Problem Description

You are given a DataFrame called players that contains information about players with columns including player_id, name, age, position, and potentially other columns.

The task is to write a function that determines the dimensions of this DataFrame - specifically, you need to find out how many rows and how many columns the DataFrame contains.

Your function should return these two values as a list in the format: [number of rows, number of columns]

For example, if the players DataFrame has 5 rows and 4 columns, your function should return [5, 4].

The solution uses pandas' shape attribute, which returns a tuple containing (number_of_rows, number_of_columns). By converting this tuple to a list using list(players.shape), we get the required output format.

Intuition

When working with DataFrames in pandas, we often need to know the size or dimensions of our data. The most direct way to think about this is: "What built-in property can tell us the structure of our DataFrame?"

Pandas provides the shape attribute specifically for this purpose. Every DataFrame has a shape attribute that immediately gives us both pieces of information we need - the number of rows and the number of columns. This attribute returns a tuple in the format (rows, columns).

Since the problem asks for the result as a list rather than a tuple, we simply need to convert the output. The conversion from tuple to list is straightforward using Python's list() function.

This approach is intuitive because:

1. We're using the most direct tool available - shape is designed exactly for getting DataFrame dimensions
2. No manual counting or iteration is needed
3. The operation is constant time O(1) since shape is a pre-computed property
4. We avoid any complex logic by leveraging pandas' built-in functionality

The beauty of this solution lies in its simplicity - rather than trying to count rows using len() and columns using len(df.columns) separately, we get both values in one clean operation with players.shape, then simply convert the format to match the required output.

Solution Approach

The implementation is straightforward and leverages pandas' built-in functionality:

1. Access the DataFrame's shape attribute: The players.shape property returns a tuple containing two elements:

   • First element: number of rows (index 0)
   • Second element: number of columns (index 1)

2. Convert tuple to list: Since shape returns a tuple like (rows, cols) but the problem requires a list format [rows, cols], we use Python's list() function to perform the conversion.

The complete implementation:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    return list(players.shape)

Key Points:

• players.shape returns something like (100, 5) if there are 100 rows and 5 columns
• list() converts this to [100, 5]
• The return type annotation List[int] indicates we're returning a list of integers

Why this approach works:

• The shape attribute is a property that pandas maintains for every DataFrame
• It's automatically updated whenever the DataFrame structure changes
• No computation is needed at runtime since the dimensions are already stored

This solution is optimal with O(1) time complexity since we're simply accessing a pre-computed property and performing a type conversion, making it the most efficient approach for this problem.

Example Walkthrough

Let's walk through a concrete example to understand how the solution works.

Suppose we have a players DataFrame with the following data:

   player_id    name  age position
0          1   Alice   25       PG
1          2     Bob   28       SG
2          3  Charlie  22       SF

This DataFrame has:

• 3 rows (one for each player)
• 4 columns (player_id, name, age, position)

Step 1: Access the shape attribute

When we call players.shape, pandas returns the tuple (3, 4):

• The first value (3) represents the number of rows
• The second value (4) represents the number of columns

Step 2: Convert tuple to list

Since the problem requires the output as a list, we apply list() to the tuple:

• Input: (3, 4) (tuple)
• Output: [3, 4] (list)

Complete execution:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    return list(players.shape)  # (3, 4) becomes [3, 4]

The function returns [3, 4], indicating the DataFrame has 3 rows and 4 columns.

Another example with different dimensions:

If players contained 100 player records with 7 attributes each:

• players.shape would return (100, 7)
• list(players.shape) would convert it to [100, 7]
• The function would return [100, 7]

This approach works regardless of DataFrame size - whether it has 1 row or 1 million rows, the solution remains the same single line of code.

Solution Implementation

Time and Space Complexity

Time Complexity: O(1)

The function performs the following operations:

• players.shape - Returns a tuple containing the dimensions of the DataFrame, which is a constant-time operation as these values are stored as attributes of the DataFrame object
• list() - Converts the shape tuple (containing 2 elements for rows and columns) to a list, which takes O(2) = O(1) time

Since all operations are constant time, the overall time complexity is O(1).

Space Complexity: O(1)

The function creates:

• A tuple from players.shape containing 2 integers (rows and columns count)
• A list containing the same 2 integers

Both the tuple and list have a fixed size of 2 elements regardless of the DataFrame size, resulting in O(1) space complexity for the additional space used by the function.

Note: The space occupied by the input DataFrame itself is not counted in the space complexity analysis as it's provided as input and not created by the function.

Common Pitfalls

1. Confusing the Order of Dimensions

A common mistake is assuming the wrong order for the dimensions. Some developers might expect [columns, rows] instead of [rows, columns].

Pitfall Example:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    # Wrong assumption about order
    rows, cols = players.shape
    return [cols, rows]  # Incorrect order!

Solution: Remember that shape always returns (rows, columns) in that specific order. This follows the mathematical convention of matrices where dimensions are expressed as m×n (rows × columns).

2. Accessing Shape Elements Individually

Some developers might try to access the shape elements separately, which is less efficient and more error-prone.

Pitfall Example:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    # Unnecessarily verbose and prone to errors
    num_rows = players.shape[0]
    num_cols = players.shape[1]
    return [num_rows, num_cols]

Solution: Use list(players.shape) directly for cleaner, more concise code that's less likely to have indexing errors.

3. Using len() Instead of shape

Developers might try to use len() for both dimensions, but len() only gives the number of rows.

Pitfall Example:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    # len(players) gives rows, but len(players.columns) needed for columns
    return [len(players), len(players)]  # Both values are the same!

Correct Alternative (though less elegant):

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    return [len(players), len(players.columns)]

4. Forgetting to Handle Empty DataFrames

While shape works correctly for empty DataFrames, some manual approaches might fail.

Pitfall Example:

def getDataframeSize(players: pd.DataFrame) -> List[int]:
    # This works but might lead to confusion with empty DataFrames
    if players.empty:
        # Developer might incorrectly return None or raise an exception
        return None  # Wrong! Empty DataFrame still has dimensions
    return list(players.shape)

Solution: Trust that shape handles all cases correctly, including empty DataFrames. An empty DataFrame with column definitions would return [0, n] where n is the number of columns.