Program to calculate the sum of first 10 natural numbers using a while loop in Python:

 # While loop in Python - Sum of first 10 natural numbers

num = 1

sum = 0

while num <= 10:

    sum += num

    num += 1

print("The sum of first 10 natural numbers is:", sum)


Comments

Post a Comment

Popular posts from this blog

Simple Python Program Code for Snake Game

 import pygame import time import random   pygame.init()   white = (255, 255, 255) yellow = (255, 255, 102) black = (0, 0, 0) red = (213, 50, 80) green = (0, 255, 0) blue = (50, 153, 213)   dis_width = 600 dis_height = 400   dis = pygame.display.set_mode((dis_width, dis_height)) pygame.display.set_caption('Snake Game by @ChatGPT')   clock = pygame.time.Clock()   snake_block = 10 snake_speed = 15   font_style = pygame.font.SysFont(None, 30)     def message(msg, color):     mesg = font_style.render(msg, True, color)     dis.blit(mesg, [dis_width / 6, dis_height / 3])     def gameLoop():       game_over = False     game_close = False       x1 = dis_width / 2     y1 = dis_height / 2       x1_change = 0            y1_change = 0       foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) ...
Read more

Iris Flowers Classification ML Project with Code

In this code, we first load the iris dataset using the load_iris() function from scikit-learn. We then split the data into training and testing sets using the train_test_split() function. Next, we scale the features using the StandardScaler() function from scikit-learn. We then train the KNN model with n_neighbors = 3 using the KNeighborsClassifier() function. After training the model, we predict the species of the iris flowers in the testing set using the predict() function. Finally, we evaluate the performance of the model using the accuracy_score() , precision_score() , recall_score() , and f1_score() functions from scikit-learn. Note that the values of the metrics may vary slightly depending on the random seed used for splitting the dataset.   # Import necessary libraries from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler from sklea...
Read more

Data exploration and scatter plot visualization in Python using Pandas and Matplotlib

 import pandas as pd import matplotlib.pyplot as plt # Load dataset data = pd.read_csv("dataset.csv") # Print the first 5 rows of the dataset print(data.head()) # Print summary statistics of the dataset print(data.describe()) # Create a scatter plot of two variables plt.scatter(data['Variable1'], data['Variable2']) plt.xlabel('Variable 1') plt.ylabel('Variable 2') plt.title('Scatter plot of two variables') plt.show() In this program, we first import the necessary libraries, including pandas for data manipulation and matplotlib for data visualization. We then load a dataset from a CSV file using the pd.read_csv() function from pandas . Next, we print the first 5 rows of the dataset using the head() method to get a quick glimpse of what the data looks like. We also print some summary statistics of the dataset using the describe() method to get an idea of the data's distribution. Finally, we create a scatter plot of two variables ...
Read more