Practical Based on Pandas Data Structures

Pandas Data Structures

Series and DataFrame To Manage and Analyze Data

Student Exam Scores Analysis

import pandas as pd

# Create a Pandas Series for student names

students = pd.Series(['Alice', 'Bob', 'Charlie', 'David', 'Eva'])

# Create a DataFrame for their scores

scores = pd.DataFrame({

    'Math': [85, 92, 78, 90, 88],

    'Science': [88, 84, 91, 89, 93],

    'English': [80, 79, 85, 94, 92]

})

# Add the student names to the DataFrame

scores['Student'] = students

# Set "Student" as the index

scores.set_index('Student', inplace=True)

# Display the DataFrame

print("Student Exam Scores:")

print(scores)

# Calculate the average score for each student

scores['Average'] = scores.mean(axis=1)

print("\nAverage Scores:")

print(scores[['Average']])

# Find the student with the highest average score

top_student = scores['Average'].idxmax()

top_average = scores['Average'].max()

print(f"\nTop student: {top_student} with an average score of {top_average:.2f}")

# Find the subject with the highest average score

subject_avg = scores.mean(axis=0)

top_subject = subject_avg.idxmax()

print(f"\nSubject with the highest average score: {top_subject} ({subject_avg[top_subject]:.2f})")

# Add a column to indicate if a student passed all subjects (Pass if score >= 50)

scores['Passed All'] = scores[['Math', 'Science', 'English']].apply(lambda row: all(row >= 50), axis=1)

print("\nScores with Pass Status:")

print(scores)

# Save the DataFrame to a CSV file

scores.to_csv('student_scores.csv', index=True)

print("\nData saved to 'student_scores.csv'")

Program Highlights

1. Pandas Series: Used for the student names.

2. Pandas DataFrame: Used for managing and analyzing scores for three subjects.

3. Calculations:

• Computes average scores for each student.
• Identifies the student with the highest average.
• Finds the subject with the highest average score.

4. Logical Operation: Adds a "Passed All" column to indicate if a student passed all subjects.

5. Data Export: Saves the DataFrame to a CSV file for future use.

Sample Output

Student Exam Scores:                  

Student   Math  Science  English                      

Alice          85          88              80

Bob            92          84              79

Charlie      78          91              85

David         90          89              94

Eva             88          93              92

Average Scores:                   

Student    Average     

Alice        84.333333

Bob          85.000000

Charlie    84.666667

David       91.000000

Eva          91.000000

Top student: David with an average score of 91.00

Subject with the highest average score: Science (89.00)

Scores with Pass Status:

                    Math  Science  English    Average  Passed All

Student                                                

Alice             85        88           80        84.333333    True

Bob               92        84           79        85.000000    True

Charlie         78        91           85        84.666667    True

David            90       89            94        91.000000    True

Eva                88       93            92        91.000000    True

Data saved to 'student_scores.csv'

Usage

• Pandas Series and DataFrame make it easy to store, manipulate, and analyze tabular data.
• The program handles tasks such as:

                  Computing averages.

                  Sorting and filtering.

                  Adding calculated columns.

• Exporting to a CSV file is useful for real-world applications where data persistence is needed.

This example is suitable for learning and can be extended for more advanced use cases.