What is Python used for?

Python has become one of the most widely used programming languages in the world mainly because it is extremely powerful, simple and has very good libraries that simplify development. But what is python, Python is a high-level, interpreted, object-oriented programming language. It was created by Guido van Rossum in 1991 and has become one of the most popular languages today due to its simplicity, readability and versatility.

Python is used in a wide variety of applications, including web development, data analysis, artificial intelligence and machine learning. In addition, Python has a large number of libraries and frameworks that facilitate the development of complex applications.

What are the uses of Python?

Python for web development

Python, among many of the areas in which it has been developed, is the development of web applications. Python in this area is mainly used for the development of the Back End (the server side) with different specialized libraries that are free to use.

Among the most outstanding libraries we have for this undertaking are:

• Flask
• Django
• Pyramid
• Web2Py

Python for machine learning

The objective of machine learning is to create algorithms that allow the system to learn by itself from the data that is given to it, to specialize in this branch there are Machine Learning Engineer courses. These applications are not composed of the same type of development where we tell the program what and how to do what it should do, but the algorithms are improved from the data we feed it.

The Neural Networks are the processing units and try to simulate the behavior of our neurons. In this network the information will be received, they will learn to process it and this will allow them to generate results based on their learning.

A more practical example is when we receive ads depending on what we have consumed, either on YouTube, Facebook and other platforms, these networks are the ones that make the recommendations.

This machine learning process has several libraries that make the job much easier, such as:

• TensorFlow
• Keras
• PyTorch

Python for Cybersecurity

Python is widely used in cybersecurity due to its simplicity, versatility, and extensive library ecosystem. It enables cybersecurity professionals to automate routine tasks, analyze security data, and develop tools and systems for securing digital assets. Here are some of the most common use cases of Python in cybersecurity:

Automation of Security Tasks: Python scripts can automate routine tasks such as scanning for vulnerabilities, monitoring network traffic, and sending alerts for suspicious activities. This reduces the manual workload on security teams and increases the efficiency of security operations.

Penetration Testing: Python is extensively used in penetration testing to identify vulnerabilities in networks and applications. It's used to develop scripts and tools that simulate cyber attacks to test the strength of security systems. Libraries like scapy for packet manipulation and Impacket for crafting and decoding network protocols are popular among penetration testers.

Forensic Analysis: Python aids in digital forensic investigations by automating the analysis of files, logs, and data remnants to uncover evidence of cyber attacks or malicious activities. Libraries like Volatility for memory analysis and pytsk3 for accessing disk images are commonly used in forensic applications.

Development of Security Tools: Python is often used to develop custom security tools, including intrusion detection systems (IDS), network scanners, and security automation scripts. The flexibility of Python allows for the quick development and deployment of such tools.

Malware Analysis: Python supports malware analysis, helping security professionals understand the behavior of malicious software. Tools developed in Python can automate the process of dissecting malware, extracting its characteristics, and understanding its impact. Libraries such as pefile for analyzing PE files and yara-python for malware identification and classification are key to this process.

Data Analysis and Security Research: Python's powerful data analysis libraries, such as pandas and NumPy, are used to process and analyze large datasets of security events and logs. This analysis helps in identifying patterns, anomalies, and trends in cyber threats.

Web Scrapping for Threat Intelligence: Python's libraries like BeautifulSoup and Scrapy can be used to scrape the web for threat intelligence data. This includes gathering information on new vulnerabilities, threat actor tactics, and indicators of compromise (IoCs).

Cryptography and Encryption: Python supports various cryptographic operations, making it possible to develop secure communication channels, encrypt sensitive information, and ensure data integrity. Libraries like cryptography and PyCrypto provide implementations of cryptographic algorithms and protocols.

Popular Libraries and Tools in Python for Cybersecurity:

• Scapy: For packet manipulation and network discovery.
• Impacket: For working with network protocols.
• Volatility: For forensic memory analysis.
• Wireshark: Although not a library, Python can be used to automate tasks in Wireshark for packet analysis.
• BeautifulSoup and Scrapy: For web scraping.
• cryptography and PyCrypto: For cryptographic functions.
• pandas and NumPy: For data analysis.
• yara-python: For malware identification.

Python for Data Science

In recent years the use of Python for Data Science has increased and has become one of the main languages for visualization and data processing today, which is to be expected. Py has extremely powerful libraries that allow the generation of visual representations such as the following:

• Lines, bars and markers.
• Subcharts and axes.
• Statistics with box plots, histograms and bar charts.
• 3D graphs.
• Pie charts.

The most used libraries for data analysis would be NumPy (the one used by most scientists who use Python to record their research) and Pandas (open source, fast and powerful tool that allows to analyze and manipulate data in a flexible and easy way).

For graph visualization we have a larger collection of libraries:

• Pandas -> It has tools for data analysis and visualization.
• Matplotlib -> A very complete library for creating static, animated and even interactive visualizations!
• Bokeh -> An interactive library for data visualization more focused on modern web browsers.
• Seaborn -> It is based on Matplotlib and is also used for data visualization.
• ggplot2 -> Allows to create graphs in a declarative way. You will have to insert the data and tell the library how to transform the variables to the presentation, the primitives of the graphics to use and ggplot2 will take care of the rest.

Python for video game development

Python has also made its entry into the world of video games thanks mainly to the following libraries:

• pygame -> The most active package for video game development within Python. With this library, Py can communicate with SDL (Simple DirectMedia Layer) to access multiple platforms. There may be a delay when updating the Python version as it must be compiled for each Python version and platform.

• pyglet -> Based on OpenGL, this library makes up for the difficulties of pygames in the sense that, being completely written in Python, it does not need to be compiled every time there is a version change (except for the jump from Python 2 to Python 3).

• turtle -> a module that comes with Python once you install it on the system and allows the creation of video games with simpler graphics and user interface.

Python for medicine

Python has been used in medical research and data processing for the detection of diseases or anomalies in the patient's health.

Making use of different application branches that we have already mentioned, in the field of medicine and pharmacology, Python is capable of:

• Creating and updating models for the development of new drugs.
• Diagnose based on medical history and symptoms.
• Analysis of medical data.

Among the libraries for the treatment and analysis of medical data, we find:

• pyGeno: Open source library for working with reference and customized genomes.
• MedPy: Open source library for processing medical images.

If we keep looking we will find that it can be used in many more branches, as can be, to mention a few:

• Robotics
• Astronomy
• Neurosciences
• Unmanned vehicles
• Meteorology

As you can see, thanks to its power and flexibility, Python is rightly one of the most widely used programming languages in different branches of development and knowledge, ranging from purely technology-related functionalities to health and human welfare. Undoubtedly a language to be taken into account, if not mandatory, if we are in the world of programming.