Problem Description

This is a basic DataFrame filtering problem where you need to select specific columns for a particular row based on a condition.

Given a DataFrame students with three columns:

• student_id (integer): unique identifier for each student
• name (string/object): student's name
• age (integer): student's age

The task is to find the student whose student_id equals 101 and return only their name and age columns (excluding the student_id column from the output).

The solution uses pandas DataFrame filtering in two steps:

1. Filter rows: students[students['student_id'] == 101] - This creates a boolean mask where only the row with student_id = 101 is selected
2. Select columns: [['name', 'age']] - This extracts only the name and age columns from the filtered result

In the example, the student with student_id = 101 is Ulysses who is 13 years old, so the output DataFrame contains one row with just these two values.

Intuition

When we need to extract specific information from a DataFrame, we think about it like filtering a spreadsheet. We have two requirements here: finding a specific row (where student_id = 101) and showing only certain columns (name and age).

The natural approach in pandas is to use boolean indexing. When we write students['student_id'] == 101, pandas creates a Series of True/False values - True where the condition matches and False elsewhere. This acts like a filter that keeps only the rows we want.

Once we have the right row, we need to display only the requested columns. In pandas, we can specify which columns to keep by passing a list of column names using double brackets [['name', 'age']]. The double brackets ensure we get a DataFrame back rather than a Series.

We combine these two operations in a single line: first filter for the row we want, then select the columns we need. This chaining approach is efficient because pandas can optimize the operation internally, and it reads naturally from left to right - "from the students DataFrame, take the row where student_id is 101, then show only the name and age columns."

This pattern of filtering rows based on conditions and selecting specific columns is fundamental in data manipulation and forms the basis for more complex queries.

Solution Approach

The implementation uses pandas DataFrame operations to filter and select data in a single expression:

def selectData(students: pd.DataFrame) -> pd.DataFrame:
    return students[students['student_id'] == 101][['name', 'age']]

Let's break down the solution step by step:

1. Boolean Indexing for Row Filtering: students['student_id'] == 101

   • This creates a boolean Series where each element is True if the corresponding student_id equals 101, and False otherwise
   • For the example data, this produces: [True, False, False, False]

2. Row Selection: students[...]

   • When we pass the boolean Series to the DataFrame using square brackets, pandas returns only the rows where the value is True
   • This gives us the complete row for the student with student_id = 101

3. Column Selection: [['name', 'age']]

   • After getting the filtered row(s), we use double square brackets with a list of column names to select specific columns
   • The double brackets [[...]] ensure the result is a DataFrame rather than a Series (which would happen with single brackets)
   • This extracts only the name and age columns, excluding student_id

The entire operation is chained together, executing from left to right. Pandas internally optimizes this combined operation, making it more efficient than performing the filtering and column selection in separate steps.

The result is a new DataFrame containing only the name and age columns for the student whose student_id is 101.

Example Walkthrough

Let's walk through the solution with a concrete example. Suppose we have the following DataFrame:

   student_id    name  age
0         101  Ulysses   13
1         53   Ximena   11
2         128    Yann    9
3         3      Hope   15

We need to find the student with student_id = 101 and return only their name and age.

Step 1: Create the boolean mask

When we evaluate students['student_id'] == 101, pandas compares each value in the student_id column with 101:

• Row 0: 101 == 101 → True
• Row 1: 53 == 101 → False
• Row 2: 128 == 101 → False
• Row 3: 3 == 101 → False

This gives us a boolean Series: [True, False, False, False]

Step 2: Apply the boolean mask to filter rows

Using students[students['student_id'] == 101], pandas keeps only the rows where the mask is True:

   student_id    name  age
0         101  Ulysses   13

At this point, we have the correct row but still have all three columns.

Step 3: Select specific columns

We apply [['name', 'age']] to the filtered result to extract only the desired columns:

      name  age
0  Ulysses   13

The final result is a DataFrame with one row containing only the name and age columns for the student with student_id = 101. The student_id column has been excluded as required.

The complete operation students[students['student_id'] == 101][['name', 'age']] chains these steps together efficiently in a single line.

Solution Implementation

Time and Space Complexity

Time Complexity: O(n) where n is the number of rows in the students DataFrame. The filtering operation students['student_id'] == 101 needs to check each row to determine if the student_id equals 101, resulting in a linear scan through all rows. The column selection [['name', 'age']] is O(1) as it only involves selecting specific columns.

Space Complexity: O(k) where k is the number of rows with student_id equal to 101. In the worst case where all students have student_id = 101, this would be O(n). In the best/average case where only one or a few students match, it would be O(1). The space is needed to store the filtered DataFrame containing only the matching rows with the selected columns 'name' and 'age'.

Common Pitfalls

1. Empty Result Handling

The most common pitfall is not handling cases where no student with student_id = 101 exists in the DataFrame. The current solution will return an empty DataFrame with columns ['name', 'age'], which might cause issues in downstream processing.

Problem Example:

# If student_id 101 doesn't exist
result = selectData(students)  # Returns empty DataFrame
# Attempting to access values might cause errors
value = result.iloc[0]['name']  # IndexError: single positional indexer is out-of-bounds

Solution:

def selectData(students: pd.DataFrame) -> pd.DataFrame:
    result = students[students['student_id'] == 101][['name', 'age']]
    if result.empty:
        # Handle empty case - could return None, raise exception, or return default
        return pd.DataFrame({'name': [None], 'age': [None]})
    return result

2. Single Bracket vs Double Bracket Confusion

Using single brackets for column selection returns a Series instead of a DataFrame, which can break expected functionality.

Problem Example:

# Wrong: Returns a Series if only one column
result = students[students['student_id'] == 101]['name']  # Returns Series, not DataFrame

# Wrong: Returns error if selecting multiple columns with single brackets
result = students[students['student_id'] == 101]['name', 'age']  # KeyError

Solution: Always use double brackets for consistent DataFrame output:

# Correct for single column
result = students[students['student_id'] == 101][['name']]  # Returns DataFrame

# Correct for multiple columns
result = students[students['student_id'] == 101][['name', 'age']]  # Returns DataFrame

3. Chained Assignment Warning

While the one-liner approach works, modifying values using chained indexing can trigger SettingWithCopyWarning.

Problem Example:

# This might trigger a warning if you try to modify the result
result = students[students['student_id'] == 101][['name', 'age']]
result['age'] = result['age'] + 1  # SettingWithCopyWarning

Solution: Use .loc for cleaner, warning-free code:

def selectData(students: pd.DataFrame) -> pd.DataFrame:
    return students.loc[students['student_id'] == 101, ['name', 'age']].copy()
    # The .copy() ensures you're working with a new DataFrame, not a view

4. Type Mismatch in Comparison

If student_id contains mixed types or strings instead of integers, the comparison might fail silently.

Problem Example:

# If student_id is stored as string '101' instead of integer 101
students['student_id'] == 101  # All values will be False

Solution: Ensure type consistency or use flexible comparison:

def selectData(students: pd.DataFrame) -> pd.DataFrame:
    # Convert to consistent type before comparison
    return students[students['student_id'].astype(str) == '101'][['name', 'age']]
    # OR
    return students[students['student_id'] == 101][['name', 'age']]