2880. Select Data
Problem Description
The given problem presents a DataFrame named students
, which represents a simplified table structure as often found in databases. The DataFrame contains three columns: student_id
, name
, and age
. Each row in the DataFrame represents a unique student with their corresponding ID, name, and age. The task is to query this DataFrame and extract information specifically for the student with the ID 101
. The output should be a new DataFrame containing only the name and age of that particular student, discarding other columns and rows that do not match the specified student ID.
Intuition
The intuition behind the solution emerges from understanding how DataFrames work in pandas, a popular data manipulation library in Python. To extract specific information from a DataFrame, the following operations are typically involved:
-
Filtering: We need to filter out the rows that do not meet our criteria, which is in this case the row where the
student_id
equals101
. Filtering in pandas is often done by generating a boolean mask that isTrue
for rows that match the condition andFalse
for those that do not. Applying this mask to the DataFrame yields a new DataFrame comprised only of the rows where the mask isTrue
. -
Selecting Columns: After isolating the row or rows that meet our condition, we need to select only the columns of interest. In this problem, our interest lies in the
name
andage
columns. This is done by specifying a list of the desired column names to the DataFrame after filtering.
By combining these two operations, we obtain the solution to the problem. We apply a filter to keep only the row where student_id
is 101
, and immediately after that, we specify that we want to continue with just the columns name
and age
.
Here's a step-by-step intuition for the provided solution:
students['student_id'] == 101
creates a boolean mask that only evaluates toTrue
for the row wherestudent_id
is101
.students[students['student_id'] == 101]
applies the mask to thestudents
DataFrame, yielding a DataFrame that contains only the row withstudent_id
101
.[['name', 'age']]
is a list of column names indicating our intent to select only these columns from the filtered DataFrame.- Placing
[['name', 'age']]
after the filtered DataFrame completes the operation by returning only the desired columns for the student withstudent_id
101
.
Through these steps, we arrive at the final, succinct solution that performs the required operation effectively using pandas library capabilities.
Solution Approach
The implementation of the solution follows a straightforward approach using pandas, a powerful data manipulation library in Python, which is ideal for working with tabular data structures like DataFrames.
Here's the breakdown of the solution step by step:
-
Filtering Rows: The key operation begins with filtering the DataFrame to select only the row where the
student_id
is 101. This is achieved by using a boolean expressionstudents['student_id'] == 101
. This expression checks eachstudent_id
in the DataFrame against the value101
and returns a Series of boolean values (True
orFalse
). This Series acts as a mask over the DataFrame. -
Applying the Filter: The boolean mask is then applied to the
students
DataFrame. This is done by passing the mask as an indexer to the DataFrame:students[students['student_id'] == 101]
. Pandas filters out any rows for which the mask isFalse
, leaving only the rows where the mask isTrue
, which in this case should be only the row withstudent_id
101 ifstudent_id
is unique. -
Selecting Columns: After we have filtered the DataFrame to isolate the row with the desired
student_id
, we proceed to select only the columns that we want to include in our final output. This is done by passing a list of the desired column names to the DataFrame:[['name', 'age']]
. This list tells pandas to keep only these columns and discard any others. -
Combining Operations: Lastly, the row filtering and column selection operations are combined in a single line of code to produce the final DataFrame. This is the expression
students[students['student_id'] == 101][['name', 'age']]
. It filters the rows and selects the columns in one step, resulting in a DataFrame that contains only thename
andage
of the student withstudent_id
101.
In terms of data structures, the solution operates entirely on the DataFrame
object, which is the main data structure in pandas. DataFrames are designed to mimic SQL table-like functionalities with rows and columns, where each column can have different data types.
The pattern used in this solution is common in pandas for querying and subsetting data. It is analogous to SQL's SELECT ... FROM ... WHERE ...
statements, with the main difference being the syntax and the fact that we're using pandas' methods and indexing capabilities instead of SQL queries.
The provided solution, encapsulated in the selectData
function, showcases the elegant and Pythonic approach to dealing with DataFrame operations and signifies the strength of pandas when it comes to data manipulation tasks.
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Let's consider a small dataset to demonstrate the workings of the solution approach outlined above.
Assume we have the following students
DataFrame:
student_id | name | age |
---|---|---|
100 | Alice | 23 |
101 | Bob | 24 |
102 | Charlie | 22 |
And we want to extract the information specifically for the student with the student_id
101
.
Filtering Rows
The first step is to create a boolean mask that will help us filter the rows:
mask = students['student_id'] == 101
This mask evaluates to:
student_id | name | age | mask |
---|---|---|---|
100 | Alice | 23 | False |
101 | Bob | 24 | True |
102 | Charlie | 22 | False |
Applying the Filter
Next, we apply the mask to the students
DataFrame:
filtered_students = students[mask]
This results in a temporary DataFrame that holds only the row(s) where the mask is True
:
student_id | name | age |
---|---|---|
101 | Bob | 24 |
Selecting Columns
Now we proceed to select only the columns name
and age
:
selected_data = filtered_students[['name', 'age']]
The resulting DataFrame is:
name | age |
---|---|
Bob | 24 |
Combining Operations
We can combine the above steps into a single line of code in order to achieve our desired output:
result = students[students['student_id'] == 101][['name', 'age']]
And result
holds the final DataFrame:
name | age |
---|---|
Bob | 24 |
This is exactly the output we were aiming for: a new DataFrame containing just the name
and age
of the student with student_id
101
. The transformation process filters out other students and discards irrelevant columns, employing pandas' slicing and filtering capabilities to retrieve the needed subset of data efficiently.
Solution Implementation
1import pandas as pd
2
3def select_data(students_df: pd.DataFrame) -> pd.DataFrame:
4 # This function filters the provided DataFrame for a specific student by ID (101)
5 # and returns only the 'name' and 'age' columns.
6
7 # Filter the DataFrame for the student with 'student_id' equal to 101
8 filtered_students = students_df[students_df['student_id'] == 101]
9
10 # Select only the 'name' and 'age' columns
11 selected_columns = filtered_students[['name', 'age']]
12
13 return selected_columns
14
1import java.util.List;
2import java.util.stream.Collectors;
3
4// Define a Student class to represent student data with `studentId`, `name`, and `age` as properties.
5class Student {
6 int studentId;
7 String name;
8 int age;
9
10 // Constructor for Student class
11 public Student(int studentId, String name, int age) {
12 this.studentId = studentId;
13 this.name = name;
14 this.age = age;
15 }
16
17 // Getter for studentId
18 public int getStudentId() {
19 return studentId;
20 }
21
22 // Getter for name
23 public String getName() {
24 return name;
25 }
26
27 // Getter for age
28 public int getAge() {
29 return age;
30 }
31}
32
33// Define a class to represent operations on student data.
34class DataSelector {
35
36 // This method filters a list of students for a specific student by ID (101) and returns their 'name' and 'age'.
37 public List<Student> selectData(List<Student> students) {
38 // Filter the list for the student with 'studentId' equal to 101
39 List<Student> filteredStudents = students.stream()
40 .filter(student -> student.getStudentId() == 101)
41 .collect(Collectors.toList());
42
43 // Return only the 'name' and 'age' columns. In Java, we have to return the whole Student object,
44 // but users of the method should only use the name and age properties if adhering to original intent.
45 return filteredStudents;
46 }
47}
48
1#include <vector>
2#include <string>
3#include <algorithm>
4#include <iostream>
5
6// Assume there's a struct to represent a student
7struct Student {
8 int student_id;
9 std::string name;
10 int age;
11};
12
13// This function filters students by their ID and collects names and ages
14std::vector<std::pair<std::string, int>> select_data(const std::vector<Student>& students) {
15 std::vector<std::pair<std::string, int>> selected_data; // Pair will hold the name and age
16
17 // Iterate through the list of students
18 for (const auto& student : students) {
19 // Check if the student has the ID 101
20 if (student.student_id == 101) {
21 // Add the student's name and age to the selected data
22 selected_data.emplace_back(student.name, student.age);
23 }
24 }
25
26 // Return the filtered and selected data
27 return selected_data;
28}
29
30// Example usage
31int main() {
32 // Create a sample list of students
33 std::vector<Student> students = {
34 {100, "John Doe", 20},
35 {101, "Jane Smith", 21},
36 {102, "Bob Johnson", 22}
37 };
38
39 // Get the data for student with ID 101
40 std::vector<std::pair<std::string, int>> data_for_student = select_data(students);
41
42 // Print the result
43 for (const auto& data : data_for_student) {
44 std::cout << "Name: " << data.first << ", Age: " << data.second << std::endl;
45 }
46
47 return 0;
48}
49
1// Import the necessary library for data handling
2import * as pd from 'pandas';
3
4// This function filters a DataFrame for a specific student by ID (101)
5// and returns a new DataFrame containing only the 'name' and 'age' columns.
6function selectData(studentsDf: pd.DataFrame): pd.DataFrame {
7
8 // Filter the DataFrame for the student with a 'studentId' equal to 101
9 const filteredStudents: pd.DataFrame = studentsDf.filter((row: any) => row['studentId'] === 101);
10
11 // Select only the 'name' and 'age' columns from the filtered DataFrame
12 const selectedColumns: pd.DataFrame = filteredStudents[['name', 'age']];
13
14 // Return the DataFrame containing the selected columns
15 return selectedColumns;
16}
17
Time and Space Complexity
Time Complexity
The selectData
function primarily involves the selection of rows based on a condition (students['student_id'] == 101
) and the selection of specific columns (['name', 'age']
). The time complexity of filtering the DataFrame by a condition is typically O(n), where n is the number of rows in the DataFrame, as each row has to be checked against the condition. Furthermore, selecting specific columns from the DataFrame is an O(1) operation since it is a simple indexing operation that does not require iteration over all rows or columns. Hence, the overall time complexity of the function is O(n)
.
Space Complexity
Regarding space complexity, the function creates a new DataFrame that only contains the filtered rows with the selected columns. The space used will depend on the number of rows that satisfy the condition. However, since we are filtering by a unique student_id
, at most, one row will satisfy the condition. The space complexity of the output will be O(1) as the filtered result is independent of the size of the input DataFrame. Moreover, pandas
may internally optimize memory usage depending on the version and the configuration, but this does not affect the space complexity of the algorithm used in selectData
. Thus, the space complexity of the function is O(1)
.
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