Student
Mon 30 June 2025
pip install pandas
Collecting pandas
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Installing collected packages: pandas
Successfully installed pandas-2.3.0
Note: you may need to restart the kernel to use updated packages.
pip install numpy
Requirement already satisfied: numpy in c:\users\hp\miniconda3\envs\py312\lib\site-packages (2.3.1)
Note: you may need to restart the kernel to use updated packages.
pip install matplotlib
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Note: you may need to restart the kernel to use updated packages.
# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Define student data
students = [
{"name": "Alice", "math": 88, "science": 92, "english": 85},
{"name": "Bob", "math": 72, "science": 78, "english": 70},
{"name": "Charlie", "math": 90, "science": 95, "english": 93},
{"name": "David", "math": 65, "science": 60, "english": 58},
{"name": "Eva", "math": 100, "science": 98, "english": 97}
]
# Create DataFrame
df = pd.DataFrame(students)
df
| name | math | science | english | |
|---|---|---|---|---|
| 0 | Alice | 88 | 92 | 85 |
| 1 | Bob | 72 | 78 | 70 |
| 2 | Charlie | 90 | 95 | 93 |
| 3 | David | 65 | 60 | 58 |
| 4 | Eva | 100 | 98 | 97 |
# Calculate total and average
df["Total"] = df[["math", "science", "english"]].sum(axis=1)
df["Average"] = df["Total"] / 3
df
| name | math | science | english | Total | Average | |
|---|---|---|---|---|---|---|
| 0 | Alice | 88 | 92 | 85 | 265 | 88.333333 |
| 1 | Bob | 72 | 78 | 70 | 220 | 73.333333 |
| 2 | Charlie | 90 | 95 | 93 | 278 | 92.666667 |
| 3 | David | 65 | 60 | 58 | 183 | 61.000000 |
| 4 | Eva | 100 | 98 | 97 | 295 | 98.333333 |
# Define grade calculator
def grade(avg):
if avg >= 90:
return "A+"
elif avg >= 80:
return "A"
elif avg >= 70:
return "B"
elif avg >= 60:
return "C"
else:
return "F"
# Apply grade
df["Grade"] = df["Average"].apply(grade)
df
| name | math | science | english | Total | Average | Grade | |
|---|---|---|---|---|---|---|---|
| 0 | Alice | 88 | 92 | 85 | 265 | 88.333333 | A |
| 1 | Bob | 72 | 78 | 70 | 220 | 73.333333 | B |
| 2 | Charlie | 90 | 95 | 93 | 278 | 92.666667 | A+ |
| 3 | David | 65 | 60 | 58 | 183 | 61.000000 | C |
| 4 | Eva | 100 | 98 | 97 | 295 | 98.333333 | A+ |
# Find topper
topper = df.loc[df["Total"].idxmax()]
print("Topper:\n", topper)
Topper:
name Eva
math 100
science 98
english 97
Total 295
Average 98.333333
Grade A+
Name: 4, dtype: object
# Count grades
df["Grade"].value_counts()
Grade
A+ 2
A 1
B 1
C 1
Name: count, dtype: int64
# Plot total marks
plt.figure(figsize=(8,5))
plt.bar(df["name"], df["Total"], color="skyblue")
plt.title("Student Total Marks")
plt.xlabel("Name")
plt.ylabel("Total Marks")
plt.show()
# Plot grades pie chart
plt.pie(df["Grade"].value_counts(), labels=df["Grade"].value_counts().index, autopct="%1.1f%%", startangle=90)
plt.title("Grade Distribution")
plt.show()
# Find subject-wise highest scorer
highest_math = df.loc[df["math"].idxmax()]
highest_science = df.loc[df["science"].idxmax()]
highest_english = df.loc[df["english"].idxmax()]
print("Topper in Math:", highest_math["name"])
print("Topper in Science:", highest_science["name"])
print("Topper in English:", highest_english["name"])
Topper in Math: Eva
Topper in Science: Eva
Topper in English: Eva
# Add Pass/Fail column
df["Result"] = df["Grade"].apply(lambda x: "Pass" if x != "F" else "Fail")
df
| name | math | science | english | Total | Average | Grade | Result | |
|---|---|---|---|---|---|---|---|---|
| 0 | Alice | 88 | 92 | 85 | 265 | 88.333333 | A | Pass |
| 1 | Bob | 72 | 78 | 70 | 220 | 73.333333 | B | Pass |
| 2 | Charlie | 90 | 95 | 93 | 278 | 92.666667 | A+ | Pass |
| 3 | David | 65 | 60 | 58 | 183 | 61.000000 | C | Pass |
| 4 | Eva | 100 | 98 | 97 | 295 | 98.333333 | A+ | Pass |
# Count Pass and Fail
df["Result"].value_counts()
Result
Pass 5
Name: count, dtype: int64
# Save to CSV
df.to_csv("student_report.csv", index=False)
Score: 15
Category: basics