#This code utilizes pandas which is a way for you as a user, to create data tables that are much more organized

#imports pandas so it's able to be used
import pandas as pd

#data is created and will be sorted from left to right into top to bottom
data = {'Name': ['Matthew', 'Lindsey', 'Josh', 'Ethan'],
        'Grade': [97, 92, 90, 80]}

#defines a variable and utilizes pandas by using a DataFrame for the data
df = pd.DataFrame(data)

#increase the side count by one
df.index += 1

print(df)

      Name  Grade
  Alice     85
    Bob     92
Charlie     78

#imports numpy and defines it as np for ease
import numpy as np

#creates a list of values similar to python lists
grades = np.array([85, 92, 78, 90, 88])

# Calculates the mean grade using np.mean
mean_grade = np.mean(grades)

print("Mean Grade:", mean_grade)

Mean Grade: 86.6

Homework Hack 1

Create a code that makes a data table which organizes the average values(mean) from a data set the has atleast 5 values per category ex:

import pandas as pd
import numpy as np

# Sample data
data = {
    'Grade': ['A', 'B', 'A', 'C', 'B', 'C', 'A', 'B', 'A'],
    'Percent': [94, 82, 91, 76, 89, 79, 92, 87, 99]
}

# Create a Pandas DataFrame
df = pd.DataFrame(data)

# Calculate the mean of each Grade using NumPy
means = df.groupby('Grade')['Percent'].mean().reset_index()

# Organize the results into a new data table
result = pd.DataFrame({'Grade': ['A', 'B', 'C'], 'Mean Grade': means['Percent']})

# Display the result
print(result)

  Grade  Mean Grade
   A        94.0
   B        86.0
   C        77.5