()

The input() function in Python is used to take user input from the console.

name = input("Enter your age: ")
print("your age is: ",name)
# If the user enters 18, the output will be: "your age is 18"

🖋️NOTE:
By default, input() returns a string, so if you need to use the input as a different type (e.g., int, float), you need to convert it.

a =input("enter your age: )
type(a)                            #str
a=int(input("enter your age: ))
type(a)                            #int

In the upcoming blog, we'll delve into Python operators and conditional statements. Happy Learning😊😊😊

A New Blog Series: Python for AI Basics

Praneeth — Fri, 29 Nov 2024 18:42:22 +0000

Welcome to my new blog series, where we will explore the fascinating world of Python in the context of machine learning. Python has become a cornerstone in the field of data science and machine learning due to its simplicity and the powerful libraries it offers. Whether you're a beginner or looking to refine your skills, this series will guide you through the essentials of Python, setting a solid foundation for your machine learning journey.

Series Roadmap

Here's what you can expect over the next 15 days:

Basics of Python
Operators in Python
Conditional and Iterating Statements
Functions
Different Data Types
OOP and Classes
File Handling
Numpy
Pandas
Matplotlib
Seaborn

Each topic will be covered in detail, with examples and exercises to reinforce your learning. By the end of this series, you'll have a comprehensive understanding of Python's capabilities in machine learning. Stay tuned for the first post, and let's embark on this learning journey together!

A Beginner's Guide to Exploring Machine Learning in 2025

Praneeth — Mon, 25 Nov 2024 21:15:49 +0000

After six months of learning Machine Learning, I have encountered issues many beginners face. Here is some advice on how to tackle them effectively.

1. Learning Python

I've noticed many of my friends weren't initially good at Python but started learning Artificial Intelligence. Python is essential for Machine Learning, and without it, implementation becomes challenging. It’s a good idea to understand the basics of Python, including loops, conditional statements, functions, data types, and classes, for a better grasp. Learning these basics typically takes about 10-15 days. Libraries like NumPy, Pandas, and scikit-learn can be learned along the way.

2. Learning From Different Sources

Another common mistake people make is referring to different sites and notes during the early days of learning ML. What i recommend is to either take a paid course over the Internet or stick to a single source throughout. This helps in maintaining continuity in the subjects.

3. Lack of Practice

Mostly, the people try to rush and go through the theory completely. AI is not a small subject to be completed in a few weeks. It takes months to complete it. Also with the current emerging Gen AI, it became more necessary to stay updated. So, if you want to remember the vast amount of data, practice is the key. So, consider using 60% of your time for practice and remaining time to learn new stuff. There are many platforms like kaggle to practice and improve your skills.

4. Better Community

Creating a better community among your friends will help learn Machine Learning faster. Form study groups, share resources, and participate in hackathons. A strong community can be invaluable for discussing ideas, solving doubts, and collaborating on projects.

These activities not only boost your skills but also keep you motivated. Platforms like Discord, Reddit, and LinkedIn are great for connecting with like-minded individuals.

5. Selecting a Path after Machine Learning

Learning only Machine Learning may not be sufficient for a successful career in the current scenario. From the other extensions like computer vision, natural language processing , Gen AI, etc, specializing in one based on your interests and building your career over that will be helpful.

Artificial Intelligence is a fascinating journey that requires patience, practice, and focus. Start small, stay consistent, and don’t hesitate to reach out to the community for support. Remember, every expert was once a beginner!