python coding test course, understanding friendships

Problem Description

You need to develop a program that understands user relationships in a social network.
The program must provide answers to the following two requests based on the friendships of the given users.

  • Print the friend list of the given user A.
  • Check whether the given users A and B are friends.

Input Format

The first line contains the number of users N and the number of friendships M. (1 ≤ N ≤ 100,000, 0 ≤ M ≤ 1,000,000)

The next M lines each contain two integers A and B, meaning A is a friend of B.

Output Format

Print the friend list of user A in ascending order on the first line.
On the second line, print ‘YES’ or ‘NO’ regarding whether A and B are friends.

Example

        Input Example:
        5 4
        1 2
        1 3
        2 4
        3 5
        
        1
        2

        Output Example:
        2 3
        NO
    

Approach

To solve this problem, you need to efficiently store and query friendship relationships.
You can utilize graph theory by using an adjacency list or set data structure.

First, initialize an empty list to store information about users and friendship relationships.
Then, receive each friendship relationship as input and store the information.
Sort the friend list for output and also provide a response to the friendship verification request.

Source Code

        def manage_friendship(n, m, friendships, a, b):
            friends = {i: set() for i in range(1, n + 1)}
            
            for x, y in friendships:
                friends[x].add(y)
                friends[y].add(x)
            
            friend_list = sorted(friends[a])
            is_friend = "YES" if b in friends[a] else "NO"
            
            return friend_list, is_friend
        
        # Input processing
        n, m = map(int, input().split())
        friendships = [tuple(map(int, input().split())) for _ in range(m)]
        a = int(input())
        b = int(input())

        # Manage friendships
        friend_list, is_friend = manage_friendship(n, m, friendships, a, b)

        # Output results
        print(" ".join(map(str, friend_list)))
        print(is_friend)
    

Conclusion

This algorithm problem requires a basic understanding of data structures and algorithms to efficiently manage and
query friendship relationships.
In real software development, handling such data relationships often appears in platforms such as custom applications or social networks,
so learning how to solve these problems is very useful.

Appendix

Many graph problems, such as friendship relationships, can evolve into more advanced problems through searching algorithms like DFS (Depth First Search) or BFS (Breadth First Search).
Based on this problem, I hope you challenge those advanced problems as well.

Python coding test course, finding the longest common subsequence

Problem Description

Given two strings, this is a problem of finding the Longest Common Subsequence (LCS) between them. A common subsequence refers to characters that appear in both strings while maintaining their order. The goal is to find the maximum length of such a subsequence.

Example

Input

String A: ABCBDAB
String B: BDCAB

Output

Longest Common Subsequence: BDAB

Approach to the Problem

To solve the LCS problem, we can use Dynamic Programming. Dynamic Programming is a technique that solves problems by storing already computed results and reusing them. To find the LCS, we store the length of the subsequence based on the comparison results of each character in the two strings.

Finding LCS using Dynamic Programming

Step 1: Initialize the DP Table

Let the lengths of the two strings be m and n, we create and initialize a 2D DP array of size (m + 1) x (n + 1). Each element of the DP array stores the length of the common subsequence found in the two strings.

Step 2: Fill the DP Table

We fill in the DP table while comparing each character of the two strings. If the characters match, the LCS length including that character is DP[i-1][j-1] + 1. If they do not match, we use DP[i][j] = max(DP[i-1][j], DP[i][j-1]) to record the length of the LCS found so far.

Step 3: Extract LCS Length and String

After filling the DP table, check DP[m][n] to get the length of the longest common subsequence, and extract the actual common subsequence using this length. The extraction process is carried out by tracing back through the table to find common characters.

Algorithm Code

    
def lcs(A, B):
    # Step 1: Initialize the DP array
    m = len(A)
    n = len(B)
    dp = [[0] * (n + 1) for _ in range(m + 1)]

    # Step 2: Fill the DP array
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if A[i - 1] == B[j - 1]:
                dp[i][j] = dp[i - 1][j - 1] + 1
            else:
                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])

    # Step 3: Get LCS length
    lcs_length = dp[m][n]

    # Extract LCS string
    lcs_str = []
    i, j = m, n
    while i > 0 and j > 0:
        if A[i - 1] == B[j - 1]:
            lcs_str.append(A[i - 1])
            i -= 1
            j -= 1
        elif dp[i - 1][j] >= dp[i][j - 1]:
            i -= 1
        else:
            j -= 1

    lcs_str.reverse()  # Reverse the extracted string to restore original order
    return lcs_length, ''.join(lcs_str)

# Test code
A = "ABCBDAB"
B = "BDCAB"
length, lcs_string = lcs(A, B)
print("LCS length:", length)
print("LCS string:", lcs_string)
    
    

Complexity Analysis

The time complexity of this algorithm is O(m * n). Since each character needs to be compared, the result is a product of the lengths of the two strings. The space complexity is also O(m * n), determined by the size of the created DP table. However, there are ways to reduce the size of the DP table. For example, since only the previous row’s data is needed, it can be implemented using two 1D arrays.

Optimized Code Example

    
def optimized_lcs(A, B):
    m = len(A)
    n = len(B)
    dp = [0] * (n + 1)

    for i in range(1, m + 1):
        current = 0  # Value at the current position
        for j in range(1, n + 1):
            temp = dp[j]
            if A[i - 1] == B[j - 1]:
                dp[j] = current + 1
            else:
                dp[j] = max(dp[j], dp[j - 1])
            current = temp  # Store the value from the previous row

    return dp[n]

# Optimized test
length = optimized_lcs(A, B)
print("LCS length:", length)
    
    

Conclusion

In this lecture, we dealt with the problem of finding the Longest Common Subsequence (LCS). This problem is a great example of how to easily utilize string processing and dynamic programming concepts. The LCS problem is widely used in various fields, especially in gene analysis and finding identical sequences, so understanding and implementing the algorithm is very useful for coding tests and practical applications.

References

Python Coding Test Course, Finding the Parenthesis Arrangement to Create the Minimum Value

In this lecture, we will solve an algorithm problem titled “Finding the Parenthesis Arrangement that Produces the Minimum Value.”
The goal of this problem is to find a way to place parentheses appropriately around given numbers and operators to minimize the calculation result.

Problem Definition

From the given string of numbers and operators, find the minimum value by considering all possible arrangements using parentheses. For example, given input such as “3+5-2+6*3”,
we need to find the correct positions for the parentheses to minimize the calculation result.

Example Problem

        Input: "3+5-2+6*3"
        Output: 9 (e.g., (3+5-2)+(6*3) => 9 is the minimum value)
    

Problem Analysis

This problem has the property that the order of operations changes depending on the placement of parentheses.
Thus, it can be solved using dynamic programming techniques.
Several steps can be considered to solve the problem.

Step 1: Understanding the Input Format

As seen in the problem, parentheses can only be inserted after operators.
Therefore, it’s necessary to separate the input into numbers and operators.

Step 2: Finding Possible Combinations of Parentheses Placement

We need to find all possible combinations from the given numbers and operators.
This can be resolved through a recursive method, and for each combination,
we compare the outcomes and store the minimum value.

Step 3: Implementing the Calculation Function

We need to implement a function that actually performs the calculations for each combination.
Care must be taken to ensure that different operations are performed depending on the operator.

Code Implementation

The following code is the final implementation example for finding the optimal parenthesis arrangement that produces the minimum value.

        
def calculate(expression):
    return eval(expression)

def min_parentheses(arr, ops):
    min_value = float('inf')
    
    def find_min(l, r):
        nonlocal min_value
        if l == r:
            return arr[l]
        
        for i in range(l, r):
            left = find_min(l, i)
            right = find_min(i + 1, r)
            expr = f"({left}{ops[i]}{right})"
            min_value = min(min_value, calculate(expr))
        
        return min_value
    
    find_min(0, len(arr) - 1)
    return min_value

def min_parentheses_solution(s):
    arr = list(map(int, s.replace('+', ' ').replace('-', ' ').replace('*', ' ').split()))
    ops = [char for char in s if not char.isdigit()]
    return min_parentheses(arr, ops)

# Example execution
print(min_parentheses_solution("3+5-2+6*3"))
        
    

Code Explanation

Let’s take a look at the functions used in the code one by one.

1. calculate Function

Evaluates the given expression string and returns the result.
It uses the eval function to compute the string as a formula.
However, it is generally advisable to avoid using eval,
and it can be modified later to implement mathematical operations safely.

2. min_parentheses Function

A function that implements the dynamic programming part, recursively dividing the passed array
to calculate the minimum value.
It performs all possible operations for each interval to update the minimum result.

3. min_parentheses_solution Function

This function separates the input string into numbers and operators,
and then calls the min_parentheses function to find the minimum value.

Result Verification

Through the code above, we can confirm that the minimum value for “3+5-2+6*3” is 9.
This example illustrates the basic structure of the algorithm, and it is a good problem to practice with custom data structures or syntax.

Conclusion

In this lecture, we learned how to tackle various cases of parenthesis arrangements to solve the problem.
Such problems frequently appear in coding tests, so it is important to establish your own algorithm and deepen your understanding of it.
Furthermore, it is recommended to experiment with different approaches to solving the problem.

Algorithm Reflection

Finally, the problem of arranging parentheses to create a minimum value requires exploring all possible cases,
making it effective to combine a brute force algorithm with dynamic programming.
Overcoming the challenges that arise during the problem-solving process greatly aids in developing algorithmic thinking skills.

Additional Practice Problems

Solve similar problems to apply the algorithm.
Example: Find the minimum value for input such as “1+2*3-4/2+5*6”.

python coding test course, finding minimum value 2

Hello, everyone preparing for coding tests! Today, we will conduct the second lecture on ‘Finding Minimum Value’. In this lecture, we will address an algorithm problem that requires finding the minimum value in a given array that satisfies a specific condition. I will explain the process of solving this algorithm problem step by step, so please read carefully.

Problem Description

Given an integer array nums and an integer target, write a function that returns the minimum value among numbers in the array that are less than target. If no such number exists, it should return -1.

Example Input and Output

  • Input: nums = [1, 3, 5, 7, 9], target = 6
  • Output: 5
  • Input: nums = [1, 2, 3], target = 1
  • Output: -1

Approach to the Problem

To solve this problem, you need to follow these steps:

  1. Traverse the array to find numbers less than target.
  2. Store the minimum value among the found numbers.
  3. If there are no numbers that satisfy the condition, return -1; otherwise, return the minimum value.

Algorithm Analysis

The time complexity to solve the above problem is O(n) because the array is traversed only once. The space complexity is O(1) as no additional space is required.

Code Implementation

Now, let’s write Python code based on the above algorithm.

def find_min_less_than_target(nums, target):
    min_value = float('inf')  # Initialize to infinity
    found = False  # Variable to check if a number satisfying the condition has been found

    for num in nums:
        if num < target:
            found = True  # Found a number less than target
            min_value = min(min_value, num)  # Update minimum value

    return min_value if found else -1  # Return value based on condition satisfaction

Test Cases

After writing the code, you should check if it works correctly with a few test cases.

assert find_min_less_than_target([1, 3, 5, 7, 9], 6) == 5
assert find_min_less_than_target([1, 2, 3], 1) == -1
assert find_min_less_than_target([10, 15, 20, 25, 30], 20) == 15
assert find_min_less_than_target([1, 2, 3, 0, -1], 2) == 1
assert find_min_less_than_target([], 5) == -1

Conclusion

In this lecture, we solved the problem of finding the minimum value in an array that satisfies a specific condition. Through the implementation of the algorithm and performance analysis, we learned how to clearly understand and solve the problem. I hope you continue to challenge various algorithm problems to build your skills. We will return with another topic next time. Thank you!

Python Coding Test Course, Finding Minimum Value 1

Coding tests are considered an important stage in the recruitment process of many companies these days. Today, we will explore one of the algorithm problems called “Finding the Minimum Value.”
This problem may seem simple as it involves finding the minimum value in an array, but it can actually be very useful when utilizing various variations and optimization techniques.
Through this lecture, we will take a detailed look at the theoretical background and how to implement the code.

Problem Description

Given an integer array arr, write a function that finds and returns the minimum value in this array.
The size of the array is 1 ≤ len(arr) ≤ 10^6, and each element of the array is an integer in the range -10^9 ≤ arr[i] ≤ 10^9.

Input

arr = [3, 1, 4, 1, 5, 9, 2, 6, 5]

Output

1

Problem-Solving Process

1. Understanding the Problem

The given problem is to find and return the minimum value in an array. Since the number of elements in the array can go up to one million,
an efficient algorithm is needed. There can be multiple approaches to find the minimum value, but let’s start with the most basic method.

2. Algorithm Design

The simplest way to find the minimum value is to iterate through the array and compare each element with the current minimum value.
This method has a time complexity of O(n), where n is the number of elements in the array.
The advantage of this method is that it is very simple and intuitive to implement.
However, since there are diverse ways to find the minimum value, other approaches can also be considered.

3. Code Implementation

Now let’s implement the algorithm in Python code.

def find_min(arr):
    # Exception handling for an empty array
    if not arr:
        return None

    # Initialize the minimum value with the first element
    min_value = arr[0]

    # Iterate through the array to find the minimum value
    for num in arr:
        if num < min_value:
            min_value = num

    return min_value

# Example usage
arr = [3, 1, 4, 1, 5, 9, 2, 6, 5]
result = find_min(arr)
print(f"The minimum value is: {result}")

4. Code Explanation

In the above code, the find_min function takes an array arr as input and finds the minimum value.
It first handles the case where the array is empty by returning None.
Next, it initializes the minimum value with the first element of the array and then iterates through all the elements of the array, comparing them with the current minimum value.
If the current element is smaller than the minimum value, it updates the minimum value.
Finally, it returns the minimum value.

5. Time Complexity Analysis

The time complexity of this algorithm is O(n).
This complexity arises because it requires iterating through all the elements of the array once.
In an array where at least n elements exist, it is necessary to check all elements to find the minimum value, so there’s no method with better time complexity than this.

Other Methods to Find the Minimum Value in a List

1. Using Built-in Functions

In Python, you can simply use the built-in function min() to find the minimum value.
In this case, the time complexity remains O(n).

result = min(arr)
print(f"The minimum value is: {result}")

2. Recursive Method

There is also a method to find the minimum value using recursion. This method makes the code more complex but maintains the same time complexity. Below is a simple recursive approach.

def find_min_recursive(arr, low, high):
    # If it's one element in the array
    if low == high:
        return arr[low]

    # Calculate the middle index of the array
    mid = (low + high) // 2

    # Find the minimum value in the left and right halves
    left_min = find_min_recursive(arr, low, mid)
    right_min = find_min_recursive(arr, mid + 1, high)

    return min(left_min, right_min)

# Finding the minimum value using recursion
result = find_min_recursive(arr, 0, len(arr) - 1)
print(f"The minimum value is: {result}")

3. Sorting and Using the First Element

Another way to find the minimum value is to sort the array first. This method has a time complexity of O(n log n),
which is therefore inefficient compared to the usual methods for finding the minimum value. However, it can be useful if associated with other tasks that require sorting.

sorted_arr = sorted(arr)
min_value = sorted_arr[0]
print(f"The minimum value is: {min_value}")

Variations of the Problem

The minimum value finding problem can have various variations. For example, the problem can be modified as follows.

1. Finding the Index of the Minimum Value

You can modify the problem to return not only the minimum value but also its index. In this case,
you would just need to keep track of the index when updating the minimum value.

def find_min_index(arr):
    if not arr:
        return None, None

    min_value = arr[0]
    min_index = 0

    for i in range(len(arr)):
        if arr[i] < min_value:
            min_value = arr[i]
            min_index = i

    return min_value, min_index

# Example usage
min_value, min_index = find_min_index(arr)
print(f"The minimum value is: {min_value}, index is: {min_index}")

2. Returning Multiple Minimum Values

If there are multiple minimum values in the array, you can consider a method that returns all of them.
In this case, once the minimum value is determined, you would store and return all indices that have that minimum value.

def find_all_min(arr):
    if not arr:
        return [], None

    min_value = arr[0]
    min_indices = []

    for i in range(len(arr)):
        if arr[i] < min_value:
            min_value = arr[i]
            min_indices = [i]  # Record new index when the minimum value changes
        elif arr[i] == min_value:
            min_indices.append(i)  # Add same minimum value

    return min_indices, min_value

# Example usage
min_indices, min_value = find_all_min(arr)
print(f"The minimum value is: {min_value}, indices are: {min_indices}")

Conclusion

Today, we explored various methods to find the minimum value in an array through the "Finding the Minimum Value" problem.
We covered not only the basic iterative method but also built-in functions, recursive approaches, and methods through sorting.
Additionally, we presented ways to solve more complex situations through variations of the problem.
Since such problems are frequently asked in coding tests, it is important to understand and practice various approaches.

Practice Problems

Please solve the following practice problems.

  • Write a function to find the minimum value after removing duplicate elements from the given array.
  • Write a function to find the minimum value in a two-dimensional array.
  • Write a function to find the k-th minimum value in an unsorted array.