Pandas Practice¶

In this lesson, we'll practice working with DataFrames.

In [1]:
import pandas as pd

Our Dataset!¶

Run the cell below to see the DataFrame you'll be working with today.

In [24]:
gradebook = pd.DataFrame({
    "StudentID": [101, 102, 103, 104, 105, 106, 107, 108, 109],
    "Name": ["Arona", "Hannah", "Renusree", "Arpan", "Mia", "Asmi", "Alyssa", "Vani", "Vatsal"],
    "Grade": [60, 92, 78, 90, 88, 73, 50, 99, 75],
    "Attendance": [95, 80, 88, 92, 98, 100, 98, 77, 95],
    "Major": ["CS", "ECE", "CS", "ECE", "CS", "Informatics", "ECE", "Informatics", "Informatics"],
    "GradYear": [2024, 2024, 2026, 2026, 2026, 2024, 2027, 2025, 2025]
})

gradebook
Out[24]:
StudentID Name Grade Attendance Major GradYear
0 101 Arona 60 95 CS 2024
1 102 Hannah 92 80 ECE 2024
2 103 Renusree 78 88 CS 2026
3 104 Arpan 90 92 ECE 2026
4 105 Mia 88 98 CS 2026
5 106 Asmi 73 100 Informatics 2024
6 107 Alyssa 50 98 ECE 2027
7 108 Vani 99 77 Informatics 2025
8 109 Vatsal 75 95 Informatics 2025

Individual Activity¶

Display all the column names in the cell below.

In [1]:
# TODO: Display column names
# .columns will return you the columns of the dataframe

Then, select the rows where Grade is greater than 85 in the cell below.

In [2]:
# TODO: Rows where Grade > 85
# hints: filtering

Group Activity¶

Add a new column called Participation with the values [100, 80, 70, 90, 100, 20, 80, 90, 100]

In [ ]:

Now, calculate the mean of the Grade, Attendance, and Participation column, and store that value under a new column, FinalScore.

In [3]:
# TODO: Calculate mean and create FinalScore
# remember that you need to store the value under a new column, not a new variable name

Next, change the index of the DataFrame to the StudentID and find what Student 105's Major is. What about their GradYear?

In [4]:
# TODO: Who is Student 105?
# Hints: in order to change the index of the dataFrame, we can use .set_index() function
# you can use .loc()
In [ ]:

Whole Class Activity¶

Write a function categorize_student that categorizes students into "High Performer" if their FinalScore is 85 or above, and "Needs Improvement" otherwise. Apply this function to create a new column PerformanceCategory.

In [8]:
def categorize_student(data):
  """
  
  """
...




categorize_student(gradebook)