import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("Baselinedata_Grade VIII E..csv",skiprows=1)  # replace with your correct filename

students_list = df[["Name", "Attendance", "Topic"]]

for index, row in students_list.iterrows():
    print(f"{row['Name']} – Attendance: {row['Attendance']} – Topic: {row['Topic']}")

nan – Attendance: nan – Topic: nan
Ashish Wakley – Attendance: True – Topic: Climate Change
Dorji Tshewang – Attendance: True – Topic: Climate Change
Jigme Namgyel – Attendance: True – Topic: Climate Change
Kelden Drukda – Attendance: True – Topic: Climate Change
Kinga Tobgay – Attendance: True – Topic: Climate Change
Kinley Dendup – Attendance: True – Topic: Climate Change
Kinley Tshering – Attendance: True – Topic: Climate Change
Lekden Thujee Drakpa – Attendance: True – Topic: Climate Change
Pema Namgay – Attendance: True – Topic: Climate Change
Rinzin Dorji – Attendance: True – Topic: Climate Change
Samten Nima – Attendance: True – Topic: Climate Change
Sherub Phuntsho – Attendance: True – Topic: Climate Change
Sherub Wangchuk – Attendance: True – Topic: Climate Change
Sonam Rabten – Attendance: True – Topic: Climate Change
Sonam Tobden – Attendance: True – Topic: Climate Change
Tashi Yoezer – Attendance: True – Topic: Climate Change
Thukten Rigtsel Dorji – Attendance: True – Topic: Climate Change
Yonten Yoezer – Attendance: True – Topic: Climate Change
Dechen Pem – Attendance: True – Topic: Climate Change
Dechen Zangmo – Attendance: True – Topic: Climate Change
Dorji Choden – Attendance: True – Topic: Climate Change
Jigme Metho – Attendance: True – Topic: Climate Change
Kamala Sunar – Attendance: True – Topic: Climate Change
Kinzang Lhazin – Attendance: True – Topic: Climate Change
Namgay Choden – Attendance: True – Topic: Climate Change
Pema Dema – Attendance: True – Topic: Climate Change
Pema Wangmo – Attendance: True – Topic: Climate Change
Pema Yangchen – Attendance: True – Topic: Climate Change
Sangay Choden – Attendance: True – Topic: Climate Change
Sonam Choki – Attendance: True – Topic: Climate Change
Tenzin Chokey – Attendance: True – Topic: Climate Change
Thukten Ngawang Choden – Attendance: True – Topic: Climate Change
Tshering Choden S – Attendance: True – Topic: Climate Change
Tshering Choden Z – Attendance: True – Topic: Climate Change
Tshering Yangzom – Attendance: True – Topic: Climate Change
Ugyen Dema – Attendance: True – Topic: Climate Change
Ugyen lhaden – Attendance: True – Topic: Climate Change
Ugyen Yangzom – Attendance: True – Topic: Climate Change
Yeshi lham – Attendance: True – Topic: Climate Change

columns_to_plot = ["Exploration", "Communication", "Creativity"]

for col in columns_to_plot:
    plt.figure(figsize=(7,7))  # Pie charts are usually square
    counts = df[col].value_counts()
    plt.pie(counts.values, labels=counts.index, autopct='%1.1f%%', startangle=90)
    plt.title(f"Student Ratings in {col}")
    plt.show()

melted = df[["Exploration", "Communication", "Creativity"]].melt()

overall_counts = melted["value"].value_counts()

plt.figure(figsize=(7,4))
plt.bar(overall_counts.index, overall_counts.values)
plt.xlabel("Overall Category")
plt.ylabel("Number of Students")
plt.title("Overall Performance Summary")
plt.xticks(rotation=20)
plt.show()

Week 1: Graphical Representation¶

Code Set Up¶

List of Students, Attendance and Topic¶

Student's performance in Exploration, Communication and Creativity¶

Overall Performance¶