Python for CBSE Class 10 AI: the eight programs the board suggests, and the file they build.

a = [12, 7, 30, 5]
b = [8, 3, 10, 15]

total = []
for i in range(len(a)):
    total.append(a[i] + b[i])

print("Sum of lists:", total)

# Output: Sum of lists: [20, 10, 40, 20]
# One-line version students should also know:
total = [x + y for x, y in zip(a, b)]

The loop version shows the examiner you understand indexing; the zip version shows you understand Python. A good file has the first with the second as a comment.

import numpy as np
from scipy import stats

marks = np.array([72, 65, 88, 91, 65, 70, 88, 65])

print("Mean:", np.mean(marks))
print("Median:", np.median(marks))
print("Mode:", stats.mode(marks, keepdims=False).mode)

# Output: Mean: 75.5 Median: 71.0 Mode: 65
# Trap: NumPy has no mode function.
# SciPy stats (or statistics.mode) supplies it.

The single most common error in Class 10 files is np.mode, which does not exist. Knowing why, and saying so in the viva, is worth more than the program.

import matplotlib.pyplot as plt

x = [2, 9]
y = [5, 10]

plt.plot(x, y, marker="o")
plt.title("Line chart from (2,5) to (9,10)")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

# In Jupyter the chart appears under the cell.

Title and axis labels are not decoration: a chart without them is the first thing a practical examiner marks down, and adding them is a two-second habit.

import matplotlib.pyplot as plt

x = [2, 9, 8, 5, 6]
y = [5, 10, 3, 7, 18]

plt.scatter(x, y)
plt.title("Scatter chart of five points")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

# Points: (2,5) (9,10) (8,3) (5,7) (6,18)

Students often swap plot for scatter and lose the mark for the wrong chart type. The exam twist here is usually a sixth point or a colour, both one-line changes for a student who reads the code.

import pandas as pd

df = pd.read_csv("students.csv")

print(df.head(10))

# head(10) shows the first ten rows.
# Exam twist: "display the last 5 rows"
# becomes df.tail(5). Same idea, one word.

The file must exist beside the notebook, and the student must know that. Practical exams are lost to a wrong filename more often than to wrong Python.

import pandas as pd

df = pd.read_csv("students.csv")

df.info()

# Columns, non-null counts, data types.
print(df.describe())
# Count, mean, min, max per numeric column.

Info and describe answer different questions, and the viva loves asking which is which. Knowing that info shows types and describe shows statistics is a guaranteed mark.

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread("photo.jpg")

plt.imshow(img)
plt.axis("off")
plt.show()

# The image appears under the cell in Jupyter.

This is why the curriculum names Jupyter: an image displays inline. In IDLE this program opens a window that vanishes on some machines, and the student concludes their code is wrong.

import cv2

img = cv2.imread("photo.jpg")

print("Shape:", img.shape)

# Output like: Shape: (720, 1280, 3)
# height, width, channels: 3 means RGB.
# This is Unit 5 (Computer Vision)
# arriving inside Unit 7.

The shape connects two units: an image is numbers, height by width by channels, exactly as the Computer Vision theory says. A student who explains the 3 has just answered a Unit 5 question in the practical viva.