Monday 2 September 2024

Troubleshooting Python in Git Bash on Windows: A Guide with Solutions

Encountering issues when trying to run Python in Git Bash on Windows is a common problem that many developers face. If you’ve found that typing python in your Git Bash command line results in the terminal freezing without any error messages or output, you’re not alone. In this post, we will explore different ways to troubleshoot and resolve this issue, offering both temporary and permanent solutions.

The Problem

The issue arises when you try to run Python directly from Git Bash, only to find that the terminal becomes unresponsive or simply sits idle without launching the Python interpreter. This behavior is different from what you might experience in PowerShell, where Python runs without any problems. Here’s what the problem might look like in your Git Bash terminal:

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Monday 20 May 2024

How to Determine the Latest Python Version Compatible with PyTorch

Are you running into issues trying to install PyTorch on your current Python setup? This is a common problem many developers face when they are using a newer version of Python that isn’t yet supported by the PyTorch libraries. In this blog post, we’ll guide you through how to determine the latest version of Python that works with PyTorch and provide you with examples to install it correctly.

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Saturday 1 January 2022

Python's GIL: The Ultimate Guide to Multi-Threading Performance Optimization

Python is one of the most widely-used programming languages today, thanks to its simplicity, flexibility, and versatility. One of the key features of Python is its ability to support multi-threading, which enables developers to write programs that can perform multiple tasks simultaneously. However, Python's Global Interpreter Lock (GIL) can often limit the benefits of multi-threading, leading to performance bottlenecks. In this article, we will take a deep dive into Python's GIL, exploring its impact on multi-threading performance and how to optimize it.

Understanding the GIL

Python's GIL is a mechanism used by the interpreter to ensure that only one thread executes Python bytecode at a time. The GIL is a single lock that is used to serialize access to Python objects, preventing multiple threads from modifying them at the same time. This is done to ensure thread-safety and prevent race conditions, but it can also limit the performance benefits of multi-threading in Python.

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Thursday 12 October 2023

Unlock the Power of Python with These Top Learning Resources

Are you eager to learn Python but unsure where to start? Look no further! In this article, we'll guide you through some of the best resources available online to help you master Python programming. Whether you're a beginner or looking to advance your skills, we've got you covered with our carefully curated list of free and certified courses. Let's dive in!

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Monday 12 August 2024

Simplifying Type Hints in Python: Using Self for Clarity and Efficiency

Python’s type hinting capabilities have significantly evolved over the years, allowing developers to write more maintainable and robust code. A common challenge, however, has been hinting methods within a class to specify that they return an instance of the class itself. This blog post explores the modern solutions provided by Python for this problem, including the use of Self from Python 3.11 onwards, and how to handle it in earlier versions.

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Saturday 14 September 2024

How to Specify Multiple Return Types Using Type Hints in Python

How to Specify Multiple Return Types Using Type Hints in Python

In Python, type hints improve code readability and help developers understand what kind of values are expected for function arguments and returns. But sometimes, a function can return multiple types, which raises the question: how can we specify these multiple return types?

In this blog post, I’ll walk you through different ways to handle multiple return types using Python type hints, covering various versions of Python and how to use unions, tuples, and type-checking libraries.

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Monday 1 March 2021

Boost Your Python Code Performance: Tips for Optimizing with JIT Compilation and Profiling Tools

Python is a powerful language for scientific computing and data analysis, but it's also known for its slower execution speed compared to compiled languages like C++ and Java. However, there are ways to optimize Python code for better performance. In this article, we'll explore some tips and tricks for optimizing Python code, including using JIT (just-in-time) compilation and profiling tools.

Use JIT Compilation

One way to improve the performance of your Python code is to use JIT compilation. JIT compilation is a technique that dynamically compiles code at runtime, rather than ahead of time. This allows the interpreter to optimize the code based on the actual data that is being processed, resulting in faster execution.

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Friday 8 July 2022

Is Python Pass by Value or Pass by Reference? Example

Python is a popular programming language known for its simplicity and ease of use. When working with Python, it's important to understand how values are passed between functions and methods. Specifically, many developers wonder whether Python is pass by value or pass by reference. In this blog post, we'll explore this topic in depth and provide some examples to help illustrate the concepts.

Pass by Value vs. Pass by Reference

First, let's define what we mean by pass by value and pass by reference. In a pass by value language, when a function or method is called, a copy of the value is created and passed to the function. This means that any changes made to the value within the function are only made to the copy, and not the original value. In contrast, in a pass by reference language, a reference to the original value is passed to the function, so any changes made to the value within the function are made to the original value.

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Wednesday 9 March 2022

what are python private variables its best practices?

Python, private variables are variables that are meant to be used only within the class in which they are defined. Private variables are defined by prefixing the variable name with two underscores (__) at the beginning of the variable name.

For example, if you have a class called Person and you want to define a private variable called __age, you would write:

class Person: def __init__(self, name, age): self.name = name self.__age = age


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Saturday 27 July 2024

Exploring the Latest Functionalities in Python: A 2024 Overview

Python continues to be one of the most popular programming languages, renowned for its simplicity and versatility. As we move into 2024, the language has introduced several exciting features that enhance its functionality and usability. In this blog post, we’ll explore some of the latest additions to Python, including structural pattern matching, type hinting improvements, and enhancements in standard libraries.

1. Structural Pattern Matching

Introduced in Python 3.10, structural pattern matching has been refined and expanded in the latest versions. This powerful feature allows developers to match complex data structures using a concise syntax, making code more readable and easier to maintain.

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Sunday 30 June 2024

Python and Perl Mappings in Linux: A Detailed Guide

In the world of Linux system administration and scripting, Python and Perl are two of the most powerful and widely used programming languages. Each language offers unique features and tools for handling various tasks related to file system manipulation, process management, and automation. This blog post explores the ways Python and Perl can be used in Linux environments, focusing on their applications, similarities, and differences in file handling, text processing, and system commands execution.

Understanding the Basics

Python is known for its readability and simplicity. It is often chosen for its straightforward syntax and extensive standard libraries. On the other hand, Perl has a reputation for its text manipulation capabilities and its use in legacy systems, particularly in the context of web servers and CGI scripts.

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Tuesday 6 August 2024

Navigating the Bytes and Strings Transition in Python 3: Solving TypeError

Transitioning from Python 2 to Python 3 can be fraught with small yet critical changes, especially when it comes to handling file operations that involve strings and bytes. A common hurdle is the TypeError: a bytes-like object is required, not 'str', which trips up many new Python 3 users. This error underscores the difference in how Python 2 and Python 3 handle strings and bytes, reflecting a more rigorous approach to data types in Python 3.

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Saturday 6 July 2024

Resolving the numpy.dtype size changed Error in Matlab-Python Integration


If you’re integrating Python and Matlab, especially for NLP tasks using libraries like spacy, encountering binary incompatibility errors such as numpy.dtype size changed can be a major roadblock. This error typically arises due to a mismatch in the compiled versions of the libraries in use, often between NumPy and its dependent libraries.

Understanding the Error

The error message:

ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject.

indicates a version conflict, where the expected size of a data type in NumPy does not match the actual size in the current environment. This mismatch often occurs after updating or changing versions of libraries without ensuring compatibility.

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Monday 19 August 2024

Why is [] Faster Than list() in Python?

When optimizing Python code, you may encounter situations where the difference between two seemingly identical operations can significantly impact performance. One such case is the difference in speed between using [] to create an empty list and using list(). Although both methods produce the same result—an empty list—the former is notably faster. Let’s dive into why this happens and how it might affect your Python code.

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Friday 5 July 2024

Navigating Compatibility Issues with NumPy 2.0 in Python Projects

When developing Python applications that utilize libraries like OpenCV and imutils, compatibility with NumPy versions can cause significant roadblocks. Recently, the release of NumPy 2.0 introduced breaking changes that can affect existing projects, as demonstrated by a common error when trying to run a module compiled with an earlier version of NumPy on the latest release.

Problem:

The core issue arises when libraries dependent on NumPy, such as OpenCV, are used in an environment where an incompatible version of NumPy is installed. This mismatch can lead to errors like ImportError: numpy.core.multiarray failed to import or AttributeError: _ARRAY_API not found. These errors are typically encountered during the import phase of the project, preventing the application from even starting.

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Saturday 25 May 2024

Setting Up Your Environment with Data Science in Python

Welcome to the exciting world of Data Science using Python! Whether you are a seasoned professional looking to polish your skills, or a beginner stepping into the realm of data analysis, Python offers a robust suite of libraries that makes data science accessible and effective. In this post, we will walk through a practical example of how to handle, analyze, and visualize data using Python.

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Wednesday 15 May 2024

Solving the "Externally-Managed-Environment" Error in Python


Many Python developers encounter the “externally-managed-environment” error when using pip to install packages in environments that are managed by a system package manager like apt. This can be frustrating, especially when working in Debian-based systems that employ strict policies to prevent conflicts between system-managed and locally-installed Python packages. In this blog post, we’ll explore several strategies to handle this error effectively without compromising your system’s integrity.Read more »

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Monday 26 August 2024

Managing Multiple Commands in Docker Compose

Docker Compose is a powerful tool that helps you define and run multi-container Docker applications. At times, you might need to run multiple commands in a single service, which can seem challenging at first. In this post, we’ll explore how to effectively manage multiple commands within Docker Compose configurations, providing clear and alternate examples to enhance your setup.

Using entrypoint and command Together

One common approach is to split the initialization and runtime aspects of your container using both entrypoint and command. Here’s an example:

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Tuesday 5 January 2021

Python Memory Management: Avoiding Common Pitfalls and Memory Issues and memory leaks and excessive garbage collection

Memory management is an important aspect of programming in any language, and Python is no exception. In Python, memory is managed automatically through a process called garbage collection. While this can be convenient for developers, it can also lead to issues like memory leaks and excessive garbage collection. In this article, we will explore Python's memory management model and provide tips for avoiding common pitfalls.

Python Memory Model

In Python, objects are created dynamically and stored in memory. Each object has a reference count, which keeps track of how many references to the object exist. When an object's reference count reaches zero, it is no longer accessible and can be garbage collected.

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Sunday 28 July 2024

Automatically Creating a requirements.txt File in Python

When working on Python projects, managing dependencies can sometimes be a challenge, especially if you download code from platforms like GitHub that don’t come with a requirements.txt file. Fortunately, there are several tools and methods to automate the creation of a requirements.txt file, which lists all the libraries and their versions needed to run your project. This guide will provide you with multiple approaches using both pip and pip3, and will cover different scenarios to suit your specific needs.

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Saturday 8 June 2024

Solving Module Import Errors with Pipx in Debian

 

Introduction

Working with Python packages on Debian can occasionally lead to unexpected challenges, especially with recent changes in how Debian handles Python environments. A common issue is encountering an ImportError when trying to use a module installed with pipx, as pipx installs packages in isolated environments.

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Saturday 15 June 2024

Automating Video Downloads from Telegram Groups with a Python Bot

In this post, we’ll explore how to automate the process of downloading videos from a Telegram group using a Python bot. This can be particularly useful for managing media in groups you administer or for automating routine data handling tasks.

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Tuesday 13 August 2024

Enhancing Date Handling in Python: Advanced Techniques and Examples

Working with dates in Python is a common requirement for many programming tasks, whether you’re managing events, performing time series analysis, or simply logging activities. While the standard datetime module is robust, there are other techniques and libraries available that can provide more flexibility and efficiency. In this blog post, we will dive deeper into some advanced methods for handling dates in Python, showcasing different approaches with practical examples.

1. Using dateutil for Robust Date Parsing

The dateutil library extends Python’s datetime module by providing additional utilities to handle a variety of date formats. It is particularly useful for parsing dates from strings that may come in different formats.

from dateutil import parser

# Directly parse a string to get today's date
today = parser.parse("today")
print("Today's Date:", today.strftime('%Y-%m-%d'))

# Parse complex date strings
complex_date = parser.parse("Tue, 12 Dec 2024 12:34:56")
print("Complex Date:", complex_date.strftime('%Y-%m-%d'))

2. High-Performance Date Parsing with ciso8601

When performance is a concern, especially when dealing with large datasets that include date strings in ISO 8601 format, ciso8601 comes into play. It is designed for rapid parsing of this particular format.

import ciso8601
import datetime

# Current time formatted and parsed for demonstration
now = datetime.datetime.now().isoformat()
parsed_date = ciso8601.parse_datetime(now)
print("Parsed ISO Date:", parsed_date.strftime('%Y-%m-%d'))

3. Simplifying Date Manipulation with delorean

Delorean is another library that simplifies date manipulation by providing a cleaner interface to navigate datetime in Python. It builds on top of pytz and dateutil to offer a timezone-aware Datetime object.

from delorean import Delorean

# Obtain a Delorean object representing now, and shift timezones
d = Delorean()
d = d.shift("UTC")
print("UTC Now:", d.strftime('%Y-%m-%d'))

# Easily move to the beginning of the day
start_of_day = d.truncate('day')
print("Start of Day:", start_of_day.strftime('%Y-%m-%d'))

4. Using Python’s Built-in time Module

For those who prefer not to use third-party libraries, Python’s built-in time module is an alternative, albeit less feature-rich, for handling date and time based operations.

import time

# Get current UTC date in a specific format
today = time.strftime("%Y-%m-%d", time.gmtime())
print("Today's UTC Date:", today)

5. Practical Date Utilities with pendulum

Pendulum is another third-party library that provides a cleaner and more high-level interface for date and time manipulation, supporting timezones and humanization.

import pendulum

# Current time in a specific timezone
now_in_paris = pendulum.now('Europe/Paris')
print("Now in Paris:", now_in_paris.to_date_string())

# Difference between two dates
today = pendulum.now()
past = pendulum.now().subtract(years=1)
print("Years Ago:", today.diff(past).in_years())

Python offers a diverse range of options for handling dates and times through its standard library and numerous third-party modules. Depending on your specific requirements—whether it’s parsing complex date strings, dealing with timezones, or optimizing for performance—there’s likely a Python library that meets your needs. By exploring these alternatives, developers can choose the most appropriate tools to simplify their date and time manipulation tasks in Python projects.

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Tuesday 8 June 2021

Top 5000 python programs examples with solutions - Part 4

Program 41:

How to write  a program to print Fibonacci series of a number?

def fib(n):
   p, q = 0, 1
   while(p < n):
       yield p
       p, q = q, p + q
k=int(input('Enter the Number\n'))
for i in fib(k):
   print(i)

 

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Friday 31 December 2021

Deploying Python Machine Learning Models: Best Practices for Production

Deploying machine learning models in production is an essential step in turning a prototype or a proof-of-concept into a valuable product. However, this step can be challenging and requires a good understanding of the deployment process and the best practices for building and deploying machine learning models.

In this article, we will explore the best practices for deploying Python machine learning models in production, including how to package your code, set up your environment, deploy your model to a server, and expose it as a REST API. We will use Flask, a popular web framework, to build a REST API that exposes a trained machine learning model, and we will walk through a step-by-step guide on how to deploy it to a server.

Best Practices for Deploying Python Machine Learning Models:

Packaging Your Code:

One of the best practices for deploying machine learning models is to package your code using a package manager like pip. This allows you to create a distribution package that contains all the required dependencies for your code, making it easier to install and deploy your code on a server.

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Friday 18 March 2022

Python Xml Parsing Types

Python provides several libraries for parsing and processing XML documents. In this tutorial, we will discuss how to parse XML documents using Python.

1.Parsing XML Documents with xml.etree.ElementTree:

The xml.etree.ElementTree module provides a simple and efficient way to parse and manipulate XML documents in Python. Here is an example of parsing an XML document using the ElementTree module:

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Wednesday 9 March 2022

Python Data Analysis: NumPy vs. Pandas vs. SciPy

Python has become a popular programming language for data analysis, thanks to the rich collection of libraries available for the task. In this article, we'll compare three of the most popular data analysis libraries in Python: NumPy, Pandas, and SciPy. We'll go through the basics of each library, how they differ, and some examples of how they're used.

Here's a comparison of NumPy, Pandas, and SciPy using a tabular format:

PointNumPyPandasSciPy
1PurposeNumerical ComputingData Manipulation
2Key FeaturesMultidimensional arrays, Broadcasting, Linear algebra, Random number generationDataFrame and Series data structures, Reading and writing data to CSV, SQL, and Excel, Merging and joining datasets
3Data Structuresndarrays (n-dimensional arrays)DataFrames and Series (tables)
4Supported Data TypesNumeric data types (integers, floats, etc.)Numeric and non-numeric data types (strings, timestamps, etc.)
5PerformanceFast and efficient for large arraysFast and efficient for structured data
6BroadcastingSupports broadcasting for element-wise operations on arraysBroadcasting is not directly supported, but can be achieved using the apply() method
7Linear AlgebraProvides a wide range of linear algebra operations, including matrix multiplication, inversion, and decompositionSupports some linear algebra operations, but not as extensive as NumPy
8Data ManipulationNot designed for data manipulation, but can be used in conjunction with other librariesDesigned for data manipulation and analysis, with tools for merging, joining, filtering, and reshaping data
9Signal and Image ProcessingNot designed for signal and image processing, but can be used in conjunction with other librariesLimited support for signal and image processing
10StatisticsBasic statistical functions are provided, but not as extensive as SciPyLimited support for statistical functions

NumPy

NumPy stands for Numerical Python, and it's a library that provides support for arrays and matrices of large numerical data. NumPy is widely used in scientific computing, data analysis, and machine learning, among others. NumPy provides a fast and efficient way to handle large datasets and perform mathematical operations on them.

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Wednesday 14 August 2024

Running External Commands in Python: From Simple Scripts to Advanced Uses



When working with Python, you often encounter scenarios where you need to execute an external command or script from within your Python code. This is a common requirement in automation, data processing, and many other fields. Python provides several methods to handle these tasks efficiently and securely. Let’s explore various methods to run external commands, highlighting the pros, cons, and appropriate use-cases for each.

Basic Execution with os.system

The simplest way to execute a command in Python is using the os.system() function. This function takes a string that contains the command you want to execute.

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Wednesday 7 August 2024

Mastering Command Execution in Python: A Comprehensive Guide

When building applications in Python that interact with the operating system, it’s essential to know how to execute external commands just as you would in a shell or command line. Whether you need to list files, process data, or automate system tasks, Python’s ability to execute external commands is incredibly powerful. Here, we’ll explore how to effectively execute these commands using the subprocess module, focusing on best practices and security considerations.

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Wednesday 15 September 2021

Top 5000 python programs examples with solutions - Part 7

Program 71:

Write a program to form below phrases? using print() function.

I miss you in the morning;

    I miss you late at night.

        Just to think about you

        Is my joy and my delight.

I miss you in the morning;

    I miss you late at night.

print("I miss you in the morning; \n\tI miss you late at night. \n\t\tJust to think about you \n\t\tIs my joy and my delight. \nI miss you in the morning; \n\tI miss you late at night.")

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Sunday 8 May 2022

python private methods, how it differs with public methods best practices

In Python, private methods are methods that are intended to be used only within the class in which they are defined. Private methods are defined by prefixing the method name with two underscores (__) at the beginning of the method name.

Here is an example of a private method in Python:

class MyClass:

    def __init__(self, value):

        self.__value = value


    def __private_method(self):

        print("This is a private method.")


    def public_method(self):

        self.__private_method()


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Friday 25 February 2022

Connecting from Inside a Docker Container to the Localhost of the Host Machine

Docker has revolutionized the way we build, ship, and run applications. It allows us to encapsulate our applications and their dependencies in containers, providing a consistent and isolated environment for running them. However, working with Docker containers sometimes raises challenges, especially when trying to access services running on the host machine, such as databases or APIs exposed on localhost.

In this blog post, we will explore the steps to connect from inside a Docker container to the localhost of the host machine. We will cover various scenarios, including connecting to services running on different ports and accessing services on both Linux and Windows host machines. We will also provide code examples and explanations for each step.

Step 1: Understand Docker Networking Basics

Docker containers have their own networking stack, which means that by default, they are isolated from the host machine's network. This isolation is done for security and stability reasons. However, it also means that if you try to access the host machine's localhost from inside a Docker container, you will encounter a connection error.

Step 2: Use Host Networking Mode

One way to allow a Docker container to access the host machine's localhost is by using the host networking mode. In this mode, the container shares the host machine's network namespace, allowing it to directly access the host's network interfaces. However, this mode has some security implications, as it exposes all host ports to the container.

docker run --network host <your_image>

Step 3: Use Docker Host IP Address

Another approach is to use the host machine's IP address instead of localhost. Docker provides a special DNS name "host.docker.internal" that resolves to the IP address of the host machine. This can be used as a replacement for localhost when connecting from inside the container.

Example (Python):

import requests

response = requests.get('http://host.docker.internal:8000/api')
print(response.text)


Step 4: Use Custom Docker Bridge Network

If you want more control over the networking and avoid using the host network mode, you can create a custom Docker bridge network and attach the container to it. This allows you to access services running on the host machine by using the host's IP address and the exposed port.

Example:

# Create a custom bridge network
docker network create my_bridge_network

# Run the container attached to the custom network
docker run --network my_bridge_network <your_image>


Step 5: Port Forwarding

In some cases, you may want to access a service running on a specific port on the host machine from inside the Docker container. In such scenarios, you can use port forwarding to map a port on the host to a port on the container.

Example:

# Forward port 8000 on the host to port 80 on the container
docker run -p 8000:80 <your_image>


Step 6: Consider Firewall and Security Settings

When connecting from inside a Docker container to the host machine's localhost or IP address, ensure that any firewalls or security settings on the host machine do not block the connection. In some cases, you may need to adjust firewall rules to allow incoming connections from Docker containers.


Let's walk through an example to demonstrate how to connect from inside a Docker container to the localhost of the host machine using the "host.docker.internal" DNS name.


Step 1: Create a Simple Flask API on the Host Machine

First, let's create a simple Flask API running on the host machine. This API will listen on port 8000 and respond with a "Hello, Docker!" message.

Create a file named app.py with the following content:


from flask import Flask

app = Flask(__name__)

@app.route('/api')
def hello():
    return 'Hello, Docker!'

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)


Step 2: Run the Flask API on the Host Machine

Open a terminal or command prompt and navigate to the directory where app.py is located. Then, run the Flask API using the following command:

python app.py


The Flask API should now be running on http://localhost:8000/api.

Step 3: Create a Docker Container to Access the API

Now, let's create a Docker container that will access the Flask API running on the host machine. We will use the python:3.9 base image and install the requests library to make HTTP requests to the API.

Create a file named `Dockerfile` with the following content:


# Use the Python 3.9 base image
FROM python:3.9

# Install the requests library
RUN pip install requests

# Set the working directory inside the container
WORKDIR /app

# Copy the Python script into the container
COPY app.py /app/app.py

# Run the Python script when the container starts
CMD ["python", "app.py"]


Step 4: Build and Run the Docker Container

Open a terminal or command prompt in the same directory where the Dockerfile and app.py files are located. Build the Docker image using the following command:

docker build -t my_flask_app .

Next, run the Docker container using the following command:

docker run -p 8000:8000 my_flask_app


Step 5: Access the Flask API from Inside the Docker Container

The Docker container is now running and should be able to access the Flask API on the host machine. Inside the container, we can use the requests library to make an HTTP request to the API.

Create a new terminal or command prompt window, and run the following command to access the Docker container:

docker exec -it <container_id> /bin/bash


Replace <container_id> with the ID of the running container. Once inside the container, run the following Python script:

import requests

response = requests.get('http://host.docker.internal:8000/api')
print(response.text)


This script will make an HTTP GET request to the Flask API running on the host machine using the "host.docker.internal" DNS name. It should print the "Hello, Docker!" message returned by the API.


In this example, we have seen, how to connect from inside a Docker container to the localhost of the host machine using the "host.docker.internal" DNS name. By leveraging this DNS name, we can access services running on the host machine from within the Docker container, making it easier to interact with services and applications during development and testing.

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Tuesday 13 August 2024

Pretty-Printing JSON in Shell Scripts: A Developer’s Guide

Working with JSON data is common in software development, especially in web development where it is often used for configuration files, communication between servers, and more. While JSON’s data structure is easy to understand, it can become quite difficult to read when it is compacted into a single line. This is where pretty-printing comes into play, enhancing the readability of JSON data by formatting it with proper indentations and line breaks.

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Friday 12 July 2024

Working with Nested Arrays in Python: Practical Examples

Nested arrays, or arrays of arrays, are a fundamental concept in programming, often used to represent matrices, grids, or any multi-dimensional data. In Python, nested arrays can be efficiently managed using lists, or for more complex applications, with libraries like NumPy. In this blog post, we’ll explore how to create, manipulate, and utilize nested arrays in Python through practical examples.

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Tuesday 27 August 2024

Pretty-Printing JSON in Shell Scripts

Working with JSON data directly in the command line can be cumbersome due to its compact and hard-to-read format. Fortunately, there are several tools and techniques you can use within a Unix shell to pretty-print JSON data, making it easier to read and debug. In this post, we’ll explore different methods to format JSON using various command-line tools.

Using jq

jq is a lightweight and flexible command-line JSON processor. It can pretty-print JSON with minimal effort:

echo '{"foo": "lorem", "bar": "ipsum"}' | jq .

This command takes a JSON string as input and pretty-prints it with proper indentations and color-coding (if your terminal supports colors).

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Saturday 20 July 2024

Understanding Python Itertools Permutations with Practical Examples

When working with permutations in Python, especially using the itertools module, understanding the behavior of iterators is crucial to avoiding common pitfalls. This blog post dives into the nuances of using itertools.permutations and explains why certain behaviors occur when you use iterators differently in your code.

The Basics of itertools.permutations

Python’s itertools.permutations function is a powerful tool for generating all possible orderings of an input sequence. It returns an iterator, which generates the permutations lazily, meaning it produces them one-by-one as you iterate over them, rather than all at once. This is efficient because it saves memory, but it also means each permutation can only be read once.

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Wednesday 17 March 2021

A beginner's guide to building desktop applications with Python and PyQt

Python is a powerful programming language that is widely used for developing various types of applications, including desktop applications. One of the popular frameworks for building desktop applications with Python is PyQt. PyQt is a set of Python bindings for the Qt application framework and is used for creating graphical user interfaces (GUIs) for desktop applications. In this beginner's guide, we will explore how to build desktop applications with Python and PyQt.

Installing PyQt

Before we start building our desktop application, we need to install PyQt. PyQt can be installed using pip, the Python package manager. 

Open your terminal or command prompt and enter the following command:

pip install pyqt5


Once PyQt is installed, we are ready to build our first desktop application.

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