To analyze the time and space complexity of a piece of code, you can follow these steps:

1. Identify Loops and Recursion: Look for loops (for, while, etc.) and recursive function calls in the code. These are often the main factors affecting time and space complexity.

2. Count Operations: Count the number of operations performed inside the loops or recursive calls. This includes arithmetic operations, assignments, function calls, comparisons, and any other significant operations.

3. Determine the Input Size: Identify the variable(s) that represent the input size. It could be the length of an array, the number of elements, or any other parameter that affects the execution of the code.

4. Express Time Complexity: Based on the count of operations and the input size, express the time complexity using Big O notation. Focus on the dominant factor that grows the fastest as the input size increases. For example:

   - If the code performs a single loop that executes 'n' times, the time complexity would be O(n).

   - If nested loops are present, resulting in 'n' iterations of the outer loop and 'm' iterations of the inner loop, the time complexity would be O(n*m).

   - If recursion is involved and the recursive function calls itself 'k' times, and each call has a time complexity of O(f(n)), the overall time complexity would be O(k * f(n)).

5. Analyze Space Complexity: Determine the space complexity by analyzing the memory usage in the code. Identify variables or data structures that grow with the input size and estimate their space requirements. Consider the additional space used by function call stacks in recursion.

   - If the space used is constant regardless of the input size, the space complexity is O(1).

   - If the space used grows linearly with the input size, the space complexity is O(n).

   - If there are recursive calls and the maximum depth of the recursion is 'k' and the space used per recursive call is O(f(n)), the overall space complexity would be O(k * f(n)).

It's important to note that these steps provide a general approach to analyzing time and space complexity from code. The actual complexity may vary depending on the specific details of the code and the programming language used. Additionally, it's recommended to consider the worst-case scenario when analyzing complexity.

If you provide a specific code snippet, I can help you analyze its time and space complexity in more detail.