How do you handle exceptions in Python?

In the first example, we are going to demonstrate how to handle exceptions in Python using a try-except block. Here is the code: 
try:
    x = 10 / 0
except ZeroDivisionError:
    print("Error: Division by zero!")

Explanation: 1. We use the try block to enclose the code that may raise an exception. 2. In this case, we are trying to divide the number 10 by zero, which will raise a ZeroDivisionError . 3. The except block catches this specific exception and allows us to handle it gracefully by printing an error message. 4. By using try-except blocks, we can prevent our program from crashing and provide a more user-friendly error message. In the second example, we are going to illustrate how to handle multiple exceptions in Python using multiple except blocks. Here is the code: 
try:
    x = int("abc")
except ValueError:
    print("Error: Invalid value for conversion to integer!")
except TypeError:
    print("Error: Incorrect data type!")

Explanation: 1. We attempt to convert the string "abc" to an integer, which will raise a ValueError . 2. We have multiple except blocks, each handling a specific type of exception that may occur within the try block. 3. In this case, the ValueError is caught by the first except block, and we print an error message indicating the invalid value. 4. If a different type of exception, such as TypeError , were to occur, it would be caught by the corresponding except block. 5. Using multiple except blocks allows us to handle different types of exceptions separately and provide customized error messages for each scenario.

Comments

Post a Comment

Popular posts from this blog

What are the different types of optimization algorithms used in deep learning?

In the first example, we are going to discuss the Stochastic Gradient Descent (SGD) optimization algorithm in deep learning using Python. # Import necessary libraries import numpy as np from sklearn.datasets import make_classification from sklearn.linear_model import SGDClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # Create a synthetic dataset X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Standardize features by removing the mean and scaling to unit variance scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Initialize the SGDClassifier with appropriate parameters sgd_clf = SGDClassifier(loss='log', max_iter=1000, learning_rate='optimal') # Fit the model on the train...
Read more

What are the different evaluation metrics used in machine learning?

In the first example, we are going to demonstrate how to use accuracy as an evaluation metric in machine learning using Python. Here is the code: from sklearn.metrics import accuracy_score y_true = [0, 1, 1, 0, 1] y_pred = [0, 1, 0, 0, 1] accuracy = accuracy_score(y_true, y_pred) print("Accuracy:", accuracy) Step 1: Import the accuracy_score function from sklearn.metrics. Step 2: Define two lists, y_true and y_pred, containing the true and predicted values. Step 3: Calculate the accuracy score by calling the accuracy_score function with y_true and y_pred as arguments. Step 4: Print out the accuracy score. In the second example, we are going to demonstrate how to use precision and recall as evaluation metrics in machine learning using Python. Here is the code: from sklearn.metrics import precision_score, recall_score y_true = [0, 1, 1, 0, 1] y_pred = [0, 1, 0, 0, 1] precision = precision_score(y_true, y_pred) recall = recall_score(y_true, y_pred) print("Precis...
Read more

What is the difference between a module and a package in Python?

In the first example, we are going to explore the concept of a module in Python. A module is a file containing Python code that can define functions, classes, and variables. It allows you to organize your code into reusable components. Here is an example code snippet demonstrating the use of a module in Python: # Create a module named mymodule.py # Define a function inside the module def greet(name): return "Hello, " + name In the code above, we have created a module named mymodule.py that defines a function called greet . This function takes a name as an argument and returns a greeting message. To use this module in another Python script, we can import it using the import keyword. import mymodule message = mymodule.greet("Alice") print(message) When we run the above code, it will import the mymodule module and use the greet function to print a greeting message for the name "Alice". In the second example, we are going to discuss the ...
Read more