Number of Senior Citizens

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ArraysStrings

You are given a 0-indexed array of strings details. Each element of details provides information about a given passenger compressed into a string of length 15. The system is such that:

The first ten characters consist of the phone number of passengers.
The next character denotes the gender of the person.
The following two characters are used to indicate the age of the person.
The last two characters determine the seat allotted to that person.

Return the number of passengers who are strictly more than 60 years old.

Example 1:

Input: details = ["7868190130M7522","5303914400F9211","9273338290F4010"]
Output: 2
Explanation: The passengers at indices 0, 1, and 2 have ages 75, 92, and 40. Thus, there are 2 people who are over 60 years old.

Example 2:

Input: details = ["1313579440F2036","2921522980M5644"]
Output: 0
Explanation: None of the passengers are older than 60.

Constraints:

1 <= details.length <= 100
details[i].length == 15
details[i] consists of digits from '0' to '9'.
details[i][10] is either 'M' or 'F' or 'O'.
The phone numbers and seat numbers of the passengers are distinct.

Try coding it in LeetCode

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

What is the data type of each element in the input array and what range of values can I expect for the age string?
What defines a 'senior citizen' numerically? Is there a minimum age I should use to determine eligibility?
What is the expected output if the input array is null or empty?
Is the input always well-formed, meaning can I always extract the age information correctly from each element of the array?
Are there any memory constraints I should be aware of, or is it safe to assume I can use additional data structures if needed?

Brute Force Solution

Approach

The goal is to count how many people are considered senior citizens based on their age information. We will look at each person's age individually and compare it to the age that defines a senior citizen. Then, we add up the number of people who meet the senior citizen criteria.

Here's how the algorithm would work step-by-step:

Take the information of the first person.
Extract the age from this information.
Compare the extracted age to the minimum age considered as a senior citizen.
If the person's age is greater than or equal to the minimum senior citizen age, mark that person as a senior citizen.
Move on to the next person and repeat steps 2 through 4.
Continue this process for all the people in the provided information.
Finally, count the total number of people marked as senior citizens. This count represents the total number of senior citizens.

Code Implementation

def count_senior_citizens(passenger_details: list[str]) -> int:
    senior_citizen_count = 0
    minimum_senior_age = 60

    for passenger_info in passenger_details:
        # Split the passenger info to extract age
        passenger_data = passenger_info.split(',')

        passenger_age = int(passenger_data[1])

        # Check if passenger qualifies as senior citizen
        if passenger_age >= minimum_senior_age:
            # Increment the count if the person is a senior
            senior_citizen_count += 1

    return senior_citizen_count

Big(O) Analysis

Time Complexity

O(n) – The provided solution iterates through a list of people once to determine the number of senior citizens. For each person, it extracts the age and compares it to a threshold, which takes constant time. Since the number of operations scales linearly with the input size, where n is the number of people, the overall time complexity is O(n).

Space Complexity

O(1) – The algorithm iterates through the input data, processing one person's information at a time. It extracts the age, compares it, and increments a counter if the person is a senior citizen. No additional data structures like lists, arrays, or hash maps are created to store intermediate results or track visited elements. The space used is limited to a fixed number of variables (e.g., age, senior citizen count), irrespective of the number of people (N) in the input. Therefore, the space complexity is constant.

Optimal Solution

Approach

The goal is to count how many people are above a certain age, based on information in a list. We can do this efficiently by looking at a specific part of each person's information to quickly determine their age and then updating our count if needed.

Here's how the algorithm would work step-by-step:

Look at each person's information one at a time.
For each person, focus on the part of their information that tells us their age (the characters representing the age at the end of the given string).
Check if the age is greater than the senior citizen age (which is 60).
If the person is a senior citizen, increase the number of senior citizens we have counted.
Once we have checked all people, the final number we counted is the total number of senior citizens.

Code Implementation

def countSeniors(passenger_info: list[str]) -> int:
    senior_citizen_count = 0
    for person in passenger_info:
        # Extract age from the end of the string.
        age_string = person[-2:]

        age = int(age_string)

        # We only count folks above 60.
        if age > 60:
            senior_citizen_count += 1

    return senior_citizen_count

Big(O) Analysis

Time Complexity

O(n) – The algorithm iterates through the input array of passengers once. For each passenger, it extracts the age, which is a constant-time operation (extracting the last two characters of a string). The comparison with the senior age (60) is also a constant-time operation. Therefore, the time complexity is directly proportional to the number of passengers, denoted as n. Thus, the overall time complexity is O(n).

Space Complexity

O(1) – The algorithm iterates through the input list of strings, but it only uses a single integer variable to count the number of senior citizens. This counter requires constant extra space, irrespective of the number of strings in the input list. No auxiliary data structures are created or used that scale with the input size N (where N is the number of strings in the input). Therefore, the space complexity is O(1).

Edge Cases

Case	How to Handle
Null or empty list of details	Return 0 if the input list is null or empty, as there are no citizens to process.
List of details with empty strings	Handle empty strings gracefully, either by skipping them or treating them as invalid data leading to a default age.
List of details with malformed strings	Implement robust error handling for malformed strings and invalid age formats; consider logging the errors and skipping such entries.
Age calculated is negative due to birth year being later than current year	Return 0 or throw exception, if age is negative, it means invalid birth or arrival year
Extremely large list of details	Ensure the solution's space complexity is efficient, potentially processing data in chunks to avoid memory issues.
All individuals are senior citizens	The solution should correctly identify all individuals if their ages meet or exceed the senior citizen threshold.
No individuals are senior citizens	The solution should return 0 if none of the individuals meet the age criteria.
Edge case year calculations (birth year close to current year)	Carefully handle year boundary conditions (e.g., birth year close to the current year) to avoid off-by-one errors in age calculation.

Null or empty list of details

How to Handle:

Return 0 if the input list is null or empty, as there are no citizens to process.

List of details with empty strings

How to Handle:

Handle empty strings gracefully, either by skipping them or treating them as invalid data leading to a default age.

List of details with malformed strings

How to Handle:

Implement robust error handling for malformed strings and invalid age formats; consider logging the errors and skipping such entries.

Age calculated is negative due to birth year being later than current year

How to Handle:

Return 0 or throw exception, if age is negative, it means invalid birth or arrival year

Extremely large list of details

How to Handle:

Ensure the solution's space complexity is efficient, potentially processing data in chunks to avoid memory issues.

All individuals are senior citizens

How to Handle:

The solution should correctly identify all individuals if their ages meet or exceed the senior citizen threshold.

No individuals are senior citizens

How to Handle:

The solution should return 0 if none of the individuals meet the age criteria.

Edge case year calculations (birth year close to current year)

How to Handle:

Carefully handle year boundary conditions (e.g., birth year close to the current year) to avoid off-by-one errors in age calculation.