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This repository was archived by the owner on Jun 29, 2024. It is now read-only.

Commit 9c656a1

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2 parents 170b7a3 + a91d3ec commit 9c656a1

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‎Calc.py

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#a simple python program to perform basic tasks like addition,subtraction,multiplication,division
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print('please select any of the number for performing arithmetic operations')
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print("1.Addition")
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print('2.Subtraction')
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print('3.Multiplication')
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print('4.Division')
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print('5.exit')
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a=int(input('Enter any of the number for performing arithmetic operations'))
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def ari(a,var1,var2):
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a,var1,var2=a,var1,var2
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if(a==1):
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print(var1+var2)
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if(a==2):
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print(var1-var2)
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if(a==3):
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print(var1*var2)
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if(a==4):
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print(var1/var2)
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return
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#Enter Two numbers
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if((a>0) and (a<5)):
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var1 = int(input('Enter First number: '))
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var2 = int(input('Enter Second number: '))
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ari(a,var1,var2)
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elif(a==5):
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exit()
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else:
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print('Invalid Option')
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print('please select 1/2/3/4/5 only')

‎TASK-10.py

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import seaborn as sns
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import pandas as pd
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import matplotlib.pyplot as plt
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# Load the Iris dataset from Seaborn
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iris = sns.load_dataset("iris")
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numeric_iris = iris.drop(columns='species')
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# Display the first few rows of the dataset
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print("First few rows of the dataset:")
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print(iris.head())
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# Summary statistics
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print("\nSummary statistics:")
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print(iris.describe())
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# Checking for missing values
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print("\nMissing values:")
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print(iris.isnull().sum())
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# Visualizations
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# Pairplot
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sns.pairplot(iris, hue="species")
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plt.title("Pairplot of Iris Dataset")
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plt.show()
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# Boxplot
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plt.figure(figsize=(10, 6))
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sns.boxplot(data=iris, orient="h")
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plt.title("Boxplot of Iris Dataset")
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plt.show()
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# Histograms
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plt.figure(figsize=(10, 6))
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iris.hist()
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plt.suptitle("Histograms of Iris Dataset")
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plt.show()
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# Correlation heatmap
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plt.figure(figsize=(8, 6))
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sns.heatmap(numeric_iris.corr(), annot=True, cmap="coolwarm")
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plt.title("Correlation Heatmap of Iris Dataset")
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plt.show()

‎TASK-11.py

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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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# Fetch the Boston housing dataset from the original source
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data_url = "http://lib.stat.cmu.edu/datasets/boston"
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raw_df = pd.read_csv(data_url, sep=r"\s+", skiprows=22, header=None)
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data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
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target = raw_df.values[1::2, 2]
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# Split the dataset into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
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# Create and train the linear regression model
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Make predictions on the training and testing sets
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y_train_pred = model.predict(X_train)
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y_test_pred = model.predict(X_test)
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# Calculate the mean squared error for training and testing sets
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train_mse = mean_squared_error(y_train, y_train_pred)
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test_mse = mean_squared_error(y_test, y_test_pred)
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print("Train MSE:", train_mse)
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print("Test MSE:", test_mse)
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# Plot residuals
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plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
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plt.scatter(y_test_pred, y_test_pred - y_test, c='green', marker='s', label='Test data')
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plt.xlabel('Predicted values')
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plt.ylabel('Residuals')
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plt.legend(loc='upper left')
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plt.hlines(y=0, xmin=min(y_train_pred.min(), y_test_pred.min()), xmax=max(y_train_pred.max(), y_test_pred.max()), color='red')
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plt.title('Residuals plot')
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plt.show()

‎TASK-12.py

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from PIL import Image
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import os
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def get_size_format(b, factor=1024, suffix="B"):
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"""
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Scale bytes to its proper byte format.
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e.g: 1253656 => '1.20MB', 1253656678 => '1.17GB'
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"""
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for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
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if b < factor:
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return f"{b:.2f}{unit}{suffix}"
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b /= factor
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return f"{b:.2f}Y{suffix}"
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def compress_img(image_name, new_size_ratio=0.9, quality=90, width=None, height=None, to_jpg=True):
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try:
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# Load the image into memory
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img = Image.open(image_name)
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# Print the original image shape
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print("[*] Image shape:", img.size)
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# Get the original image size in bytes
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image_size = os.path.getsize(image_name)
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print("[*] Size before compression:", get_size_format(image_size))
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if width and height:
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# If width and height are set, resize with them instead
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img = img.resize((width, height), Image.LANCZOS)
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elif new_size_ratio < 1.0:
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# If resizing ratio is below 1.0, multiply width & height with this ratio to reduce image size
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img = img.resize((int(img.size[0] * new_size_ratio), int(img.size[1] * new_size_ratio)), Image.LANCZOS)
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# Split the filename and extension
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filename, ext = os.path.splitext(image_name)
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# Make a new filename appending "_compressed" to the original file name
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if to_jpg:
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# Change the extension to JPEG
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new_filename = f"{filename}_compressed.jpg"
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# Ensure image is in RGB mode for JPEG
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if img.mode in ("RGBA", "LA"):
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img = img.convert("RGB")
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else:
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# Retain the same extension of the original image
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new_filename = f"{filename}_compressed{ext}"
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# Save the compressed image
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img.save(new_filename, optimize=True, quality=quality)
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# Print the new image shape
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print("[+] New Image shape:", img.size)
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# Get the new image size in bytes
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new_image_size = os.path.getsize(new_filename)
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print("[*] Size after compression:", get_size_format(new_image_size))
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print(f"[*] Compressed image saved as: {new_filename}")
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except FileNotFoundError:
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print("Error: The file was not found.")
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except OSError as e:
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print(f"Error: {e}")
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# Example usage:
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input_image = input("Enter the path to the image: ")
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compress_img(input_image, new_size_ratio=0.8, quality=80, width=800, height=600)

‎TASK-5.py

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import requests
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import datetime
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# Your OpenWeatherMap API key
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API_KEY = '69e7dd8a8069d4066de2a18ea5996e36'
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BASE_URL = 'http://api.openweathermap.org/data/2.5/'
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# Function to get current weather data
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def get_current_weather(city):
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url = f"{BASE_URL}weather?q={city}&appid={API_KEY}&units=metric"
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response = requests.get(url)
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return response.json()
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# Function to get weather forecast data
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def get_forecast(city):
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url = f"{BASE_URL}forecast?q={city}&appid={API_KEY}&units=metric"
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response = requests.get(url)
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return response.json()
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# Function to display weather data
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def display_weather_data(city):
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current_weather = get_current_weather(city)
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forecast = get_forecast(city)
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if current_weather.get("cod") != 200 or forecast.get("cod") != "200":
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print("Failed to retrieve data. Please check the city name or API key.")
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return
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# Current weather
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print(f"\nCurrent weather in {city.capitalize()}:")
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print(f"Temperature: {current_weather['main']['temp']}°C")
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print(f"Weather: {current_weather['weather'][0]['description']}")
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print(f"Humidity: {current_weather['main']['humidity']}%")
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print(f"Wind Speed: {current_weather['wind']['speed']} m/s")
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# Forecast
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print(f"\n5-Day Forecast for {city.capitalize()}:")
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for item in forecast['list']:
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timestamp = item['dt']
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date_time = datetime.datetime.fromtimestamp(timestamp)
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if date_time.hour == 12: # Show data for 12 PM each day
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temp = item['main']['temp']
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weather = item['weather'][0]['description']
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print(f"{date_time.strftime('%Y-%m-%d %H:%M:%S')}: {temp}°C, {weather}")
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# Temperature trends (Average temperatures per day)
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temp_trends = {}
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for item in forecast['list']:
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date = datetime.datetime.fromtimestamp(item['dt']).date()
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if date not in temp_trends:
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temp_trends[date] = []
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temp_trends[date].append(item['main']['temp'])
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print(f"\nTemperature Trends in {city.capitalize()}:")
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for date, temps in temp_trends.items():
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avg_temp = sum(temps) / len(temps)
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print(f"{date}: {avg_temp:.2f}°C")
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# Main function
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def main():
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city = input("Enter the city name: ")
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display_weather_data(city)
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if __name__ == "__main__":
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main()

‎TASK-6.py

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import requests
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from bs4 import BeautifulSoup
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import pandas as pd
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import json
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from urllib.parse import urljoin
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# Function to check robots.txt for scraping permission
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def check_robots_txt(base_url):
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robots_url = urljoin(base_url, '/robots.txt')
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response = requests.get(robots_url)
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if response.status_code == 200:
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robots_txt = response.text
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if "Disallow: /" in robots_txt:
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return False
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return True
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return False
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# Function to list all tables in the HTML
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def list_tables(soup):
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tables = soup.find_all("table")
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if not tables:
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raise Exception("No tables found on the webpage.")
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table_summaries = []
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for i, table in enumerate(tables):
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summary = table.attrs.get("summary", f"Table {i+1}")
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table_summaries.append(summary)
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return tables, table_summaries
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# Function to extract data from the selected table
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def extract_data(table):
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data = []
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headers = [header.text.strip() for header in table.find_all("th")]
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rows = table.find_all("tr")[1:] # Skipping the header row
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for row in rows:
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cells = row.find_all("td")
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row_data = [cell.text.strip() for cell in cells]
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data.append(row_data)
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return headers, data
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# Main function to perform web scraping
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def main():
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# Read the URL from the user
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base_url = input("Enter the URL of the website to scrape: ")
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# Check if scraping is allowed
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if not check_robots_txt(base_url):
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print("It is not possible to perform web scraping on this website.")
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return
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# Send a GET request to fetch the raw HTML content
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response = requests.get(base_url)
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if response.status_code != 200:
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raise Exception(f"Failed to load page {base_url}")
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# Parse the content with BeautifulSoup
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soup = BeautifulSoup(response.content, "html.parser")
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# List all tables
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try:
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tables, table_summaries = list_tables(soup)
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except Exception as e:
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print(f"Error during table listing: {e}")
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return
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# Display the tables to the user and ask for a selection
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print("Tables found on the webpage:")
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for i, summary in enumerate(table_summaries):
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print(f"{i + 1}: {summary}")
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try:
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table_index = int(input("Enter the number of the table you want to scrape: ")) - 1
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if table_index < 0 or table_index >= len(tables):
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raise ValueError("Invalid table number selected.")
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except ValueError as e:
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print(f"Error during table selection: {e}")
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return
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# Extract data from the selected table
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try:
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headers, data = extract_data(tables[table_index])
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except Exception as e:
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print(f"Error during data extraction: {e}")
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return
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# Convert to DataFrame
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df = pd.DataFrame(data, columns=headers)
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# Save to CSV
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df.to_csv("scraped_data.csv", index=False)
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# Save to JSON
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df.to_json("scraped_data.json", orient="records")
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print("Data has been scraped and saved to scraped_data.csv and scraped_data.json")
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if __name__ == "__main__":
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main()

‎TASK-7.py

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import nltk
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from nltk.chat.util import Chat, reflections
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# Define pairs of patterns and responses
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pairs = [
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(r'hi|hello|hey', ['Hello!', 'Hey there!', 'Hi! How can I help you?']),
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(r'how are you?', ['I\'m doing well, thank you!', 'I\'m good, thanks for asking!']),
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(r'what\'s your name\??', ['I\'m a chatbot!', 'You can call me ChatBot.']),
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(r'(.*) your name\??', ['I\'m a chatbot!', 'You can call me ChatBot.']),
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# Add more patterns and responses as needed
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]
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# Create a Chatbot instance
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chatbot = Chat(pairs, reflections)
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print("Welcome! Type 'quit' to end the conversation.")
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# Start the conversation loop
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while True:
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user_input = input("You: ")
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if user_input.lower() == 'quit':
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print("ChatBot: Bye! Have a great day!")
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break
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else:
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response = chatbot.respond(user_input)
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print("ChatBot:", response)

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