Student Report Analyzer Using Python
Overview This project is a simple student report analyzer built using Python along with the NumPy and Pandas libraries. The program organizes student marks in different subjects, calculates their average score, determines whether they pass or fail, assigns grades, and identifies top performing students. The purpose of this project is to practice data analysis concepts that are commonly used in data science.
Objective The main objective of this project is to demonstrate how basic student data can be analyzed programmatically using Python. It shows how tabular data can be created, processed, and analyzed using Pandas DataFrames.
Technologies Used Python NumPy Pandas
Features Stores student names and subject marks in a structured dataset Creates a Pandas DataFrame from the dataset Calculates the average marks of each student across subjects Rounds the calculated averages to two decimal places Determines pass or fail status based on the average score Assigns grades based on defined score ranges Displays students who achieved grade A Sorts students based on average marks and displays the top performers
Grading Logic Students are graded according to their average marks
Average greater than or equal to 75 results in Grade A Average greater than or equal to 60 and less than 75 results in Grade B Average greater than or equal to 40 and less than 60 results in Grade C Average below 40 results in Fail
Project Workflow Student names and marks are stored in Python lists The lists are converted into a dictionary The dictionary is used to create a Pandas DataFrame The program calculates the average marks of each student The result column identifies whether the student passed or failed The grade column assigns grades using conditional logic The program identifies grade A students as top performers The DataFrame is sorted to display the top three students based on average marks
Learning Outcome This project helps in understanding how data analysis workflows operate using Python. It introduces practical usage of Pandas DataFrames, conditional operations using NumPy, data sorting, and simple reporting systems. These concepts form a foundation for larger data science and analytics projects.
Author Uday Bhaskar Aspiring Data Science Learner focusing on Python, NumPy, Pandas, and practical data analysis projects.