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Get Full Names II in Python (Time Complexity: O(n))


Given an array of strings names that contain first and last names adjecent to each other, return an array containing the full names (separated by one space)

Example:

Input:  names = ["John", "Doe", "Andy", "Smith", "Mary", "Johnson"]

Output:  ["John Doe", "Andy Smith", "Mary Johnson"]

Understanding the Problem

The core challenge of this problem is to pair adjacent elements in the input list to form full names. This is a common task in data processing where names might be split into separate fields.

Potential pitfalls include handling lists with an odd number of elements or empty lists. However, based on the problem statement, we assume the input list always contains an even number of elements.

Approach

To solve this problem, we can iterate through the list in steps of two, combining each pair of adjacent elements into a single string. This approach ensures that we efficiently process the list in a single pass.

Let's break down the steps:

  1. Initialize an empty list to store the full names.
  2. Iterate through the input list in steps of two.
  3. For each pair of elements, concatenate them with a space in between and add the result to the full names list.
  4. Return the full names list.

Algorithm

Here is a step-by-step breakdown of the algorithm:

  1. Initialize an empty list full_names.
  2. Use a for loop to iterate through the input list names with a step of 2.
  3. In each iteration, concatenate the current element and the next element with a space in between.
  4. Append the concatenated string to full_names.
  5. After the loop, return full_names.

Code Implementation

def get_full_names(names):
    # Initialize an empty list to store the full names
    full_names = []
    
    # Iterate through the list in steps of 2
    for i in range(0, len(names), 2):
        # Concatenate the current element and the next element with a space in between
        full_name = names[i] + " " + names[i + 1]
        # Append the concatenated string to the full_names list
        full_names.append(full_name)
    
    # Return the list of full names
    return full_names

# Example usage
names = ["John", "Doe", "Andy", "Smith", "Mary", "Johnson"]
print(get_full_names(names))  # Output: ["John Doe", "Andy Smith", "Mary Johnson"]

Complexity Analysis

The time complexity of this approach is O(n), where n is the number of elements in the input list. This is because we iterate through the list once, processing each pair of elements in constant time.

The space complexity is also O(n) because we store the full names in a new list.

Edge Cases

Potential edge cases include:

  • An empty list: The function should return an empty list.
  • A list with an odd number of elements: Based on the problem statement, we assume this won't happen.

Example edge case:

Input:  names = []
Output:  []

Testing

To test the solution comprehensively, consider the following test cases:

  • Standard case with multiple pairs of names.
  • Empty list.
  • List with a single pair of names.

Example test cases:

# Test case 1: Standard case
names = ["John", "Doe", "Andy", "Smith", "Mary", "Johnson"]
assert get_full_names(names) == ["John Doe", "Andy Smith", "Mary Johnson"]

# Test case 2: Empty list
names = []
assert get_full_names(names) == []

# Test case 3: Single pair of names
names = ["Alice", "Brown"]
assert get_full_names(names) == ["Alice Brown"]

Thinking and Problem-Solving Tips

When approaching such problems, consider the following tips:

  • Break down the problem into smaller, manageable parts.
  • Think about edge cases and how to handle them.
  • Write pseudocode to outline your approach before coding.
  • Test your solution with a variety of test cases.

To improve problem-solving skills, practice regularly on coding challenge platforms and study different algorithms and data structures.

Conclusion

In this blog post, we discussed how to solve the problem of combining adjacent first and last names into full names. We explored the problem, developed an efficient algorithm, and implemented it in Python. By understanding the problem and testing our solution, we ensured its correctness and efficiency.

Understanding and solving such problems is crucial for data processing and manipulation tasks. Practice regularly to improve your problem-solving skills and explore further challenges.

Additional Resources

For further reading and practice, consider the following resources: