What is the time complexity of Direct DataFrame Construction approach?

The time complexity is O(n) where n is the number of rows in the student_data array.

What is the space complexity of Dictionary Conversion approach?

The space complexity is O(n) for storing intermediate lists and the resulting DataFrame.

What is the time complexity of List of Dictionaries approach?

The time complexity is O(n) where n is the number of rows, as we iterate through each row once to create dictionaries.

Create a DataFrame from List

Difficulty: Easy | Acceptance: 81.00% | Paid: No Topics: N/A

You are given a 2D integer array student_data where student_data[i] represents the data for the ith student. The data for each student is represented as [student_id, age].

Return a pandas DataFrame with columns student_id and age containing the data from student_data.

Table of Contents
Examples
Constraints
Direct DataFrame Construction
Dictionary Conversion Approach
List of Dictionaries Approach

Examples

Example 1

Input:

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

Output:

+------------+-----+
| student_id | age |
+------------+-----+
| 1          | 15  |
| 2          | 11  |
| 3          | 11  |
| 4          | 20  |
+------------+-----+

Explanation: A DataFrame was created on top of student_data, with two columns named student_id and age.

Constraints

1 <= student_data.length <= 10⁵
student_data[i].length == 2
1 <= student_data[i][0] <= 10⁹
1 <= student_data[i][1] <= 100

Direct DataFrame Construction

Intuition Use pandas DataFrame constructor directly by passing the 2D list as data and specifying column names.

Steps

Import pandas library
Create DataFrame using pd.DataFrame() with student_data and columns parameter
Return the DataFrame

python

import pandas as pd

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

Complexity

Time: O(n) where n is the number of rows
Space: O(n) for storing the DataFrame
Notes: Most efficient and idiomatic pandas approach

Dictionary Conversion Approach

Intuition Convert the 2D list into a dictionary format where keys are column names and values are lists of data for each column.

Steps

Extract student_id values from each row into a list
Extract age values from each row into a list
Create a dictionary with column names as keys
Pass dictionary to pd.DataFrame()

python

import pandas as pd

def createDataFrame(student_data):
    student_ids = [row[0] for row in student_data]
    ages = [row[1] for row in student_data]
    data_dict = {'student_id': student_ids, 'age': ages}
    return pd.DataFrame(data_dict)

Complexity

Time: O(n) where n is the number of rows
Space: O(n) for storing intermediate lists and DataFrame
Notes: More verbose but useful when data comes from different sources

List of Dictionaries Approach

Intuition Convert each row into a dictionary with column names as keys, then create DataFrame from list of dictionaries.

Steps

Iterate through each row in student_data
Create a dictionary for each row mapping column names to values
Collect all dictionaries into a list
Pass list of dictionaries to pd.DataFrame()

python

import pandas as pd

def createDataFrame(student_data):
    data = [{'student_id': row[0], 'age': row[1]} for row in student_data]
    return pd.DataFrame(data)

Complexity

Time: O(n) where n is the number of rows
Space: O(n) for storing list of dictionaries and DataFrame
Notes: Useful when dealing with JSON-like data structures