190 Python Projects with Source Code: solved and explained for free.

Python is one of the best programming languages. Due to its readability and beginner-friendly nature, it has been accepted by industries around the world. So to master Python for any field you have to work on projects. In this article, I will take you through more than 190 Python projects with source code.

Socket Programming in Python: Client, Server, Peer Libraries.

In this tutorial, you'll learn how to send data from client-to-server and client-to-client connections using socket programming in Python. Socket programming is the implementation of two sockets to send and receive data bi-directionally at any given moment. It connects two sockets (or nodes) together and allows them to communicate in real time. Internet-connected applications that need to operate in real time greatly benefit from the implementation of sockets in their networking code, allowing data to be sent and received at any time. In this Python tutorial, you'll learn how to send data from client-to-server and client-to-client connections using socket network programming in Python. You'll also learn how you can leverage the power of PubNub to send data between two or more client devices for peer-to-peer communication using the PubNub Python SDK. Although this tutorial guides you step-by-step through this process, you can view a video walkthrough of this tutorial on YouTube and find the source code for this application in the GitHub repository.

Python Magic Methods You Haven't Heard About.

Python's magic methods - also known as dunder (double underscore) methods - can be used to implement a lot of cool things. Most of the time we use them for simple stuff, such as constructors (__init__), string representation (__str__, __repr__) or arithmetic operators (__add__/__mul__). There are however many more magic methods which you probably haven't heard about and in this article we will explore all of them (even the hidden and undocumented)! read more...

All-knowing machines are a fantasy Beware the human-sounding ChatGPT.

The idea of an all-knowing computer program comes from science fiction and should stay there. Despite the seductive fluency of ChatGPT and other language models, they remain unsuitable as sources of knowledge. We must fight against the instinct to trust a human-sounding machine, argue Emily M. Bender and Chirag Shah. Decades of science fiction have taught us that a key feature of a high-tech future is computer systems that give us instant access to seemingly limitless collections of knowledge through an interface that takes the form of a friendly (or sometimes sinisterly detached) voice. The early promise of the World Wide Web was that it might be the start of that collection of knowledge. With Meta’s Galactica, OpenAI’s ChatGPT and earlier this year LaMDA from Google, it seems like the friendly language interface is just around the corner, too. read more...

Hack your brain for better problem solving.

This article was originally published on Big Think+. Problem-solving skills are in demand. Every job posting lists them under must-have qualifications, and every job candidate claims to possess them, par excellence. Young entrepreneurs make solutions to social and global problems the heart of their mission statements, while parents and teachers push for curricula that encourage critical-thinking methods beyond solving for x.

Practical SQL for Data Analysis.

Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such a long way, that many developers and data scientists lost track of what they can do with the database they already have! In this article the author demonstrate how to use SQL to perform fast and efficient data analysis.

Data Cleaning IS Analysis, Not Grunt Work.

First let’s start with stating the problem with existing writing on “Data Cleaning”. Wikipedia's post on data cleaning does a decent summary of the big important qualities of data quality: Validity, Accuracy, Completeness, Consistency, Uniformity. It’s also got a section on “process” that’s really dry and academic (in a negative way) and won’t help you clean any data at all. Next I’m just gonna sample posts from the top links on Google when I search “Data cleaning”. I’ll provide links as reference so you know what I’m griping about. This highly PageRanked one is like a friendlier expansion of the Wikipedia page at the start. Luckily it redeems itself in the process section by listing a big list of example techniques to use to clean data, things like cleaning spaces, dropping irrelevant values, etc. Has some examples and illustrations!. read more...

Metasploit — A Walkthrough Of The Powerful Exploitation Framework.

Metasploit can handle everything from scanning to exploitation. In this article, we will take a look at what makes Metasploit the most versatile penetration testing toolkit. Penetration testing is hacking with permission. You might have seen cool hackers on TV attacking computer systems without getting caught. But that’s not how it works in the real world. If you hack someone without permission, there is a high chance that you will end up in jail. So if you are planning to learn hacking with evil intentions, I am not responsible for any damage you cause. All my articles are purely educational. read more...

6 Useful Python Libraries & Tools For Data Science Beginners.

Python is kind of like the frozen yogurt of programming languages — it’s extremely popular and versatile on its own, but it’s even better when you add toppings. Of course, by “toppings,” we’re talking about the many Python libraries and tools that level-up what you can do with the language. With data science, in particular, there are lots of pre-written Python code packages and extra tools that allow you to work with data in more advanced ways, explains Ada Morse, Codecademy Curriculum Developer in Data Science. In the new free course Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation. read more...

Recon-ng Information gathering tool in Kali Linux.

Recon-ng is free and open source tool available on GitHub. Recon-ng is based upon Open Source Intelligence (OSINT), the easiest and useful tool for reconnaissance. Recon-ng interface is very similar to Metasploit 1 and Metasploit 2.Recon-ng provides a command-line interface that you can run on Kali Linux. This tool can be used to get information about our target(domain). The interactive console provides a number of helpful features, such as command completion and contextual help. Recon-ng is a Web Reconnaissance tool written in Python. It has so many modules, database interaction, built-in convenience functions, interactive help, and command completion, Recon-ng provides a powerful environment in which open source web-based reconnaissance can be conducted, and we can gather all information. read more...

Top 10 Data Visualization Books.

In this article, we provide a list of 10 must-read books that will elevate your data visualization skills. Whether you’re a seasoned data practitioner looking to sharpen your visual design and storytelling skills, or a new entrant looking to grasp the foundations of data visualization, there’s something here for everyone.

Simulating the Python REPL inside ChatGPT

Lately, I have been reading some interesting articles about [ChatGPT][https://chat.openai.com]. In one, someone invented a new language and had ChatGPT speak it. In another, someone ran a virtual machine inside ChatGPT. The latter inspired me to ask this question: Can you run an interactive Python session inside ChatGPT? The short answer is “yes, you can”, and ChatGPT knows quite some Python! I played around with ChatGPT, gave it some different prompts and instructions, and then tested ChatGPT's limits and understanding of the Python language. I was blown away by ChatGPT's understanding of Python. It understood and gave the correct results for code that used: read more...

Python programming language.

Python is a high-level, general-purpose programming language that was created in the late 1980s. It is known for its simplicity, readability, and flexibility, making it a popular choice for both beginners and experienced programmers. One of the main reasons for Python's popularity is its simplicity. The language has a straightforward syntax and uses indentation to denote blocks of code, making it easy to read and understand. This simplicity also makes Python a great choice for prototyping and testing ideas quickly, as well as for creating simple scripts to automate tasks. In addition to its simplicity, Python is also known for its flexibility. It can be used for a wide range of applications, including web development, scientific computing, data analysis, and artificial intelligence. Its extensive standard library and a large number of third-party libraries make it easy to perform a variety of tasks without the need to write a lot of code. One of the key features of Python is its support for object-oriented programming (OOP). OOP is a programming paradigm that organizes code into "objects," which represent real-world entities and can contain data and behaviors. This makes it easier to write reusable and maintainable code, as well as to create complex systems by combining different objects. Python is also an interpreted language, which means that it does not need to be compiled before it is executed. This makes it easier to develop and debug code, as changes can be tested immediately without the need to compile the code first. Overall, Python is a powerful and versatile programming language that is well-suited for a wide range of applications. Its simplicity, flexibility, and support for OOP make it a popular choice among developers, and it is widely used in a variety of industries, including finance, science, and technology.

SQL Cheat Sheet — SQL Reference Guide for Data Analysis.

Whether you’re learning SQL through one of our interactive SQL courses or by some other means, it can be really helpful to have a SQL cheat sheet. Bookmark this article, or download and print the PDF, and keep it handy for quick reference the next time you’re writing an SQL query! Need to brush up on your SQL before you’re ready for the cheat sheet? Check out our interactive online SQL Fundamentals course, read about why you should learn SQL, or do some research about SQL certifications and whether you’ll need one.

Python for Web Scraping.

Web Scraping is the process of extracting data from a website. Although you only need the basics of Python to start learning web scraping, this might sometimes get complicated because web scraping goes beyond Python. It also involves learning the basics of HTML, XPath, and tons of new methods in Beautiful Soup, Selenium, or Scrapy. read more...

LSTM Google Stock Prediction part 2 -Seasonal Decomposition Time SeriesLevel 3, 22 minutes

In our second part of the LSTM stock prediction using Tensorflow. Now that we have our deep learning model with LSTM recurrent layer set up we focus on the times series part of our project and dive deep into the seasonal decomposition of our MACD, RSI, Fast Stochastic, and pct_change indicators. The goal here is that we can just use the last 20 days but for each indicator that create a hundred features. As common with time series stock market prediction, we are limited on the number of rows. So we can't use so many features and we use statsmodel seasonal decompose function from the statsmodel.tsa.seasonal library. This allows use to study the autocorrelation in only the seasonal component and extract which of the past days have a correlation. This allows us to choose only a few features instead of so many. read more...

How to Build a Python Program to Download YouTube Videos.

You can quickly watch videos with a few mouse clicks, but downloading videos is difficult. But in a recent upgrade, YouTube now allows you to save videos in its download folder for offline viewing. Still, you are unable to save them locally.

In this tutorial, you will learn how to use Python code to download YouTube videos. As you may know, one of Python's great strengths is its huge number of modules and libraries. We will write the Python script using the popular pytube package.