Problem Description

This problem asks you to create a pandas DataFrame from a 2D list containing student information.

You are given a 2D list called student_data where each inner list represents a student and contains two integers:

• The first integer is the student's ID
• The second integer is the student's age

Your task is to convert this 2D list into a pandas DataFrame with:

• Two columns named student_id and age
• The data should maintain the same order as in the original 2D list

For example, if student_data = [[1, 15], [2, 11], [3, 11], [4, 20]], the resulting DataFrame should have:

• A student_id column with values: 1, 2, 3, 4
• An age column with values: 15, 11, 11, 20

The solution uses the pd.DataFrame() constructor, passing the 2D list as the data parameter and specifying the column names using the columns parameter. This directly creates a DataFrame with the required structure and column names.

Intuition

The key insight here is recognizing that pandas DataFrames are designed to work seamlessly with 2D data structures like lists of lists. Each inner list in our 2D structure naturally maps to a row in the DataFrame, and each position within those inner lists corresponds to a column.

Since we have a 2D list where each inner list has exactly two elements (student ID and age), we can think of this as tabular data with two columns. The pandas library provides a direct way to convert such structured data into a DataFrame.

The most straightforward approach is to use the pd.DataFrame() constructor, which accepts various data structures including 2D lists. When we pass our student_data directly to this constructor, pandas automatically:

1. Treats each inner list as a row
2. Uses the position of elements (index 0 and index 1) to determine which column they belong to

However, by default, pandas would create columns with numeric names (0, 1). Since we need specific column names (student_id and age), we use the columns parameter to explicitly name them in the order they appear in each inner list.

This approach works because there's a direct one-to-one mapping between our input format (2D list) and the desired output format (DataFrame with rows and columns). No data transformation or manipulation is needed - just a format conversion with proper column naming.

Solution Approach

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

1. Import pandas library: We need pandas to work with DataFrames, imported as pd by convention.

2. Use the DataFrame constructor: The pd.DataFrame() function is called with two key arguments:

   • Data parameter: Pass the student_data (2D list) directly as the first argument. Pandas automatically interprets each inner list as a row.
   • Columns parameter: Specify columns=['student_id', 'age'] to assign meaningful names to the two columns in the exact order they appear in each inner list.

3. Return the DataFrame: The constructor creates and returns a properly formatted DataFrame object.

The complete implementation in one line:

return pd.DataFrame(student_data, columns=['student_id', 'age'])

This solution works because:

• Pandas DataFrames natively accept 2D lists as input data
• Each inner list [id, age] becomes a row in the DataFrame
• The columns parameter maps the first element of each inner list to 'student_id' and the second element to 'age'
• The row order is preserved from the original 2D list

No explicit loops, data transformation, or intermediate data structures are needed. The pandas library handles all the internal conversion from the list structure to the DataFrame's internal representation.

Example Walkthrough

Let's walk through a small example to illustrate how the solution works.

Given Input:

student_data = [[101, 18], [102, 19], [103, 17]]

Step-by-Step Process:

1. Initial Data Structure

   • We have a 2D list with 3 inner lists
   • Each inner list contains 2 elements: [student_id, age]

   [[101, 18],
    [102, 19], 
    [103, 17]]

2. Pass to DataFrame Constructor

   • When we call pd.DataFrame(student_data, columns=['student_id', 'age']):
   • Pandas reads the first inner list [101, 18] and creates the first row
   • The value 101 goes to column 'student_id' (first column)
   • The value 18 goes to column 'age' (second column)
   • This process repeats for each inner list

3. Mapping Process

   [101, 18]  →  Row 0: student_id=101, age=18
   [102, 19]  →  Row 1: student_id=102, age=19
   [103, 17]  →  Row 2: student_id=103, age=17

4. Final DataFrame Output

      student_id  age
   0         101   18
   1         102   19
   2         103   17

The key is that pandas automatically interprets:

• Each inner list as a complete row
• The position of elements within each list corresponds to the column order specified
• The columns parameter assigns names in the same order: first element → 'student_id', second element → 'age'

This direct mapping eliminates the need for any loops or manual data transformation - pandas handles the conversion internally through its DataFrame constructor.

Solution Implementation

Time and Space Complexity

Time Complexity: O(n × m) where n is the number of rows (students) and m is the number of columns (2 in this case: student_id and age).

The pd.DataFrame() constructor needs to iterate through the input data to create the internal data structure. Since we have n students and each student has m attributes (student_id and age), the constructor must process n × m elements. Given that m = 2 is constant in this specific case, we can simplify this to O(n).

Space Complexity: O(n × m) where n is the number of rows and m is the number of columns.

The DataFrame stores all the input data in its internal structure. For n students with m = 2 attributes each, the space required is proportional to n × m. Since m = 2 is constant here, the space complexity can be simplified to O(n). Additionally, the column names list ['student_id', 'age'] requires O(1) space as it's a fixed-size list regardless of the input size.

Common Pitfalls

1. Missing or Incorrect Column Order

A frequent mistake is assuming pandas will automatically know the column names or accidentally reversing them:

Incorrect:

# Missing column names - creates default numeric column names (0, 1)
return pd.DataFrame(student_data)

# Wrong column order
return pd.DataFrame(student_data, columns=['age', 'student_id'])

Solution: Always explicitly specify column names in the correct order matching the data structure:

return pd.DataFrame(student_data, columns=['student_id', 'age'])

2. Handling Empty Input

The function may receive an empty list, which could cause unexpected behavior if not handled properly:

Potential Issue:

student_data = []
df = pd.DataFrame(student_data, columns=['student_id', 'age'])
# Creates an empty DataFrame with correct columns but no rows

Solution: This actually works correctly - pandas handles empty lists gracefully and creates an empty DataFrame with the specified columns. However, be aware that operations on empty DataFrames might behave differently than expected.

3. Type Confusion with Dictionary Constructor

Some might try to use a dictionary approach incorrectly:

Incorrect:

# This won't work as expected with the 2D list structure
return pd.DataFrame({'student_id': student_data[0], 'age': student_data[1]})

Solution: When working with a 2D list where each inner list is a row, use the list directly with column names. Use dictionary construction only when you have separate lists for each column:

# If you had separate lists:
ids = [1, 2, 3, 4]
ages = [15, 11, 11, 20]
df = pd.DataFrame({'student_id': ids, 'age': ages})

# But with 2D list structure, use:
df = pd.DataFrame(student_data, columns=['student_id', 'age'])

4. Malformed Input Data

If inner lists have inconsistent lengths, pandas will raise a ValueError:

Problematic Input:

student_data = [[1, 15], [2], [3, 11, 99]]  # Inconsistent lengths

Solution: Validate input data before creating the DataFrame:

def createDataframe(student_data: List[List[int]]) -> pd.DataFrame:
    # Optional validation
    if student_data and not all(len(row) == 2 for row in student_data):
        raise ValueError("Each student record must have exactly 2 values")
  
    return pd.DataFrame(student_data, columns=['student_id', 'age'])