2469. Convert the Temperature

Problem Description

You are given a temperature value in Celsius as a floating-point number rounded to two decimal places. Your task is to convert this temperature to both Kelvin and Fahrenheit scales.

The conversion formulas are:

Kelvin = Celsius + 273.15
Fahrenheit = Celsius * 1.80 + 32.00

You need to return both converted values as an array in the format [kelvin, fahrenheit].

The problem accepts answers within 10^-5 of the actual answer, allowing for small floating-point precision differences.

For example, if given celsius = 36.50, you would:

Calculate Kelvin: 36.50 + 273.15 = 309.65
Calculate Fahrenheit: 36.50 * 1.80 + 32.00 = 97.70
Return the array: [309.65, 97.70]

The solution is straightforward - apply both conversion formulas to the input Celsius value and return the results in the specified array format.

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Intuition

This is a direct mathematical conversion problem with no hidden complexity. Since we're given explicit formulas for converting Celsius to both Kelvin and Fahrenheit, the most straightforward approach is to simply apply these formulas.

The key insight is that both conversions are independent linear transformations:

Converting to Kelvin is just adding a constant (273.15)
Converting to Fahrenheit involves scaling by 1.8 and then adding 32

There's no need for optimization or clever algorithms here because:

We're performing exactly two arithmetic operations for Kelvin conversion
We're performing exactly three arithmetic operations for Fahrenheit conversion
Both conversions can be computed in constant time O(1)

The natural approach is to compute both values directly and return them in the required array format. Since the problem guarantees the input is already a valid floating-point number and accepts answers within 10^-5 precision, we don't need to worry about edge cases or precision handling - standard floating-point arithmetic will suffice.

This leads us to the simple solution: create an array containing [celsius + 273.15, celsius * 1.8 + 32] and return it.

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Solution Approach

The implementation follows a simulation approach where we directly apply the temperature conversion formulas.

Step-by-step implementation:

Input: We receive a single parameter celsius of type float
Apply the conversion formulas:
- Calculate Kelvin: celsius + 273.15
- Calculate Fahrenheit: celsius * 1.8 + 32
Return the results: Package both calculated values into a list [kelvin, fahrenheit]

The solution can be implemented in a single line:

return [celsius + 273.15, celsius * 1.8 + 32]

Why this approach works:

Both temperature conversions are deterministic mathematical operations
No intermediate variables are needed since each conversion is a simple expression
The list constructor directly creates our output format

Time Complexity: O(1) - We perform a fixed number of arithmetic operations regardless of input value

Space Complexity: O(1) - We only create one output list with exactly two elements

The beauty of this solution lies in its simplicity. Since the problem provides exact formulas and requires no validation or edge case handling, we can translate the mathematical formulas directly into code without any additional logic or data structures.

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Example Walkthrough

Let's walk through the solution with a concrete example: celsius = 22.00

Step 1: Apply Kelvin conversion

Formula: Kelvin = Celsius + 273.15
Calculation: 22.00 + 273.15 = 295.15

Step 2: Apply Fahrenheit conversion

Formula: Fahrenheit = Celsius * 1.80 + 32.00
Calculation: 22.00 * 1.80 + 32.00
- First multiply: 22.00 * 1.80 = 39.60
- Then add: 39.60 + 32.00 = 71.60

Step 3: Package results into array

Create array: [295.15, 71.60]
Return this array as the final answer

Let's trace through the code execution:

def convertTemperature(celsius):
    return [celsius + 273.15, celsius * 1.80 + 32.00]

# When celsius = 22.00:
# 1. Evaluate celsius + 273.15 → 22.00 + 273.15 → 295.15
# 2. Evaluate celsius * 1.80 + 32.00 → 22.00 * 1.80 + 32.00 → 71.60
# 3. Create list [295.15, 71.60]
# 4. Return [295.15, 71.60]

The solution directly applies both conversion formulas in a single return statement, computing both temperature values simultaneously and packaging them into the required output format.

Solution Implementation

1from typing import List
2
3class Solution:
4    def convertTemperature(self, celsius: float) -> List[float]:
5        """
6        Convert temperature from Celsius to Kelvin and Fahrenheit.
7      
8        Args:
9            celsius: Temperature value in Celsius
10          
11        Returns:
12            List containing [Kelvin, Fahrenheit] temperatures
13        """
14        # Convert Celsius to Kelvin by adding 273.15
15        kelvin = celsius + 273.15
16      
17        # Convert Celsius to Fahrenheit using formula: (C × 9/5) + 32
18        fahrenheit = celsius * 1.8 + 32
19      
20        # Return both converted temperatures as a list
21        return [kelvin, fahrenheit]
22

1class Solution {
2    /**
3     * Converts a temperature from Celsius to Kelvin and Fahrenheit.
4     * 
5     * @param celsius The temperature in Celsius to be converted
6     * @return An array containing two elements:
7     *         - Index 0: Temperature in Kelvin (Celsius + 273.15)
8     *         - Index 1: Temperature in Fahrenheit (Celsius * 1.8 + 32)
9     */
10    public double[] convertTemperature(double celsius) {
11        // Convert Celsius to Kelvin by adding 273.15
12        double kelvin = celsius + 273.15;
13      
14        // Convert Celsius to Fahrenheit using the formula: (C * 9/5) + 32
15        // Note: 9/5 = 1.8
16        double fahrenheit = celsius * 1.8 + 32;
17      
18        // Return both converted values in an array
19        return new double[] {kelvin, fahrenheit};
20    }
21}
22

1class Solution {
2public:
3    /**
4     * Converts temperature from Celsius to Kelvin and Fahrenheit
5     * @param celsius - Temperature value in Celsius
6     * @return Vector containing two values: [Kelvin, Fahrenheit]
7     */
8    vector<double> convertTemperature(double celsius) {
9        // Convert Celsius to Kelvin by adding 273.15
10        double kelvin = celsius + 273.15;
11      
12        // Convert Celsius to Fahrenheit using formula: (C × 9/5) + 32
13        double fahrenheit = celsius * 1.8 + 32.0;
14      
15        // Return both converted values in a vector
16        return {kelvin, fahrenheit};
17    }
18};
19

1/**
2 * Converts a temperature from Celsius to Kelvin and Fahrenheit
3 * @param celsius - The temperature in Celsius to be converted
4 * @returns An array containing [Kelvin, Fahrenheit] temperatures
5 */
6function convertTemperature(celsius: number): number[] {
7    // Convert Celsius to Kelvin by adding 273.15
8    const kelvin: number = celsius + 273.15;
9  
10    // Convert Celsius to Fahrenheit using the formula: (C × 9/5) + 32
11    const fahrenheit: number = celsius * 1.8 + 32;
12  
13    // Return both converted temperatures as an array
14    return [kelvin, fahrenheit];
15}
16

Time and Space Complexity

The time complexity is O(1) because the function performs a fixed number of arithmetic operations (one addition for Kelvin conversion and one multiplication plus one addition for Fahrenheit conversion) regardless of the input value. These operations take constant time.

The space complexity is O(1) because the function creates and returns a list containing exactly 2 float values, which occupies a constant amount of memory space that doesn't scale with the input.

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Common Pitfalls

1. Floating-Point Precision Issues

While this problem appears straightforward, developers often encounter unexpected floating-point precision errors when working with decimal numbers.

The Pitfall:

# Problematic approach - using intermediate calculations
celsius = 36.50
kelvin = celsius + 273.15  # Results in 309.65000000000003 on some systems
fahrenheit = celsius * 9/5 + 32  # May produce slight variations

Due to how floating-point numbers are represented in binary, operations like 36.50 + 273.15 might not yield exactly 309.65 but rather something like 309.65000000000003.

Solution: The problem statement mentions accepting answers within 10^-5 of the actual answer, which handles this gracefully. However, if exact precision were required, you could:

Round the results to a specific number of decimal places
Use Python's decimal module for arbitrary precision arithmetic

2. Using Integer Division or Wrong Formula Coefficients

The Pitfall:

# Wrong - using integer division in Python 2 style
fahrenheit = celsius * 9//5 + 32  # This truncates the result!

# Wrong - using 1.8 vs 9/5 inconsistently
fahrenheit = celsius * (9/5) + 32.00  # Correct but mixing notation

Solution: Stick to the exact formulas provided in the problem:

Use 1.8 or 1.80 for Fahrenheit conversion (not 9/5 which equals 1.8)
Always use floating-point division to maintain precision

3. Incorrect Order of Operations

The Pitfall:

# Wrong - parentheses in wrong place
fahrenheit = celsius * (1.8 + 32)  # This adds before multiplying!

Solution: Follow the mathematical order of operations (PEMDAS):

fahrenheit = celsius * 1.8 + 32  # Multiplication first, then addition

4. Returning Values in Wrong Order

The Pitfall:

# Wrong order - returning [Fahrenheit, Kelvin] instead
return [celsius * 1.8 + 32, celsius + 273.15]

The problem specifically asks for [kelvin, fahrenheit] in that order.

Solution: Always verify the required output format:

return [celsius + 273.15, celsius * 1.8 + 32]  # Kelvin first, then Fahrenheit

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Given a sorted array of integers and an integer called target, find the element that equals to the target and return its index. Select the correct code that fills the ___ in the given code snippet.

1def binary_search(arr, target):
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1public static int binarySearch(int[] arr, int target) {
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4
5    while (left ___ right) {
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1function binarySearch(arr, target) {
2    let left = 0;
3    let right = arr.length - 1;
4
5    while (left ___ right) {
6        let mid = left + Math.trunc((right - left) / 2);
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<=, left, right

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I don't know

2469. Convert the Temperature

Problem Description

Intuition

Solution Approach

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Example Walkthrough

Solution Implementation

Time and Space Complexity

Common Pitfalls

1. Floating-Point Precision Issues

2. Using Integer Division or Wrong Formula Coefficients

3. Incorrect Order of Operations

4. Returning Values in Wrong Order

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