~/Python Programming

1.1 Features of Python

1.2 Python Editors — Jupyter Notebook

Installation & Running

# Install via pip (in admin mode command prompt)
pip install notebook

# Launch Jupyter Notebook from a terminal
jupyter notebook

1.3 Tokens — Building Blocks of Python

1.4 Operators in Python

1.5 Data Types

Python is a dynamically-typed language — it figures out the type of a variable automatically from the value assigned to it.

1.6 Input Function & Type Casting

The input() function reads text from the user. It always returns a string — so to get numeric input, use a conversion function.

name = input("What is your name? ")     # returns string
age = int(input("Your age? "))           # convert to integer
marks = float(input("Your marks? "))       # convert to float

Sample Program 1 — Display a string

print("National Animal - Tiger")

Sample Program 2 — Area of a rectangle

length = 50
breadth = 20
area = length * breadth
print("Area of rectangle =", area)

Sample Program 3 — Total marks of a student

name = input("Enter name: ")
m1 = float(input("Marks in subject 1: "))
m2 = float(input("Marks in subject 2: "))
m3 = float(input("Marks in subject 3: "))
total = m1 + m2 + m3
print(name, "scored total =", total)

1.7 Control Flow Statements

By default, Python runs statements top-to-bottom. To selectively run or repeat code, use control flow statements:

Selection — if / else

if condition:
    # runs if condition is True
elif another_condition:
    # runs if second condition is True
else:
    # runs if all above are False

Sample Program 4 — Restaurant menu selector

choice = input("Veg / Non-veg? ")
if choice == "Veg":
    print("Menu - Veg only")
elif choice == "Non-veg":
    print("Menu - Veg & Non-veg")
else:
    print("Menu - All options available")

Sample Program 5 — Triangle type

a = float(input("Side 1: "))
b = float(input("Side 2: "))
c = float(input("Side 3: "))

if a == b == c:
    print("Equilateral triangle")
elif a == b or b == c or a == c:
    print("Isosceles triangle")
else:
    print("Scalene triangle")

For Loop — Syntax

for variable in sequence:
    # body — runs once per item

range(5) generates 0, 1, 2, 3, 4 (five values starting from 0).

for i in range(5):
    print("Python")

Sample Program 6 — Even numbers 100-110 & their squares

for i in range(100, 111):
    if i % 2 == 0:
        print(i, "square =", i**2)

Sample Program 7 — Display each list element with its type

lst = [25, 15.6, "car", "XY", 100]
for item in lst:
    print(item, type(item))

Sample Program 8 — Split a string into words

s = input("Enter a sentence: ")
words = s.split()
for w in words:
    print(w)

Sample Program 9 — Display dictionary values

d = {1: "One", 2: "Two", 3: "Three"}
for k in d:
    print(k, "→", d[k])

2.1 CSV Files (Comma-Separated Values)

Common CSV Operations

Sample Program 10 — Read a CSV file

import csv
file = open("students.csv", "r")
details = csv.reader(file)
for rec in details:
    print(rec)
file.close()

2.2 Introducing Python Libraries

A library is a collection of ready-made, reusable functions organised into modules. Libraries are imported with the import statement. For example, the math library contains sqrt(), pow(), abs(), sin().

2.3 NumPy (Numerical Python)

Why Use NumPy in AI?

Suppose you have exam scores for students in multiple subjects. NumPy arrays let you efficiently:

• Calculate average scores per subject.
• Find total scores per student.
• Compute the overall average across all subjects.
• Identify the highest and lowest scores.

Installation

pip install numpy

Creating NumPy Arrays

import numpy as np

# From a list of tuples
arr1 = np.array([(1, 2, 3), (4, 5, 6)])
print(arr1)

# Empty array, then fill with user input
arr2 = np.empty(5)
for i in range(5):
    arr2[i] = float(input("Enter value: "))

2.4 Pandas (Panel Data / Python Data Analysis)

Installation

pip install pandas

Series — 1-D Labelled Array

A Series is a one-dimensional array where each value has a label called an index. By default the index is 0, 1, 2, 3… Think of a Series as one column in a spreadsheet.

import pandas as pd

s = pd.Series([25, 30, 35, 40])
print(s)

DataFrame — 2-D Labelled Table

A DataFrame is a 2-D table (rows + columns) where each column can have a different data type. Think of it as a MySQL table or a spreadsheet sheet.

import pandas as pd
import numpy as np

# Method 1 — From NumPy ndarray
arr = np.array([[80, 75], [90, 85]])
df1 = pd.DataFrame(arr, columns=["Maths", "Science"])

# Method 2 — From list of dictionaries
data = [{"Name": "Amit", "Marks": 75},
        {"Name": "Ashu", "Marks": 95}]
df2 = pd.DataFrame(data)

Common DataFrame Operations

Handling Missing Values (NaN)

DataFrame Attributes

Import / Export CSV with Pandas

import pandas as pd

# Import a CSV into a DataFrame
df = pd.read_csv("students.csv")

# Export a DataFrame to CSV
df.to_csv("output.csv", sep=",")

2.5 Scikit-learn (sklearn) — Machine Learning in Python

Key Features

• Wide range of supervised and unsupervised algorithms.
• Tools for model selection, evaluation, validation.
• Supports classification, regression, clustering, dimensionality reduction.
• Integrates seamlessly with NumPy, SciPy, Pandas.

Installation

pip install scikit-learn

2.6 End-to-End ML Pipeline — The Iris Example

The Iris dataset is a classic ML dataset with 150 iris flower samples across 3 species (setosa, versicolor, virginica). For each flower: sepal length, sepal width, petal length, petal width.

Step 1 — Load the Dataset

from sklearn.datasets import load_iris
iris = load_iris()

X = iris.data       # feature vectors (input)
y = iris.target     # target variable (species labels)

print(X[:10])       # first 10 rows

A sample row [5.1, 3.5, 1.4, 0.2] means sepal length = 5.1 cm, sepal width = 3.5 cm, petal length = 1.4 cm, petal width = 0.2 cm.

Step 2 — Split Train / Test Sets (80 : 20)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=1)

Step 3 — Train the K-Nearest-Neighbours Classifier

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

n_neighbors=3 means the classifier uses the 3 closest training points to predict each new sample.

Step 4 — Predict and Evaluate

y_pred = knn.predict(X_test)

from sklearn import metrics
accuracy = metrics.accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Step 5 — Predict on New Sample Data

sample = [[5, 5, 3, 2], [2, 4, 3, 5]]
preds = knn.predict(sample)

pred_species = []
for p in preds:
    pred_species.append(iris.target_names[p])

print("Predictions:", pred_species)

3.1 Practice Programs from Syllabus

The CBSE syllabus requires at least 5 programs on Level 1 (operators, data types, control statements) and 5 programs on Level 2 (NumPy, Pandas, Scikit-learn).

Level 1 — Program 1: Tipper (15 % & 20 %)

bill = float(input("Total restaurant bill: "))
tip15 = bill * 0.15
tip20 = bill * 0.20
print("15% tip =", tip15)
print("20% tip =", tip20)

Level 1 — Program 2: Driving-licence eligibility

age = int(input("Enter your age: "))
if age >= 18:
    print("Eligible for driving licence")
else:
    print("Not eligible - wait", 18-age, "more years")

Level 1 — Program 3: Car-service check

km = float(input("Kilometre reading: "))
if km >= 15000:
    print("Car needs service")
else:
    print("Service not needed yet")

Level 1 — Program 4: First 10 even natural numbers (for loop)

for i in range(2, 21, 2):
    print(i)

Level 1 — Program 5: Salary computation

basic = float(input("Basic salary: "))
HRA = 0.30 * basic
DA = 0.20 * basic
PF = 0.12 * basic
net = basic + HRA + DA - PF
print("Net Salary =", net)

Level 2 — Program 1: NumPy Series from array

import numpy as np
import pandas as pd

arr = np.array([10, 20, 30, 40, 50])
s = pd.Series(arr)
print(s)

Level 2 — Program 2 to 5: admission.csv analysis

Based on a CSV with columns Name, CLASS, Gender, Marks:

import pandas as pd

# (a) Create DataFrame from CSV
df = pd.read_csv("admission.csv")

# (b) Display first 3 rows
print(df.head(3))

# (c) Display Ravi's details
print(df[df["Name"] == "Ravi"])

# (d) Total rows and columns
print(df.shape)

# (e) Display Gender column
print(df["Gender"])

3.2 Practical Certification

Useful Python Learning Links