What is the time complexity of using head() to get first 3 rows?

O(1) - accessing the first 3 rows of a DataFrame is a constant time operation as it only needs to return a view of the first 3 elements.

What is the space complexity of the slicing approach?

O(1) - slicing creates a view of the original DataFrame rather than copying the data, so no additional space is used proportional to the input size.

Which approach is most recommended for pandas DataFrames?

The head() method is most recommended as it is the most readable and idiomatic pandas approach specifically designed for this purpose.

Display the First Three Rows

Difficulty: Easy | Acceptance: 93.00% | Paid: No Topics: N/A

Examples
Constraints
Approach 1: Using head()
Approach 2: Using Slicing
Approach 3: Using iloc

Examples

Example 1:

Input: DataFrame employees +-------------+--------+------------+--------+ | employee_id | name | department | salary | +-------------+--------+------------+--------+ | 3 | Bob | IT | 72000 | | 90 | Alice | Sales | 90000 | | 9 | Tatiana| Engineering| 88000 | | 60 | Ann | IT | 79000 | +-------------+--------+------------+--------+

Output: +-------------+--------+------------+--------+ | employee_id | name | department | salary | +-------------+--------+------------+--------+ | 3 | Bob | IT | 72000 | | 90 | Alice | Sales | 90000 | | 9 | Tatiana| Engineering| 88000 | +-------------+--------+------------+--------+

Explanation: The first 3 rows of the DataFrame are displayed.

Constraints

The DataFrame may have 0 or more rows.

Approach 1: Using head()

Intuition The pandas head() method is specifically designed to return the first n rows of a DataFrame, making it the most direct and readable solution.

Steps

Call the head(3) method on the DataFrame to get the first 3 rows
Return the result

python

import pandas as pd

def selectFirstRows(employees: pd.DataFrame) -&gt; pd.DataFrame:
    return employees.head(3)

Complexity

Time: O(1) - accessing first 3 rows is constant time
Space: O(1) - only returns a view of the original data
Notes: Most readable and idiomatic pandas approach

Approach 2: Using Slicing

Intuition Python slicing syntax [:3] can be used to select the first 3 rows of a DataFrame, similar to how it works with lists.

Steps

Use slice notation [:3] on the DataFrame
Return the sliced result

python

import pandas as pd

def selectFirstRows(employees: pd.DataFrame) -&gt; pd.DataFrame:
    return employees[:3]

Complexity

Time: O(1) - slicing first 3 elements is constant time
Space: O(1) - creates a view, not a copy
Notes: Concise Python syntax, works well for small selections

Approach 3: Using iloc

Intuition The iloc indexer allows integer-location based indexing, providing explicit row selection by position.

Steps

Use iloc[0:3] to select rows at positions 0, 1, and 2
Return the result

python

import pandas as pd

def selectFirstRows(employees: pd.DataFrame) -&gt; pd.DataFrame:
    return employees.iloc[0:3]

Complexity

Time: O(1) - selecting fixed number of rows
Space: O(1) - returns a view of the data
Notes: More explicit about integer-based indexing, useful when combined with column selection