Problem Description

In this problem, we are tasked with constructing a DataFrame using the pandas library in Python. pandas is a powerful data manipulation library that provides numerous tools to work with structured data. A DataFrame is one of the core structures in pandas, designed to mimic the functionality of a database table or an Excel spreadsheet – essentially, it's a labeled two-dimensional array where each column can be of a different data type.

We are given a 2-dimensional list student_data where each sub-list contains two elements: the first element represents a student's ID, and the second is the student's age. The challenge is to convert this 2D list into a DataFrame with two columns. Those columns are to be named student_id and age.

The DataFrame must maintain the same row order as it was in the provided 2D list. For example, if the input is [[1, 20], [2, 22]], the resulting DataFrame should look like this:

Creating DataFrames from various types of data is a common operation in data analysis, as it facilitates easy data manipulation, filtering, and analysis.

Intuition

The intuitive approach for solving this problem is to leverage the DataFrame constructor method provided by pandas. This method can accept various forms of input data including lists, dictionaries, and other DataFrames. When given a 2D list, each sub-list is interpreted as a row in the DataFrame.

In order to ensure that the columns of the DataFrame are correctly labeled, we specify the columns parameter when calling the DataFrame constructor, passing in a list with the desired column names: ['student_id', 'age'].

Since the task does not involve any complex transformations, the provided code simply passes the student_data list and the column names to the DataFrame constructor and returns the newly created DataFrame. The rationale is that by directly utilizing the built-in functionality of pandas, we can create the required DataFrame object in an efficient and readable way.

Solution Approach

The solution approach is straightforward, utilizing the pandas library's data structure and functionality without employing complex algorithms or design patterns.

Here are the steps for the implementation:

1. Import the pandas library to gain access to the DataFrame construction capability.

   import pandas as pd

2. Define the function createDataframe which takes one argument, student_data. This argument is expected to be a 2-dimensional list where the inner lists consist of exactly two integers, the student's ID and the student's age.

3. Inside the function, the DataFrame constructor of pandas is called with two parameters. The first parameter is the student_data list itself, and the second is the columns parameter, which is a list containing the column names, namely ['student_id', 'age'].

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

4. The DataFrame constructor interprets each sub-list in student_data as a row in the DataFrame, with the first integer in the sub-list being placed under the student_id column, and the second under the age column.

By adhering to the Pythonic principle of "simple is better than complex," this solution avoids overengineering, relying on the efficient and well-tested internal mechanisms of the pandas library to achieve the desired result.

No special algorithm or additional data structures are required. The pattern used is one of directly mapping the input data to the DataFrame structure. This makes the code highly readable, easy to maintain, and efficient for the problem at hand.

In summary, the approach takes advantage of pandas's built-in functions to transform a 2D list into a structured DataFrame with minimal code, achieving the goal with elegance and efficiency.

Example Walkthrough

Let's walk through a small example to illustrate the solution approach. Suppose we have the following 2D list which represents student_data:

student_data = [[101, 18], [102, 19], [103, 20]]

Each sub-list contains a student's ID and the student's age. We want to create a DataFrame from this list where each student's information is a row, with student_id and age as column headers.

By following the steps outlined in the solution approach:

1. First, we import the pandas library, which is essential for creating DataFrames.

   import pandas as pd

2. We define a function createDataframe that will accept student_data as an argument.

   def createDataframe(student_data):

3. Within the function, we call the DataFrame constructor of pandas, passing the student_data list and providing the column names ['student_id', 'age'] through the columns parameter.

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

4. As a result, the DataFrame constructor interprets each inner list from student_data as a row. For our example, the constructor will create a DataFrame that looks like this:

This process creates a pandas DataFrame with the correct row order and the specified column names. Finally, calling our createDataframe function with student_data:

df = createDataframe(student_data)
print(df)

Will output:

   student_id  age
0         101   18
1         102   19
2         103   20

Our example demonstrates the simplicity and elegance of the solution approach, relying on the powerful pandas library to efficiently create a DataFrame from a 2D list with the desired structure and labels.

Solution Implementation

Time and Space Complexity

The time complexity of creating a dataframe using pandas.DataFrame is generally O(n) where n is the total number of elements in the input list student_data. Each element (a sub-list in this case) is inserted into the DataFrame during creation.

The space complexity is also O(n) since the DataFrame stores all elements of the input list. Each sub-list corresponds to a row in the DataFrame, and each element within a sub-list corresponds to a cell in the DataFrame.