Gen AI Certification Bootcamp: Build Production-Ready GenAI Applications (2nd August 2025)

 

Looking to break into generative AI (GenAI) development and build real-world AI-powered apps? The GenAI Certification Bootcamp is your all-in-one, hands-on program designed to help you master the complete GenAI stack—from prompt engineering to cloud deployment.

Whether you're a developer, data scientist, or aspiring AI engineer, this course teaches you how to build intelligent systems using the most in-demand tools in the GenAI ecosystem.

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What Is the GenAI Certification Bootcamp?

The GenAI Systems Bootcamp is a practical, project-based course that walks you through the entire lifecycle of GenAI application development. You'll learn how to integrate LLMs (like OpenAI’s GPT models) with automation tools, real-time data, and multi-agent systems to create scalable, production-grade AI apps.

Key Technologies Covered:

• LangChain – Build modular, reusable AI workflows

• LangGraph – Enable branching logic and stateful AI agents

• CrewAI – Orchestrate multiple AI agents for advanced automation

• n8n – Automate workflows and integrate APIs

• MCP (Memory, Context, Prompt) – Design intelligent memory systems

• CI/CD for GenAI – Deploy and maintain GenAI apps in production

---

Skills You’ll Gain

By the end of the bootcamp, you’ll be able to:

✅ Design powerful prompts and retrieval-augmented generation (RAG) systems
✅ Build multi-agent AI systems that collaborate to complete tasks
✅ Connect LLMs to real-world APIs and tools using automation platforms
✅ Implement vector databases and context memory
✅ Deploy applications to the cloud with continuous integration and deployment pipelines

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Who Should Join This Bootcamp?

This course is designed for:

• Developers looking to enter the GenAI space

• Data scientists wanting to move from model training to full-stack AI

• Entrepreneurs & product builders exploring AI automation

• Students & career changers interested in future-proof AI skills

No prior experience with LangChain or GenAI tools? No problem—this bootcamp starts from the ground up and gets you building fast.

---

Why This Bootcamp Stands Out

🔥 Hands-On Learning – Build actual GenAI applications, not just theory
📦 Full-Stack Coverage – From prompts and vector search to agents, workflows, and deployment
📜 Certification Included – Showcase your GenAI skills to employers or clients
💼 Portfolio-Ready Projects – Walk away with deployable AI apps that prove your expertise

---

What You’ll Build

Throughout the course, you’ll create multiple AI projects, such as:

• AI Assistant powered by LangChain & vector memory

• Automated agent teams that complete real tasks (CrewAI)

• Real-time AI tools integrated with APIs (n8n workflows)

• Fully deployed GenAI apps with CI/CD pipelines

These projects aren’t toy examples—they reflect what companies are building with GenAI today.

---

Certification That Moves Your Career Forward

Upon completing the bootcamp, you’ll receive a verified GenAI Certification—proof that you can build and deploy real GenAI systems using industry-standard tools.

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Ready to Build the Future of AI?

The GenAI Certification Bootcamp is more than just a course—it’s your launchpad into one of the most exciting fields in tech. Whether you're building your own GenAI startup or adding AI expertise to your resume, this bootcamp gives you the tools to succeed.

👉 Start building production-grade GenAI apps today
🔗 Enroll Now

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Python Coding challenge - Day 630| What is the output of the following Python Code?

 Code Explanation:

1. Importing the random Module

import random

What it does:

Imports Python’s built-in random module, which provides functions for generating random numbers.

2. Defining the rand_evens(n) Generator Function

def rand_evens(n):

What it does:

Defines a generator function named rand_evens that will yield n even random integers between 1 and 100.

3. Start of the While Loop

    while n:

What it does:

Loops as long as n is not zero (i.e., n > 0).

Purpose:

Ensures that exactly n even numbers will be generated.

4. Generating a Random Integer

        r = random.randint(1, 100)

What it does:

Generates a random integer r between 1 and 100, inclusive.

5. Checking if the Number is Even

        if r % 2 == 0:

What it does:

Checks if the random number r is even (i.e., divisible by 2).

6. Yielding the Even Number

            yield r

What it does:

If r is even, it yields (returns) the value to the caller (e.g. during iteration).

Why use yield:

Turns rand_evens into a generator that can lazily produce values on demand.

7. Decreasing the Counter

            n -= 1

What it does:

Decrements n by 1 after successfully yielding an even number.

Purpose:

Ensures exactly n even numbers are generated in total.

8. Printing the Number of Generated Even Numbers

print(len(list(rand_evens(3))))

Step-by-step:

rand_evens(3) returns a generator that will yield 3 even random numbers.

list(rand_evens(3)) converts the generator output to a list of 3 numbers.

len(...) calculates the length of the list, which will always be 3.

print(...) displays the number 3 on the console.

Expected Output

3

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Python Coding challenge - Day 629| What is the output of the following Python Code?

Code Explanation:

1. Importing cycle from itertools

from itertools import cycle

What it does:

This line imports the cycle function from Python's built-in itertools module.

Purpose of cycle:

cycle(iterable) returns an infinite iterator that repeatedly cycles through the elements of the iterable.

Example: cycle(["A", "B"]) will return "A", "B", "A", "B", "A", ... infinitely.

2. Defining the repeater Generator Function

def repeater():

    for val in cycle(["A", "B"]):

        yield val

What it does:

Defines a generator function named repeater.

Inside the function:

for val in cycle(["A", "B"]): Loops infinitely over the values "A" and "B".

yield val: Yields (returns) one value at a time each time the generator is called using next().

3. Creating the Generator Object

g = repeater()

What it does:

Calls the repeater function and stores the resulting generator object in variable g.

Effect:

This does not start execution immediately. It prepares the generator for iteration.

4. Using next() to Get Values from Generator

print([next(g) for _ in range(4)])

What it does:

Uses a list comprehension to call next(g) 4 times.

Each call to next(g) resumes execution of the generator and returns the next value in the cycle.

Output:

Since cycle(["A", "B"]) repeats "A", "B", "A", "B"..., the output will be:

['A', 'B', 'A', 'B']

Summary Output

['A', 'B', 'A', 'B']

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Python Coding Challange - Question with Answer (01250725)

 

Explanation:

✅ Step-by-step:

1. arr = [1, 2, 3]
   → Creates a list named arr.

2. arr2 = arr
   → This does not copy the list.
   → It means arr2 refers to the same list object in memory as arr.

3. arr2[0] = 100
   → Changes the first element of the list to 100.
   → Since both arr and arr2 point to the same list, this change is reflected in both.

4. print(arr)
   → Outputs the modified list.

---

✅ Output:

[100, 2, 3]

---

 Summary:

In Python, assigning one list to another variable (e.g., arr2 = arr) creates a reference, not a copy.

To make a copy, you'd need:

arr2 = arr.copy()  # or arr2 = arr[:]

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Python Coding Challange - Question with Answer (01240725)

 

Explanation:

1. Initialization:

   
   i = 5

   • A variable i is set to 5.

2. Loop Condition:

   
   while i > 0:

   • The loop runs as long as i is greater than 0.

3. Decrement i:

   i -= 1

   • In each iteration, i is reduced by 1 before any checks or printing.

4. Check for break:

   python
   if i == 2:
      break

   • If i becomes 2, the loop immediately stops (breaks).

5. Print i:

   • If i is not 2, it prints the current value of i.

---

🔄 Iteration Details:

Before Loop	i = 5
Iteration 1	i becomes 4 → i != 2 → print(4)
Iteration 2	i becomes 3 → i != 2 → print(3)
Iteration 3	i becomes 2 → i == 2 → breaks loop

✅ Output:

4
3

---

Key Concept:

• break immediately stops the loop when the condition is met.

• i -= 1 happens before the if check and print().

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Python Coding challenge - Day 627| What is the output of the following Python Code?

 Code Explanation:

1. from typing import Generator

Explanation:

This line imports the Generator type hint from Python's typing module.

It's used to annotate generator functions for better readability and static type checking.

Generator[YIELD_TYPE, SEND_TYPE, RETURN_TYPE] — here you'll specify the type of values yielded, sent, and returned.

2. def count_up(n: int) -> Generator[int, None, None]:

Explanation:

This defines a function named count_up that takes a single argument n of type int.

The return type is annotated as Generator[int, None, None], meaning:

It yields values of type int.

It does not receive values via .send().

It does not return a final value using return.

3. for i in range(1, n+1):

Explanation:

This creates a for loop that iterates over the numbers from 1 to n, inclusive.

range(1, n+1) generates numbers from 1 up to and including n.

4. yield i

Explanation:

yield makes the function a generator.

Each time count_up() is iterated (e.g., in a loop or converted to a list), it will yield the next value of i.

This pauses the function, returning i to the caller, and resumes from the same place on the next iteration.

5. print(list(count_up(3)))

Explanation:

count_up(3) creates a generator that yields 1, 2, and 3.

list(...) consumes the generator and turns the yielded values into a list: [1, 2, 3].

print(...) outputs that list to the console.

Output:

[1, 2, 3]

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Python Coding challenge - Day 628| What is the output of the following Python Code?

Code Explanation:

1. import uuid

Explanation:

This imports Python's built-in uuid module.

The uuid module allows you to generate universally unique identifiers (UUIDs).

uuid.uuid4() generates a random UUID based on random numbers.

2. def uuid_generator(n):

Explanation:

This defines a generator function named uuid_generator.

It takes a single argument n, which indicates how many UUIDs to generate.

3. for _ in range(n):

Explanation:

This loop runs n times.

The underscore _ is used here as a throwaway variable, since we don’t need the loop index.

4. yield uuid.uuid4()

Explanation:

In each loop iteration, the generator yields a new randomly-generated UUID using uuid.uuid4().

This makes uuid_generator(n) a generator that yields n UUIDs.

5. print(len(list(uuid_generator(3))))

Explanation:

uuid_generator(3) returns a generator that will yield 3 UUIDs.

list(...) consumes the generator, creating a list of 3 UUIDs.

len(...) calculates the length of that list — which is 3.

print(...) prints that number (3) to the console.

Output:

3

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Python Coding Challange - Question with Answer (01230725)

 

Step-by-Step Explanation

1. Define a global variable:

   count = 0

   • A variable count is initialized in the global scope with value 0.

2. Define a function counter:

   def counter():
       global count 
       count += 1

   • The global keyword tells Python:

     "Use the count variable from the global scope, not a new local one."

3. First function call:

   counter()

   • count += 1 → count = 0 + 1 → count = 1

4. Second function call:

   
   counter()

   • count += 1 → count = 1 + 1 → count = 2

5. Print the result:

   
   print(count)

   • This prints:

     2

---

✅ Output:

2

---

Key Concept:

• global allows a function to modify a variable defined outside the function.

• Without global, Python would assume count is local, and you'd get an UnboundLocalError.

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Python Coding challenge - Day 626| What is the output of the following Python Code?

 

Code  Explanation:

1. Import Enum

from enum import Enum

You're importing the Enum class from Python's enum module.

Enum is used to create enumerations: named constants that are unique and immutable.

2. Define Enum Class

class Color(Enum):

    RED = 1

    GREEN = 2

This defines an enumeration named Color with two members:

Color.RED with value 1

Color.GREEN with value 2

3. Generator Function

def colors():

    yield Color.RED

    yield Color.GREEN

This is a generator function named colors.

It yields two enum members: Color.RED and Color.GREEN.

4. List Comprehension with .name

print([c.name for c in colors()])

This line:

Calls the colors() generator

Iterates through each yielded value

Accesses the .name attribute of each Color enum member

Color.RED.name → "RED"

Color.GREEN.name → "GREEN"

The result is a list of strings: ['RED', 'GREEN']

Final Output

['RED', 'GREEN']

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Python Coding challenge - Day 625| What is the output of the following Python Code?

 

Code Explanation:

 1. Function Definition

def flatten(lst):

This defines a recursive generator function named flatten.

It takes one argument lst, which is expected to be a list (possibly nested).

2. For Loop: Iterating Over the List

    for item in lst:

This line iterates through each element in the list lst.

item can be an individual element (like an int or string) or another list.

3. Check for Nested List

        if isinstance(item, list):

This checks whether the current item is a list.

If it is, the function recursively calls itself to handle further flattening.

Recursive Case: Use yield from

            yield from flatten(item)

yield from is a special Python syntax that delegates iteration to another generator.

It recursively calls flatten(item) to flatten any nested sublist.

All values yielded from the recursive call are passed upward.

5. Base Case: Yield Atomic Element

        else:

            yield item

If item is not a list, it is a base value (like an integer).

The function simply yields the value, adding it to the flattened output.

6. Final Call & Output

print(list(flatten([1, [2, [3, 4], 5]])))

This calls the flatten function with a nested list.

The result is converted to a list using list(...), since flatten returns a generator.

Output:

[1, 2, 3, 4, 5]

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Python Coding challenge - Day 623| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def repeat_double(n):

Defines a generator function named repeat_double that takes a single argument n.

The generator will yield (produce) values one at a time when iterated over.

Loop Through Range

    for i in range(n):

A for loop runs from i = 0 to i = n - 1.

range(n) generates a sequence of numbers: [0, 1, 2, ..., n-1].

Yield First Value

        yield i

yield pauses the function and returns the value of i to the caller.

The function's state is saved so it can resume from here later.

Yield Double Value

        yield i * 2

After yielding i, the function resumes and yields i * 2 (i.e., double the value).

So for each iteration, it gives two values: i and i*2.

Calling the Function and Printing

print(list(repeat_double(3)))

Calls the repeat_double generator with n = 3.

This means the loop runs for i = 0, 1, and 2.

The values yielded are:

For i = 0: yields 0, 0

For i = 1: yields 1, 2

For i = 2: yields 2, 4

These values are collected into a list using list().

Final Output

[0, 0, 1, 2, 2, 4]

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Python Coding challenge - Day 624| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def safe_gen():

This defines a generator function named safe_gen.

It will yield values one at a time using the yield keyword.

Try Block Starts

    try:

Begins a try block to handle exceptions that may occur during the yield statements.

First Yield

        yield 1

The first value 1 is yielded successfully.

The generator pauses here and waits for the next iteration.

Second Yield — Division by Zero

        yield 2 / 0

When execution resumes, it tries to compute 2 / 0, which raises a ZeroDivisionError.

The error is caught by the except block, and this yield never completes.

Third Yield (Skipped)

        yield 3

This line is never executed, because control jumps to the except block once the exception is raised.

Exception Handling

    except ZeroDivisionError:

        yield "Error caught"

This catches the ZeroDivisionError from 2 / 0.

Instead of crashing, it yields the string "Error caught".

Calling and Printing

print(list(safe_gen()))

This runs the generator and collects all yielded values into a list:

First, 1 is yielded.

Then 2 / 0 causes an exception.

Instead of stopping, "Error caught" is yielded from the except block.

yield 3 is skipped due to the error.

Final Output

[1, 'Error caught']

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Python Coding Challange - Question with Answer (01220725)

 

Explanation

🔸 x = int("abc")

• This line tries to convert the string "abc" into an integer.

• But "abc" is not a valid integer, so Python raises a:

  ValueError: invalid literal for int() with base 10: 'abc'

🔸 except ValueError:

• This catches the ValueError and executes the code inside the except block.

🔸 print("fail")

• Since the error was caught, it prints:

  
  fail

---

✅ Output:

fail

---

Key Concept:

• try-except is used to handle errors gracefully.

• int("abc") fails, but the program doesn’t crash because the except block handles the error.

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Python Coding challenge - Day 622| What is the output of the following Python Code?

 

Code Explanation:

1. Define the function flatten_once(lst)

def flatten_once(lst):

This function is a generator that flattens a list only one level deep.

2. Loop over the list elements

for sub in lst:

Iterates over each item in the list lst.

For the input:

[1, [2, 3], 4]

The elements in order are:

1 (not a list)

[2, 3] (a list)

4 (not a list)

3. Check if element is a list

if isinstance(sub, list):

If the current element is a list, we want to yield its items individually.

4. Yield items based on type

If it's a list:

yield from sub

For [2, 3], this means yield 2, then 3.

If it's not a list:

yield sub

For 1 and 4, they are yielded directly.

How it Processes the Input

Given:

flatten_once([1, [2, 3], 4])

The generator yields:

1 → from yield sub

2 → from yield from [2, 3]

3 → from yield from [2, 3]

4 → from yield sub

Final Output

[1, 2, 3, 4]

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Python Coding challenge - Day 621| What is the output of the following Python Code?

 

 Code Explanation:

1. import random

Loads the random module.

This allows us to use random.randint(1, 6) to simulate a dice roll.

2. Define the generator function dice_rolls(n)

def dice_rolls(n):

    for _ in range(n):

        yield random.randint(1, 6)

This function doesn't execute until it's called.

When called, it yields n random values between 1 and 6, one at a time.

3. Execute dice_rolls(4)

list(dice_rolls(4))

Calls the generator to get 4 dice rolls.

Example output from dice_rolls(4) might be: [3, 6, 1, 5]

Note: Dice rolls are random, so the exact numbers will vary.

4. len(...) counts the rolls

len([3, 6, 1, 5])  # Example result

The length of the list is 4 because we rolled the dice 4 times.

5. print(...) prints the count

print(4)

So the final output is:

Final Output

4

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Python Coding Challange - Question with Answer (01210725)

 

Step-by-Step Explanation:

1. Define function f()

   • Inside f(), x is first assigned the value 1.

2. Define nested function g()

   • Inside g(), the statement print(x) is not executed yet, it’s just stored as part of the function definition.

3. Reassign x to 2

   • Still inside f(), x is updated to 2 before calling g().

4. Call g()

   g()

   • Now g() is executed.

   • Python follows lexical (static) scoping, so g() looks for x in the enclosing scope, which is f().

   • Since x = 2 at the time of g() execution, it prints:

---

✅ Output:

2

---

Key Concept:

• Python uses lexical scoping (also called static scoping).

• The value of x that g() sees is the one from its enclosing function f(), as it exists at the time g() is called — in this case, x = 2.

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Python Coding Challange - Question with Answer (01200725)

 

Step-by-Step Explanation

1. Initialize a variable:

   
   total = 0

   • A variable total is created and set to 0. It will be used to accumulate the sum.

2. For loop:

   
   for i in range(1, 5):
       total += i

   • range(1, 5) generates the numbers: 1, 2, 3, 4 (remember, the end is exclusive).

   • The loop adds each of these values to total.

   Here's what happens on each iteration:

   • i = 1: total = 0 + 1 = 1

   • i = 2: total = 1 + 2 = 3

   • i = 3: total = 3 + 3 = 6

   • i = 4: total = 6 + 4 = 10

3. Print the result:

   
   print(total)

   • It prints the final value of total, which is:

---

✅ Output:

10

---

Key Concept:

• range(start, end) includes the start but excludes the end.

• += is a shorthand for total = total + i.

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Python Coding challenge - Day 620| What is the output of the following Python Code?

 

Code Explanation:

1. Function Definition

def track_yields():

Defines a generator function named track_yields.

This function will yield values one at a time when iterated.

2. For Loop: Iterating Over a Range

    for i in range(3):

Loops through the values 0, 1, and 2.

3. Print Before Yielding

        print(f"Yielding {i}")

Prints a message before yielding each value.

Helps track when a value is being prepared to yield.

4. Yield Statement

        yield i

Yields the current value of i to the calling loop.

Pauses the function until the next iteration is requested.

5. Print After Loop Completion

    print("Done")

After all items from range(3) are yielded, this line is executed.

Indicates that the generator has completed.

6. For Loop Consuming the Generator

for val in track_yields():

    pass

Iterates through all values yielded by track_yields().

pass means the loop does nothing with val, but still causes the generator to run.

7. Output

Even though the loop body does nothing, track_yields() still prints messages due to print() inside the generator:

Yielding 0

Yielding 1

Yielding 2

Done

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Python Coding challenge - Day 619| What is the output of the following Python Code?

 

Code Explanation:

1. Function Definition

def walk_tree(node):

Defines a function walk_tree that takes one parameter node.

node can be an int or a nested list of ints/lists.

2. Check if Node is an Integer

    if isinstance(node, int):

Checks whether the current node is an int.

This is the base case in the recursion.

3. Yield the Integer

        yield node

If node is an integer, yield it (output it from the generator).

This means the function pauses here and returns the value to the caller.

4. Handle the List Case

    else:

If node is not an integer (i.e., it's a list), this block executes.

5. Loop Through Sub-Nodes

        for sub in node:

Iterates over each element (sub) in the list node.

Each element may itself be an int or another list.

6. Recursive Call and Yield

            yield from walk_tree(sub)

Recursively calls walk_tree on each sub.

yield from means: yield all values produced by the recursive call.

7. Define the Tree Structure

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

Creates a nested list (tree-like structure).

It contains integers and nested sublists.

8. Print the Flattened Tree

print(list(walk_tree(tree)))

Calls walk_tree(tree) to start traversing.

Wraps the generator with list() to evaluate all yields into a single list.

Prints the resulting flat list of integers:

Output: [1, 2, 3, 4, 5]

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Python Coding Challange - Question with Answer (01190725)

 

Step-by-Step Explanation:

1. Initialize a list:

   
   backpack = [0]

   • A list backpack is created with one item: [0].

2. Call the function:

   
   add_item(backpack)

   • The list backpack is passed to the function add_item.

   • Inside the function, the parameter bag refers to the same list object as backpack.

3. Inside the function:

   
   bag += [1]

   • This modifies the original list in place.

   • += on a list performs in-place addition, equivalent to bag.extend([1]).

   • So bag (and therefore backpack) becomes [0, 1].

4. Print the list:

   
   print(backpack)

   • The backpack list has been changed, so it prints:

     
     [0, 1]

---

✅ Output:

[0, 1]

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Key Concept:

• Mutable objects like lists can be modified inside functions.

• Using += on a list modifies the original list in-place.

Mathematics with Python Solving Problems and Visualizing Concepts

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Python Coding challenge - Day 618| What is the output of the following Python Code?

 

Code Explanation:

 1. Importing the heapq Module

import heapq

The heapq module provides functions for heap (priority queue) operations.

It also includes heapq.merge(), which merges multiple sorted inputs into a single sorted iterator efficiently.

2. Defining the Function: merge_sorted()

def merge_sorted():

A function named merge_sorted is defined.

It doesn’t take any arguments and returns the merged result of two sorted lists.

3. Creating Two Sorted Lists

    a = [1, 3, 5]

    b = [2, 4, 6]

List a contains sorted odd numbers.

List b contains sorted even numbers.

Both are sorted in ascending order.

 4. Merging the Sorted Lists

    return heapq.merge(a, b)

heapq.merge(a, b) merges both already sorted lists into a sorted iterator.

Unlike a + b followed by sorted(), this does not load all data into memory.

It returns a lazy iterator, which yields the next smallest element on each iteration.

5. Printing the Merged Output

print(list(merge_sorted()))

The merged iterator is converted into a list using list(...), which triggers the iteration and collects all results.

print(...) displays the final sorted merged list.

Output:

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

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Python Coding challenge - Day 617| What is the output of the following Python Code?

 

Code Explanation:

1. Importing the json Module

import json

Imports Python’s built-in json module.

This module allows you to parse JSON-formatted strings into Python objects (like lists and dictionaries).

2. JSON String as Input

data = '[{"a": 1}, {"a": 2}, {"a": 3}]'

data is a JSON-formatted string representing a list of dictionaries.

Each dictionary has a single key "a" with an integer value.

Equivalent Python object after parsing:

[{"a": 1}, {"a": 2}, {"a": 3}]

3. Defining the Generator Function

def extract_values(json_str):

This defines a function named extract_values that takes a JSON string as input.

It will yield (generate) values from the key 'a' in each dictionary.

4. Parsing and Iterating Over JSON Data

    for obj in json.loads(json_str):

json.loads(json_str) converts the JSON string into a Python list of dictionaries.

The loop iterates over each dictionary (obj) in that list.

5. Yielding Values from Key 'a'

        yield obj['a']

For each dictionary in the list, the function yields the value associated with the key 'a'.

So this yields: 1, then 2, then 3.

6. Calling the Function and Summing the Values

print(sum(extract_values(data)))

extract_values(data) returns a generator that yields 1, 2, 3.

sum(...) calculates the total sum of these values:

1 + 2 + 3 = 6

print(...) displays the result.

Output:

6

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Mathematics with Python Solving Problems and Visualizing Concepts

 

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