~/Advance Python

Program 1: Add Elements of Two Lists

list1 = [10, 20, 30, 40, 50]
list2 = [1, 2, 3, 4, 5]

# Add element-wise using list comprehension
result = [list1[i] + list2[i] for i in range(len(list1))]

print("List 1:", list1)
print("List 2:", list2)
print("Sum of elements:", result)

import numpy as np

list1 = np.array([10, 20, 30, 40, 50])
list2 = np.array([1, 2, 3, 4, 5])
result = list1 + list2

print("Sum:", result)

Program 2: Calculate Mean, Median and Mode using NumPy

import numpy as np
from scipy import stats

data = np.array([5, 2, 9, 4, 7, 2, 8, 2, 3])

mean_value   = np.mean(data)
median_value = np.median(data)
mode_value   = stats.mode(data, keepdims=True).mode[0]

print("Data :", data)
print("Mean  :", mean_value)
print("Median:", median_value)
print("Mode  :", mode_value)

Program 3: Display a Line Chart

import matplotlib.pyplot as plt

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

plt.plot(x, y, marker='o', color='blue', linewidth=2)
plt.title("Line Chart from (2,5) to (9,10)")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.grid(True)
plt.show()

Program 4: Display Scatter Chart for (2,5), (9,10), (8,3), (5,7), (6,18)

import matplotlib.pyplot as plt

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

plt.scatter(x, y, color='red', s=100, marker='o')
plt.title("Scatter Chart")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.grid(True)
plt.show()

Program 5: Read CSV File and Display First 10 Rows

import pandas as pd

# Read CSV file
df = pd.read_csv('data.csv')

# Display first 10 rows
print("First 10 rows of the dataset:")
print(df.head(10))

Program 6: Read CSV File and Display Information

import pandas as pd

df = pd.read_csv('data.csv')

print("Shape of dataset:", df.shape)
print("\nColumn names:", df.columns.tolist())
print("\nData types:")
print(df.dtypes)
print("\nStatistical summary:")
print(df.describe())
print("\nFull info:")
df.info()

Program 7: Read and Display an Image using Python

import cv2

# Read the image
img = cv2.imread('photo.jpg')

# Display the image in a window
cv2.imshow('My Image', img)

# Wait for any key press
cv2.waitKey(0)

# Close the window
cv2.destroyAllWindows()

import cv2
import matplotlib.pyplot as plt

img = cv2.imread('photo.jpg')
# OpenCV reads in BGR, convert to RGB for correct colours
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

plt.imshow(img_rgb)
plt.axis('off')
plt.show()

Program 8: Read an Image and Identify its Shape

import cv2

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

print("Shape of image:", img.shape)
print("Height (rows)  :", img.shape[0])
print("Width  (cols)  :", img.shape[1])
print("Number of Channels :", img.shape[2])
print("Total pixels        :", img.shape[0] * img.shape[1])
print("Image data type     :", img.dtype)

Example: Read CSV → Analyse → Visualise

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 1. Read data (Pandas)
df = pd.read_csv('students.csv')
print("Dataset Shape:", df.shape)

# 2. Calculate statistics (NumPy)
avg_marks = np.mean(df['Marks'])
print("Average marks:", avg_marks)

# 3. Visualise (Matplotlib)
plt.bar(df['Name'], df['Marks'], color='skyblue')
plt.title("Student Marks")
plt.xlabel("Student Name")
plt.ylabel("Marks")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()