Metasploit VS Pandas

Metasploit

Metasploit: https://github.com/rapid7/metasploit-framework

1-) Learn Hacking on a debian based distro like Kali Linux - I personally started with tools like nikto, camhacker... and then moved to more complex frameworks like metasploit.

I once had to give a presentation about Metasploit, and whether it was ethically correct for the creator to make it free and open-source, available to everyone. And in researching this I realized that there were a lot of parallels between the arguments for or against hacking tools being readily available and the arguments for or against gun control. I'll just list a few quickly:

Metasploit Framework (mentioned earlier)

This phase is where the pen testers practically prove that there exist potential vulnerabilities in the target system. The pen testers do the hacking using an array of technical approaches and social engineering methods to exploit the vulnerabilities. The ethical hackers commonly use Metasploit framework to automatically execute exploitation against the target systems. Moreover, they may install malwares such as rootkit to persistently maintain their foothold and further compromise the target system.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.