Python Practicals
CBSE · SUB CODE 843 · CLASS 11 & 12 · PRACTICAL FILE
Python Practicals — Basic to Advanced
20 practical programs with explanation, daily life examples and expected output
Level 1 — Basics
Level 2 — Libraries
Level 3 — ML Programs
HOW TO USE THIS PRACTICAL FILE
Each practical has: Aim (what we are doing) → Daily Life Example (why it matters) → Code → Expected Output. Copy the code into Python IDLE or any Python editor and run it!
LEVEL 1 — Python Basics
Programs 1 to 7 · Operators, Data Types, Control Statements
P1
Simple Calculator
AIM:
Write a Python program to perform addition, subtraction, multiplication and division of two numbers.
🛒 DAILY LIFE: Bill Calculator at a Shop
When you buy items at a shop, the cashier adds up prices, applies discount (subtraction), and calculates GST (multiplication). This program does the same!
a = float(input("Enter first number: "))
b = float(input("Enter second number: "))
print(f"Addition: {a} + {b} = {a+b}")
print(f"Subtraction: {a} - {b} = {a-b}")
print(f"Multiplication: {a} x {b} = {a*b}")
if b != 0:
print(f"Division: {a} / {b} = {a/b:.2f}")
else:
print("Division by zero is not allowed!")
EXPECTED OUTPUT:
Enter first number: 10 Enter second number: 4 Addition: 10.0 + 4.0 = 14.0 Subtraction: 10.0 - 4.0 = 6.0 Multiplication: 10.0 x 4.0 = 40.0 Division: 10.0 / 4.0 = 2.50
P2
Grade Calculator
AIM:
Write a Python program to input marks and display the grade using if-elif-else.
📋 DAILY LIFE: School Report Card
Every school gives grades based on marks. This program automates what your teacher does manually — takes marks and gives A, B, C or F grade!
marks = int(input("Enter your marks (0-100): "))
if marks >= 90:
grade = "A+ (Outstanding)"
elif marks >= 75:
grade = "A (Excellent)"
elif marks >= 60:
grade = "B (Good)"
elif marks >= 40:
grade = "C (Average)"
else:
grade = "F (Fail — Please improve!)"
print(f"Marks: {marks}")
print(f"Grade: {grade}")
EXPECTED OUTPUT:
Enter your marks (0-100): 87 Marks: 87 Grade: A (Excellent)
P3
Multiplication Table
AIM:
Write a program to print the multiplication table of any number using a for loop.
n = int(input("Enter a number: "))
print(f"\nMultiplication Table of {n}:")
print("-" * 25)
for i in range(1, 11):
print(f" {n} x {i:2d} = {n*i:3d}")
EXPECTED OUTPUT:
Enter a number: 7 Multiplication Table of 7: ------------------------- 7 x 1 = 7 7 x 2 = 14 ...up to 10
P4
Fibonacci Series
AIM:
Generate Fibonacci series up to n terms. (0,1,1,2,3,5,8,13...)
🌻 DAILY LIFE: Found in Nature!
Fibonacci numbers appear in sunflower seeds, pine cones, and spiral shells. Each number = sum of previous two: 0+1=1, 1+1=2, 1+2=3, 2+3=5...
n = int(input("How many terms? "))
a, b = 0, 1
print("Fibonacci Series:")
for i in range(n):
print(a, end=" ")
a, b = b, a + b
EXPECTED OUTPUT (n=8):
Fibonacci Series: 0 1 1 2 3 5 8 13
P5
Temperature Converter
Convert temperature between Celsius and Fahrenheit. Formula: F = (C × 9/5) + 32
celsius = float(input("Enter temperature in Celsius: "))
fahrenheit = (celsius * 9/5) + 32
kelvin = celsius + 273.15
print(f"\n{celsius}°C = {fahrenheit:.2f}°F")
print(f"{celsius}°C = {kelvin:.2f} K")
Enter temperature in Celsius: 37 37.0°C = 98.60°F 37.0°C = 310.15 K
LEVEL 2 — NumPy, Pandas & Matplotlib
Programs 6 to 13 · Data Analysis and Visualization
P6
Mean, Median, Mode, Std Dev
AIM: Calculate Mean, Median, Mode, Standard Deviation and Variance of a dataset using Python.
🏏 DAILY LIFE: Cricket Player Performance Analysis
Used to analyze a batsman's consistency. Low std deviation = consistent player. High = unpredictable!
import numpy as np
from scipy import stats
# Runs scored in 10 matches
runs = np.array([45, 82, 23, 67, 95, 56, 38, 71, 44, 60])
mean = np.mean(runs)
median = np.median(runs)
mode_res = stats.mode(runs, keepdims=True)
std_dev = np.std(runs)
variance = np.var(runs)
print("=== Cricket Performance Analysis ===")
print(f"Runs: {runs}")
print(f"Mean (Average): {mean:.1f}")
print(f"Median: {median:.1f}")
print(f"Std Deviation: {std_dev:.1f}")
print(f"Variance: {variance:.1f}")
if std_dev < 20:
print("Verdict: Very consistent player!")
else:
print("Verdict: Inconsistent player.")
=== Cricket Performance Analysis === Runs: [45 82 23 67 95 56 38 71 44 60] Mean (Average): 58.1 Median: 58.0 Std Deviation: 21.4 Verdict: Inconsistent player.
P7
Bar Graph — Subject Marks
AIM: Create a bar graph showing marks in different subjects for a student.
import matplotlib.pyplot as plt
subjects = ['AI', 'Maths', 'English', 'Physics', 'Hindi']
marks = [92, 85, 78, 88, 80]
colors = ['#534AB7','#1D9E75','#D85A30','#185FA5','#BA7517']
plt.figure(figsize=(8,5))
bars = plt.bar(subjects, marks, color=colors, width=0.5)
# Add value labels on each bar
for bar, mark in zip(bars, marks):
plt.text(bar.get_x() + bar.get_width()/2,
bar.get_height() + 1, str(mark),
ha='center', fontsize=11, fontweight='bold')
plt.title("Priya's Subject-wise Marks — Class 11", fontsize=14)
plt.xlabel("Subjects")
plt.ylabel("Marks (out of 100)")
plt.ylim(0, 110)
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()
RESULT: A coloured bar graph showing marks for each subject with labels on top of each bar.
P8
Line Graph — Monthly Temperature
AIM: Plot monthly temperature data as a line graph using Matplotlib.
import matplotlib.pyplot as plt
months = ['Jan','Feb','Mar','Apr','May',
'Jun','Jul','Aug','Sep','Oct','Nov','Dec']
temp = [15,18,24,32,38,40,35,34,30,26,20,14]
plt.figure(figsize=(10,5))
plt.plot(months, temp, color='#D85A30',
marker='o', linewidth=2, markersize=7)
plt.fill_between(months, temp, alpha=0.1, color='#D85A30')
plt.title("Monthly Temperature — Hyderabad", fontsize=14)
plt.xlabel("Month")
plt.ylabel("Temperature (°C)")
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()
P9
Pandas — Student DataFrame
AIM: Create a student DataFrame and perform operations like filtering, sorting and calculating statistics.
import pandas as pd
data = {
'Name': ['Aarav','Priya','Rahul','Sneha','Arjun','Meera'],
'AI_Marks': [85, 92, 73, 95, 88, 79],
'Math_Marks':[80, 88, 65, 91, 82, 74],
'Attendance':[92, 98, 75, 99, 88, 83]
}
df = pd.DataFrame(data)
# Add total column
df['Total'] = df['AI_Marks'] + df['Math_Marks']
print("=== Class Report ===")
print(df.to_string(index=False))
print(f"\nClass AI Average: {df['AI_Marks'].mean():.1f}")
print(f"\nTop Student: {df.loc[df['Total'].idxmax(),'Name']}")
# Filter: students with AI marks above 85
print("\nAI Toppers (>85):")
print(df[df['AI_Marks'] > 85][['Name','AI_Marks']])
=== Class Report === Name AI_Marks Math_Marks Attendance Total Aarav 85 80 92 165 Priya 92 88 98 180 ... Top Student: Sneha
LEVEL 3 — Machine Learning Programs
Programs 14 to 20 · Linear Regression, kNN, k-Means, Scatter Plot
P10
Linear Regression — Predict Marks
AIM: Use Linear Regression to predict exam marks based on study hours.
📖 DAILY LIFE: "If I study X hours, what marks will I get?"
A student wants to know — if I study 9 hours, what score can I expect? Linear Regression answers this question using past data!
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
# Training data: hours studied vs marks
hours = np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])
marks = np.array([35, 42, 55, 61, 68, 75, 79, 85, 90, 95])
# Train the model
model = LinearRegression()
model.fit(hours, marks)
# Predict for 7.5 hours
new_hours = [[7.5]]
predicted_marks = model.predict(new_hours)
print(f"If you study 7.5 hours → Predicted marks: {predicted_marks[0]:.1f}")
# Plot the result
plt.scatter(hours, marks, color='blue', label='Actual Data')
plt.plot(hours, model.predict(hours), color='red', label='Regression Line')
plt.title("Study Hours vs Exam Marks")
plt.xlabel("Hours Studied")
plt.ylabel("Marks")
plt.legend()
plt.grid(alpha=0.3)
plt.show()
print(f"Slope (rate per hour): {model.coef_[0]:.2f}")
print(f"R² Score (accuracy): {model.score(hours,marks):.3f}")
If you study 7.5 hours → Predicted marks: 82.3 Slope (rate per hour): 6.42 R² Score (accuracy): 0.991
P11
kNN — Fruit Classifier
AIM: Use k-Nearest Neighbour algorithm to classify fruits based on weight and color score.
🍎 DAILY LIFE: Sorting fruits at a fruit stall
A fruit vendor wants to automatically sort apples (0) and oranges (1) by weight and size. kNN classifies each new fruit by looking at its 3 most similar neighbours!
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
# Training data: [weight(g), diameter(cm)] → 0=Apple, 1=Orange
X_train = np.array([
[182, 8.2], [167, 7.8], [176, 8.0], # Apples
[150, 7.0], [154, 7.2], [161, 7.5], # Oranges
[185, 8.3], [148, 6.9], [178, 8.1], # Mix
[155, 7.3]
])
y_train = np.array([0,0,0, 1,1,1, 0,1,0,1])
# Train kNN with k=3
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
# Test new fruits
test_fruits = np.array([[170, 7.9], [152, 7.1]])
results = knn.predict(test_fruits)
labels = ['Apple', 'Orange']
for i, fruit in enumerate(test_fruits):
print(f"Fruit (weight={fruit[0]}g, dia={fruit[1]}cm) → {labels[results[i]]}")
Fruit (weight=170g, dia=7.9cm) → Apple Fruit (weight=152g, dia=7.1cm) → Orange
P12
k-Means — Customer Grouping
AIM: Use k-Means Clustering to group customers based on age and spending score.
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
# Customer data: [age, spending_score(1-100)]
customers = np.array([
[22,75],[25,85],[23,70],[20,90],[24,80], # Young, high spend
[45,20],[50,30],[48,15],[52,25],[46,22], # Middle-aged, low spend
[35,50],[38,55],[36,48],[40,52],[37,45] # Middle group
])
# Group into 3 customer segments
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
kmeans.fit(customers)
labels = kmeans.labels_
# Plot clusters
colors = ['#534AB7','#1D9E75','#D85A30']
segment_names = ['Young High Spenders',
'Middle-aged Low Spenders',
'Regular Customers']
plt.figure(figsize=(8,5))
for i in range(3):
cluster = customers[labels == i]
plt.scatter(cluster[:,0], cluster[:,1],
color=colors[i], label=segment_names[i], s=80)
centers = kmeans.cluster_centers_
plt.scatter(centers[:,0], centers[:,1],
color='black', marker='X', s=200, label='Centers')
plt.title("Customer Segmentation with k-Means")
plt.xlabel("Age")
plt.ylabel("Spending Score")
plt.legend(fontsize=9)
plt.grid(alpha=0.3)
plt.show()
print("Customer Groups:", labels)
P13
Pearson Correlation Coefficient
AIM: Calculate Pearson Correlation Coefficient between study hours and marks.
🔗 WHAT IS CORRELATION?
Correlation tells us if two things are related. +1 = perfectly related (study more → score more), -1 = opposite related, 0 = no relation.
import numpy as np
from scipy.stats import pearsonr
hours = np.array([2, 3, 5, 6, 7, 8, 9, 10])
marks = np.array([45, 55, 68, 72, 79, 85, 88, 94])
corr, p_value = pearsonr(hours, marks)
print(f"Pearson Correlation: {corr:.4f}")
print(f"P-value: {p_value:.6f}")
if corr > 0.7:
print("Interpretation: Strong Positive Correlation!")
print("Conclusion: More study hours = Better marks ✓")
elif corr > 0.4:
print("Interpretation: Moderate Positive Correlation")
else:
print("Interpretation: Weak or No Correlation")
Pearson Correlation: 0.9956 P-value: 0.000002 Interpretation: Strong Positive Correlation! Conclusion: More study hours = Better marks ✓
QUICK REFERENCE — ALL PROGRAMS SUMMARY
BASIC
P1-P5
Calculator, Grades, Loops, Fibonacci, Converter
NUMPY
P6
Mean, Median, Mode, Std Dev, Variance
MATPLOTLIB
P7-P8
Bar Graph, Line Graph, Pie, Histogram, Scatter
PANDAS
P9
DataFrame, Filter, Sort, CSV Read/Write
ML
P10-P13
Linear Regression, kNN, k-Means, Correlation
AI Logic School · Python Practicals · CBSE Class 11 & 12 · Sub Code 843 · Practical File
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