Problem Description

You are given a DataFrame called employees that contains information about company employees. The DataFrame has the following structure:

• employee_id (int): A unique identifier for each employee
• name (object): The employee's name
• department (object): The department where the employee works
• salary (int): The employee's salary

Your task is to write a function that returns the first 3 rows of this DataFrame.

The solution uses pandas' built-in head() method, which is designed to retrieve the first n rows from a DataFrame. By calling employees.head(3), the function returns a new DataFrame containing only the first 3 rows of the original employees DataFrame, preserving all columns and their values.

This is a fundamental pandas operation commonly used for quickly inspecting the beginning of a dataset or limiting the output to a specific number of top rows.

Intuition

When we need to select a specific number of rows from the beginning of a DataFrame, the most straightforward approach is to use pandas' built-in methods rather than manual indexing or slicing.

The key insight is recognizing that this is a common operation in data analysis - viewing the first few rows of a dataset to understand its structure and content. Pandas provides the head() method specifically for this purpose.

Why head(3) over other approaches like employees[:3] or employees.iloc[:3]? While these alternatives would also work, head() is:

• More readable and self-documenting - it clearly expresses the intent to get the "head" of the data
• Safer when dealing with DataFrames that might have fewer rows than requested (it returns all available rows without throwing an error)
• The conventional pandas way to perform this operation

The solution is elegant in its simplicity - we don't need loops, conditional logic, or complex indexing. We leverage the fact that pandas has already solved this common use case with a dedicated method that handles edge cases gracefully.

Solution Approach

The implementation is straightforward and consists of a single function call:

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:
    return employees.head(3)

Let's break down the implementation:

1. Function Definition: The function selectFirstRows takes one parameter:

   • employees: A pandas DataFrame containing employee data
   • The function returns a pandas DataFrame (the first 3 rows)

2. Core Operation: The solution uses the head() method:

   • employees.head(3) retrieves the first 3 rows from the DataFrame
   • This method creates a new DataFrame containing only these rows
   • All columns (employee_id, name, department, salary) are preserved
   • The original DataFrame remains unchanged

3. Method Behavior:

   • If the DataFrame has 3 or more rows, it returns exactly 3 rows
   • If the DataFrame has fewer than 3 rows (e.g., only 2 rows), it returns all available rows without error
   • If the DataFrame is empty, it returns an empty DataFrame

The beauty of this solution lies in its use of pandas' optimized internal implementation. The head() method efficiently accesses the first n rows without needing to iterate through the entire DataFrame, making it performant even for large datasets.

No additional data structures or complex algorithms are needed - we simply leverage pandas' built-in functionality to achieve the desired result in a single, clean line of code.

Example Walkthrough

Let's walk through a concrete example to understand how the solution works.

Suppose we have the following employees DataFrame:

When we call selectFirstRows(employees), the function executes employees.head(3).

Step-by-step execution:

1. The head(3) method is invoked on the DataFrame
2. Pandas internally accesses the first 3 rows based on the DataFrame's index
3. A new DataFrame is created containing only these rows
4. All column information is preserved

Result:

The function returns this new DataFrame with exactly 3 rows, maintaining the original column structure and data types.

Edge Case Example:

If the DataFrame only had 2 employees:

Calling employees.head(3) would return all 2 available rows without any errors, demonstrating the method's graceful handling of edge cases where fewer rows exist than requested.

Solution Implementation

Time and Space Complexity

Time Complexity: O(1) or O(k) where k = 3

The head(3) operation in pandas retrieves the first 3 rows from the DataFrame. Since we're accessing a fixed number of rows (3), this operation takes constant time regardless of the total size of the DataFrame. The implementation typically involves slicing the underlying data structure, which for a fixed slice size is a constant-time operation.

Space Complexity: O(1) or O(k) where k = 3

The space complexity is also constant because:

• The head(3) method returns a view or a copy of only 3 rows from the original DataFrame
• The amount of memory used for the output is proportional to the number of rows returned (3) and the number of columns, but since we're always returning exactly 3 rows, this is considered constant space
• No additional data structures are created that scale with the input size
• The original DataFrame is not modified, and only a reference to the first 3 rows is returned

Note: While the space used technically depends on the number of columns in the DataFrame, since the column count is not changing and we're only considering row operations, the space complexity with respect to the number of rows in the input is O(1).

Common Pitfalls

1. Assuming the DataFrame Always Has 3 Rows

A common misconception is that head(3) will always return exactly 3 rows. If the DataFrame has fewer than 3 rows, head(3) will return all available rows without raising an error.

Example:

# DataFrame with only 2 rows
df = pd.DataFrame({'employee_id': [1, 2], 'name': ['Alice', 'Bob']})
result = df.head(3)  # Returns 2 rows, not 3

Solution: If you need to ensure exactly 3 rows (padding with NaN if necessary), you could use:

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:
    result = employees.head(3)
    if len(result) < 3:
        # Optionally handle the case where fewer than 3 rows exist
        # Could pad with NaN rows or raise a warning
        pass
    return result

2. Modifying the Returned DataFrame Thinking It's a Copy

While head() returns a new DataFrame object, it creates a view that shares the underlying data with the original DataFrame. Modifying values in the returned DataFrame might trigger a SettingWithCopyWarning.

Example:

first_three = employees.head(3)
first_three['salary'] = 100000  # May trigger SettingWithCopyWarning

Solution: If you need to modify the returned data, explicitly create a copy:

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:
    return employees.head(3).copy()

3. Not Handling Empty DataFrames

When the input DataFrame is empty, head(3) returns an empty DataFrame, which might not be the expected behavior in all contexts.

Example:

empty_df = pd.DataFrame()
result = empty_df.head(3)  # Returns empty DataFrame with no rows or columns

Solution: Add validation if empty DataFrames need special handling:

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:
    if employees.empty:
        # Handle empty DataFrame case
        # Could return a default DataFrame or raise an exception
        return pd.DataFrame(columns=['employee_id', 'name', 'department', 'salary'])
    return employees.head(3)

4. Confusing head() with Index-Based Selection

head(3) returns the first 3 rows based on the DataFrame's current order, not based on index values. If the DataFrame has a non-sequential index, this might cause confusion.

Example:

# DataFrame with custom index
df = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie']}, index=[5, 10, 15])
df.head(2)  # Returns rows with index 5 and 10, not rows with index 0 and 1

Solution: If you need specific index values, use iloc or loc:

def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame:
    # If you need rows at positions 0, 1, 2 regardless of index
    return employees.iloc[:3]