Paramiko’s logging can be very helpful for debugging, but it can also be quite verbose and clutter your output when you’re running your code normally. This is how to control and turn off logging in Paramiko.
Solving Unknown Server Error in Paramiko
The “Unknown Server” error in Paramiko typically arises when the SSH client (your Python script using Paramiko) cannot verify the identity of the SSH server it’s trying to connect to. This is a crucial security measure to prevent man-in-the-middle attacks. Here’s a breakdown of common causes and solutions:
How to use Paramiko to collect server metrics
Paramiko allows collecting server metrics remotely. This tutorial shows how to gather system information. This is very useful for monitoring.
Troubleshooting Intermittent SSH Connections with Paramiko
Intermittent SSH connections are frustrating to debug. This tutorial covers common causes and solutions. It improves connection reliability greatly.
Resolving paramiko.ssh_exception.ProxyCommandFailure
Learn how to troubleshoot and resolve the paramiko.ssh_exception.ProxyCommandFailure error when using Paramiko for SSH connections through a proxy.
Solving paramiko.ssh_exception.PasswordRequiredException for SSH Keys
The paramiko.ssh_exception.PasswordRequiredException error occurs when you attempt to use an encrypted SSH private key without providing the necessary passphrase. Paramiko needs this passphrase to decrypt the private key before it can use it for authentication.
How to Avoid RuntimeWarning: divide by zero encountered in log in Python
The RuntimeWarning: divide by zero encountered in log is a common warning that occurs when you attempt to compute the natural logarithm of zero or negative numbers using functions like numpy.log(). This warning indicates that there’s an invalid operation happening in your code, which could lead to unexpected results or NaN (Not a Number) values.
Solving “ImportError: No module named ‘numpy'”
The error message ImportError: No module named ‘numpy’ indicates that the NumPy library is not installed in your Python environment. NumPy is a fundamental package for scientific computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.
How to Use django-adaptors
Django-adaptors is a valuable Django application that streamlines data import and export processes between various file formats and your Django models. It provides a flexible and efficient way to manage data exchange in your Django projects. This guide will explain how to install and use django-adaptors to import data from CSV files and export data Continue reading
Understanding Django Apps: How Many Apps Should Your Project Have?
Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. One of its core principles is the concept of “apps”—self-contained modules that encapsulate specific functionality. A common question among Django developers is: How many apps should my Django project have? This article explores the considerations for structuring your Django project Continue reading
Handling NumPy’s FloatingPointError: NaN or Inf in Operations
As a Python developer, you’re likely to encounter scenarios where your computations produce unexpected results, such as NaN (Not a Number) or Inf (Infinity). These values can arise in various mathematical operations and, if not handled properly, can lead to bugs or crashes in your programs. I’ll walk you through the common causes of these Continue reading
Solving NumPy’s ValueError: Arrays with Incompatible Shapes
NumPy is a powerful library in Python, especially useful for numerical and scientific computing. However, one common issue that users often encounter is the ValueError: operands could not be broadcast together with shapes. This error occurs when performing operations on arrays that do not have compatible shapes. We will explore the concept of broadcasting in Continue reading
Correcting AxisError: axis x is out of bounds for array of dimension y
An AxisError is typically encountered in Python when attempting to access an axis of an array that exceeds the dimensions of the array. This error is common in libraries such as NumPy, which is extensively used for numerical data operations. How to diagnose and fix this error?
Handling File Not Found: FileNotFoundError
Encountering a FileNotFoundError can be a common issue when dealing with file operations in Python. Let’s see strategies for handling this exception and ensuring your code is robust and user-friendly.
Debugging Common Concurrency Issues in Python: Deadlocks and Race Conditions
Concurrency issues like deadlocks and race conditions are common in multi-threaded and multi-process applications. These issues can lead to unpredictable behavior, application freezes, and data corruption. In this guide, we’ll discuss what these issues are and how to debug them effectively using Python.