Create a DataFrame from List - Practice Coding Problems

Create a DataFrame from List - Problem

Pandas Easy

You're a data scientist who just received student information in the form of a 2D list. Your task is to transform this raw data into a properly structured pandas DataFrame for further analysis.

Given a 2D list called student_data where each inner list contains exactly two elements:

The first element is the student ID
The second element is the student's age

Create a DataFrame with two columns: student_id and age. The DataFrame should preserve the exact same order as the original 2D list.

Example:

Input: [[1, 15], [2, 11], [3, 11], [4, 20]]

Output: A DataFrame with columns 'student_id' and 'age' containing the respective values.

Input & Output

example_1.py — Basic Student Data

$ Input: student_data = [[1, 15], [2, 11], [3, 11], [4, 20]]

› Output: student_id age 0 1 15 1 2 11 2 3 11 3 4 20

💡 Note: The 2D list is converted to a DataFrame where the first column becomes 'student_id' and the second column becomes 'age', maintaining the original order.

example_2.py — Single Student

$ Input: student_data = [[100, 18]]

› Output: student_id age 0 100 18

💡 Note: Even with a single student, the DataFrame is created with the same column structure.

example_3.py — Mixed Ages

$ Input: student_data = [[5, 25], [10, 30], [15, 22]]

› Output: student_id age 0 5 25 1 10 30 2 15 22

💡 Note: The function handles various student IDs and ages, preserving the exact order from the input list.

Visualization

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Understanding the Visualization

Raw Input

2D list with student information scattered as nested arrays

Structure Recognition

Pandas identifies the pattern: each sub-list represents a row, elements represent column values

Column Assignment

Apply meaningful column names 'student_id' and 'age' to give context to the data

DataFrame Creation

Final organized structure ready for data analysis and manipulation

Key Takeaway

🎯 Key Insight: Pandas DataFrame constructor automatically handles the conversion from 2D lists to structured data when provided with appropriate column names, making data transformation seamless and efficient.

Time & Space Complexity

Time Complexity

⏱️

O(n)

Pandas internally processes each row once to create the DataFrame, where n is the number of students

✓ Linear Growth

Space Complexity

O(n)

Space is used to store the DataFrame structure and data, no additional intermediate storage needed

⚡ Linearithmic Space

Constraints

1 ≤ len(student_data) ≤ 10³
Each inner list contains exactly 2 elements: [student_id, age]
1 ≤ student_id ≤ 10⁶
1 ≤ age ≤ 150
student_data is guaranteed to be a valid 2D list

Asked in

G Google 25 a Amazon 18 f Meta 15 N Netflix 12

The optimal solution uses pandas' DataFrame constructor directly: pd.DataFrame(student_data, columns=['student_id', 'age']). This leverages pandas' built-in optimization for converting 2D lists to DataFrames with O(n) time complexity and minimal code.

Common Approaches

Approach	Time	Space	Notes
✓ Direct DataFrame Constructor (Optimal)	O(n)	O(n)	Use pandas DataFrame constructor directly with the 2D list and column names
Manual DataFrame Construction	O(n)	O(n)	Create DataFrame by manually extracting columns and constructing it step by step

Direct DataFrame Constructor (Optimal) — Algorithm Steps

Import pandas library
Call pd.DataFrame() constructor with the 2D list as data parameter
Specify columns parameter with ['student_id', 'age']
Return the created DataFrame

Visualization

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Step-by-Step Walkthrough

Input 2D List

Raw student data as nested lists

DataFrame Constructor

pd.DataFrame(data, columns) processes the data

Structured DataFrame

Final DataFrame with labeled columns

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>

typedef struct {
    int student_id;
    int age;
} Student;

typedef struct {
    Student* records;
    int size;
} DataFrame;

DataFrame createDataframe(int student_data[][2], int n) {
    DataFrame df;
    df.records = malloc(n * sizeof(Student));
    df.size = n;
    
    for (int i = 0; i < n; i++) {
        df.records[i].student_id = student_data[i][0];
        df.records[i].age = student_data[i][1];
    }
    
    return df;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Pandas internally processes each row once to create the DataFrame, where n is the number of students

✓ Linear Growth

Space Complexity

O(n)

Space is used to store the DataFrame structure and data, no additional intermediate storage needed

⚡ Linearithmic Space

Constraints

1 ≤ len(student_data) ≤ 10³
Each inner list contains exactly 2 elements: [student_id, age]
1 ≤ student_id ≤ 10⁶
1 ≤ age ≤ 150
student_data is guaranteed to be a valid 2D list

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Direct DataFrame Constructor (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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