Get the Size of a DataFrame

Difficulty: Easy | Acceptance: 84.80% | Paid: No Topics: N/A

Write a solution to get the size of the DataFrame. The size is the number of elements in the DataFrame.

Example 1: Input:

+------------+--------+---------+
| student_id | name   | age     |
+------------+--------+---------+
| 1          | Alice  | 20      |
| 2          | Bob    | 22      |
| 3          | Charlie| 19      |
+------------+--------+---------+

Output:

9

Example 2: Input:

+------------+
| student_id |
+------------+
| 1          |
| 2          |
+------------+

Output:

2

Examples

Example 1

Input:

+-----------+----------+-----+-------------+--------------------+
| player_id | name     | age | position    | team               |
+-----------+----------+-----+-------------+--------------------+
| 846       | Mason    | 21  | Forward     | RealMadrid         |
| 749       | Riley    | 30  | Winger      | Barcelona          |
| 155       | Bob      | 28  | Striker     | ManchesterUnited   |
| 583       | Isabella | 32  | Goalkeeper  | Liverpool          |
| 388       | Zachary  | 24  | Midfielder  | BayernMunich       |
| 883       | Ava      | 23  | Defender    | Chelsea            |
| 355       | Violet   | 18  | Striker     | Juventus           |
| 247       | Thomas   | 27  | Striker     | ParisSaint-Germain |
| 761       | Jack     | 33  | Midfielder  | ManchesterCity     |
| 642       | Charlie  | 36  | Center-back | Arsenal            |
+-----------+----------+-----+-------------+--------------------+

Output:

[10, 5]

Explanation: This DataFrame contains 10 rows and 5 columns.

Constraints

0 <= students.length <= 1000
0 <= students.columns <= 1000

Approach 1: Using Built-in Dimensions

Intuition The size of a DataFrame is simply the product of the number of rows and the number of columns. Most data structure libraries provide direct access to these dimensions.

Steps

• Retrieve the number of rows.
• Retrieve the number of columns.
• Return the product of rows and columns.

import pandas as pd

def getDataFrameSize(students: pd.DataFrame) -> int:
    return students.shape[0] * students.shape[1]

Complexity

• Time: O(1)
• Space: O(1)
• Notes: Accessing dimensions is a constant time operation.

Approach 2: Iterative Counting

Intuition If direct dimension properties are not available, we can iterate through every cell in the DataFrame and count them manually.

Steps

• Initialize a counter to 0.
• Iterate through each row.
• For each row, iterate through each column.
• Increment the counter for each cell.
• Return the counter.

import pandas as pd

def getDataFrameSize(students: pd.DataFrame) -> int:
    count = 0
    for i in range(len(students)):
        for col in students.columns:
            count += 1
    return count

Complexity

• Time: O(n * m) where n is rows and m is columns.
• Space: O(1)
• Notes: This is inefficient compared to direct property access but demonstrates the underlying logic.