Grades Analysis with NumPy This project provides a simple Python script that demonstrates how to analyze a list of grades using the NumPy library. The script calculates various statistics, sorts the grades, and filters high-performing students based on specific criteria.

Prerequisites To run this code, you need Python installed with NumPy. If you haven't installed NumPy, you can install it via pip:

pip install numpy

Code Overview The following steps are included in this script:

Importing NumPy: Import the numpy library to work with arrays and perform calculations. Creating the Grades Array: Define a list of grades, then convert it into a NumPy array. Calculating Statistics: Calculate the mean, median, and standard deviation of the grades. Sorting and Finding Minimum/Maximum Grades: Sort the grades in ascending order and find the minimum and maximum grades. Finding the Index of the Highest Grade: Find the index of the highest grade in the array. Counting Grades Above 90: Count the number of students who scored above 90. Calculating the Percentage of Students Above 90: Calculate the percentage of students who scored above 90. Filtering High Performers: Create a new array called high_performers containing only grades above 90. Creating Passing Grades Array: Create another array passing_grades that contains all grades above 75.

Running the Script

Save the script as grades_analysis.py. Run the script in the terminal or any Python IDE:

Output Running the script will output the following information:

The original grades array Mean, median, and standard deviation of the grades Maximum and minimum grades Sorted grades Index of the highest grade Count and percentage of grades above 90 Lists of high performers and passing grades