Problem Description

This problem asks you to rename the columns of a DataFrame called students. The DataFrame contains information about students with four columns: id, first, last, and age.

Your task is to rename these columns to more descriptive names:

• id should become student_id
• first should become first_name
• last should become last_name
• age should become age_in_years

The solution uses pandas' rename() method with a dictionary mapping the old column names to the new ones. The inplace=True parameter modifies the original DataFrame directly rather than creating a copy. After renaming, the function returns the modified DataFrame with the updated column names while keeping all the data intact.

For example, if the input DataFrame has a row with values [1, "Mason", "King", 6] under columns [id, first, last, age], the output will have the same row values but under the renamed columns [student_id, first_name, last_name, age_in_years].

Intuition

When we need to change column names in a DataFrame, we're essentially performing a mapping operation - taking each old name and replacing it with a new name. The most straightforward way to express this mapping is through a dictionary, where keys represent the current names and values represent the desired new names.

The pandas library provides the rename() method specifically for this purpose. Since we have a clear one-to-one mapping between old and new column names, we can create a dictionary that captures these relationships:

• 'id' → 'student_id'
• 'first' → 'first_name'
• 'last' → 'last_name'
• 'age' → 'age_in_years'

The key insight is that column renaming is a metadata operation - we're only changing the labels, not the actual data. This makes it a very efficient operation. By using inplace=True, we avoid creating an unnecessary copy of the entire DataFrame, which saves memory especially when dealing with large datasets.

This approach is intuitive because it mirrors how we think about renaming: "I want to change this name to that name." The dictionary structure naturally represents this thought process, making the code readable and maintainable.

Solution Approach

The implementation uses pandas' built-in rename() method to change the column names. Here's how the solution works step by step:

1. Define the mapping dictionary: Create a dictionary that maps each old column name to its new name:

   columns={
       'id': 'student_id',
       'first': 'first_name',
       'last': 'last_name',
       'age': 'age_in_years',
   }

2. Apply the rename operation: Call the rename() method on the DataFrame with the mapping dictionary:

   students.rename(columns={...}, inplace=True)

3. In-place modification: The inplace=True parameter tells pandas to modify the original DataFrame directly instead of creating a new one. This is more memory-efficient as it avoids duplicating the data.

4. Return the modified DataFrame: After the renaming operation completes, return the same DataFrame object (now with updated column names):

   return students

The entire operation happens in constant time relative to the number of rows since we're only updating the column metadata, not iterating through the data. The space complexity is O(1) when using inplace=True, as we're not creating a copy of the DataFrame.

This pattern of using a dictionary for mapping transformations is common in pandas operations and provides a clean, declarative way to specify the desired changes.

Example Walkthrough

Let's walk through a concrete example to illustrate how the column renaming works.

Initial DataFrame: Suppose we have a students DataFrame with the following data:

Step 1: Create the mapping dictionary We define how each column should be renamed:

columns = {
    'id': 'student_id',
    'first': 'first_name', 
    'last': 'last_name',
    'age': 'age_in_years'
}

Step 2: Apply the rename operation When we call students.rename(columns=columns, inplace=True), pandas looks at each current column name and checks if it exists as a key in our dictionary:

• Finds 'id' → replaces with 'student_id'
• Finds 'first' → replaces with 'first_name'
• Finds 'last' → replaces with 'last_name'
• Finds 'age' → replaces with 'age_in_years'

Step 3: Result The DataFrame now has updated column names while preserving all the original data:

Notice that only the column headers changed - the actual data values remain exactly the same. The operation is like relabeling the columns without touching the contents, similar to changing the labels on filing cabinet drawers without moving the files inside.

Solution Implementation

Time and Space Complexity

Time Complexity: O(1)

The rename() operation in pandas with inplace=True modifies the column labels directly by updating the internal column index mapping. This is a constant-time operation as it only involves updating references/pointers to the column names in the DataFrame's metadata, regardless of the number of rows in the DataFrame. The operation doesn't iterate through the data values themselves.

Space Complexity: O(1)

With inplace=True, the renaming operation modifies the existing DataFrame object without creating a copy of the data. Only the column index mapping is updated in-place, which requires a constant amount of additional memory for the new column name strings. The space used doesn't scale with the size of the input DataFrame (number of rows or columns beyond those being renamed).

Common Pitfalls

1. Forgetting to Return the DataFrame When Using inplace=True

A common mistake is assuming that rename() with inplace=True returns the modified DataFrame. In reality, when inplace=True is used, the method returns None and modifies the original DataFrame directly.

Incorrect approach:

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    # This returns None!
    return students.rename(columns={
        'id': 'student_id',
        'first': 'first_name',
        'last': 'last_name',
        'age': 'age_in_years',
    }, inplace=True)

Correct approach:

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    students.rename(columns={
        'id': 'student_id',
        'first': 'first_name',
        'last': 'last_name',
        'age': 'age_in_years',
    }, inplace=True)
    return students  # Return the modified DataFrame

2. Not Handling Missing or Extra Columns

If the input DataFrame doesn't have all expected columns or has additional columns, the code will still work but may not behave as expected. The rename() method only renames columns that exist and match the keys in the mapping dictionary.

More robust approach:

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    # Check if expected columns exist
    expected_columns = {'id', 'first', 'last', 'age'}
    if not expected_columns.issubset(students.columns):
        missing = expected_columns - set(students.columns)
        raise ValueError(f"Missing expected columns: {missing}")
  
    column_mapping = {
        'id': 'student_id',
        'first': 'first_name',
        'last': 'last_name',
        'age': 'age_in_years',
    }
  
    students.rename(columns=column_mapping, inplace=True)
    return students

3. Confusion Between inplace=True and Creating a Copy

Some developers prefer to avoid inplace=True to maintain functional programming principles and avoid side effects. If you want to preserve the original DataFrame, don't use inplace=True:

Alternative approach without modifying the original:

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    # Creates and returns a new DataFrame with renamed columns
    return students.rename(columns={
        'id': 'student_id',
        'first': 'first_name',
        'last': 'last_name',
        'age': 'age_in_years',
    })

4. Case Sensitivity Issues

Column names in pandas are case-sensitive. If the actual column names have different casing than expected, the rename operation will silently do nothing for those columns.

Solution with case-insensitive handling:

def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    # Create a case-insensitive mapping
    current_columns = {col.lower(): col for col in students.columns}
  
    column_mapping = {}
    mappings = {
        'id': 'student_id',
        'first': 'first_name',
        'last': 'last_name',
        'age': 'age_in_years',
    }
  
    for old_name, new_name in mappings.items():
        if old_name.lower() in current_columns:
            column_mapping[current_columns[old_name.lower()]] = new_name
  
    students.rename(columns=column_mapping, inplace=True)
    return students