Get the Size of a DataFrame - Practice Coding Problems

Get the Size of a DataFrame - Problem

Pandas Easy

You're given a pandas DataFrame called players that contains information about sports players. Your task is to determine the dimensions of this DataFrame - specifically, how many rows and columns it contains.

DataFrame Schema:

Column Name	Type
player_id	int
name	object
age	int
position	object
...	...

Goal: Write a function that returns the DataFrame's dimensions as a list: [number_of_rows, number_of_columns]

This is a fundamental pandas operation that you'll use frequently in data analysis to understand the size and structure of your datasets.

Input & Output

example_1.py — Small DataFrame

$ Input: players = pd.DataFrame({ 'player_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Carol'], 'age': [25, 27, 23], 'position': ['Forward', 'Midfielder', 'Defender'] })

› Output: [3, 4]

💡 Note: The DataFrame has 3 rows (players) and 4 columns (player_id, name, age, position)

example_2.py — Single Row DataFrame

$ Input: players = pd.DataFrame({ 'player_id': [1], 'name': ['Alice'], 'age': [25] })

› Output: [1, 3]

💡 Note: The DataFrame has 1 row and 3 columns

example_3.py — Empty DataFrame

$ Input: players = pd.DataFrame(columns=['player_id', 'name', 'age', 'position'])

› Output: [0, 4]

💡 Note: The DataFrame has 0 rows but still has 4 defined columns

Visualization

Tap to expand

Understanding the Visualization

Identify DataFrame Structure

DataFrame is like a table with rows representing records and columns representing attributes

Access Shape Metadata

Pandas stores dimension information as metadata, accessible via .shape property

Return Dimensions

Convert the (rows, cols) tuple to [rows, cols] list format

Key Takeaway

🎯 Key Insight: The .shape property provides instant access to DataFrame dimensions without any computation, making it the optimal O(1) solution for this problem.

Time & Space Complexity

Time Complexity

⏱️

O(1)

Shape is stored as metadata, accessed in constant time

✓ Linear Growth

Space Complexity

O(1)

Only creating a small list to return the result

✓ Linear Space

Constraints

The DataFrame will be a valid pandas DataFrame object
Number of rows: 0 ≤ rows ≤ 10⁶
Number of columns: 1 ≤ columns ≤ 10³
The DataFrame structure is guaranteed to be consistent

Asked in

N Netflix 45 ♫ Spotify 38 🏠 Airbnb 32 🚗 Uber 28

The optimal solution uses pandas' built-in .shape attribute which returns a tuple (rows, columns) in O(1) constant time. Simply convert this tuple to a list using list(df.shape) to get the required format [rows, columns].

Common Approaches

Approach	Time	Space	Notes
✓ Built-in Shape Property (Optimal)	O(1)	O(1)	Use pandas DataFrame's built-in .shape attribute for instant dimensions
Manual Counting (Brute Force)	O(n*m)	O(1)	Manually iterate through rows and columns to count dimensions

Built-in Shape Property (Optimal) — Algorithm Steps

Access the DataFrame's .shape attribute
Convert the tuple to a list
Return the result

Visualization

Tap to expand

Step-by-Step Walkthrough

Access .shape

DataFrame.shape returns (rows, cols) tuple

Convert to List

Convert tuple to list format

Return Result

Return [rows, cols] in O(1) time

Code -

solution.c — C

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

int* getDataframeSize(int** players, int rows, int cols) {
    int* result = malloc(2 * sizeof(int));
    result[0] = rows;  // Number of rows passed as parameter
    result[1] = cols;  // Number of columns passed as parameter
    return result;
}

// Alternative with struct return
typedef struct {
    int rows;
    int cols;
} DataFrameSize;

DataFrameSize getDataframeSizeStruct(int** players, int rows, int cols) {
    DataFrameSize size = {rows, cols};
    return size;
}

Time & Space Complexity

Time Complexity

⏱️

O(1)

Shape is stored as metadata, accessed in constant time

✓ Linear Growth

Space Complexity

O(1)

Only creating a small list to return the result

✓ Linear Space

Constraints

The DataFrame will be a valid pandas DataFrame object
Number of rows: 0 ≤ rows ≤ 10⁶
Number of columns: 1 ≤ columns ≤ 10³
The DataFrame structure is guaranteed to be consistent

68.5K Views

High Frequency

~5 min Avg. Time

2.3K Likes

Ln 1, Col 1

Smart Actions

💡 Explanation

AI Ready

💡 Suggestion Tab to accept Esc to dismiss

// Output will appear here after running code

Code Editor Closed

Click the red button to reopen

Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Built-in Shape Property (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

Select Compiler