Introduction to Python
Python is an interpreted scripting language conceived in the 1980s, with a focus on code readability. It’s version 2 was released in 2000, and version 3 in 2008. Python 3 had significant fundamental changes, causing a lack of backward compatibility. Despite this, python 2 gained a lot of traction once it matured, and probably what caused python to remain a universal favorite while version 3 was being adopted.
What can I do with Python?
Python has evolved to be a very powerful, multi-paradigm language. It fully supports object-oriented programming, structural programming. It also supports functional and logical programming. Due to flexibility, and user-friendly, it’s supported by a huge open-source community, allowing it’s used to span across a vast number of domains.
Some of the Python domains are very popularly used for website development, automation of operational work, creating bots, data science, data analysis, machine learning, application development, utility scripts, browser automation, testing and deployment pipeline creation.
Importance of Python
Python has become the preferred language for the majority of the open-source community. Due to its popularity in this community, and user-friendliness, it has become popular among a huge chunk of fresh graduates and folks in early-stages of their career. This is has caused a good amount of exploratory work executed by testing python capabilities, and increasing the same when required. Python frameworks like Django are powering some of the very well known companies like Instagram, Pinterest, Mozilla, National Geographic, etc.
Selenium has become a major library used for browser automation and automated testing. Python is leading in the number of libraries support the Data Eco-world (Data Analysis, Data Visualisation, Data Science, Production-ready models, etc) with libraries like scikit-learn, TensorFlow, seaborn, matplotlib,numpy, pandas, etc. Needless to say, with active, popular and wide usage of python, it has a very important place is the software industry and its rise.
Python can be used in web Development
Python is a language which is easy to learn and understand in Web Development. Python also offers many frameworks which are mentioned below.
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What is Back-end Development?
Web Frameworks like Django, Flask, Falcon, hug, etc are extremely popular for developing server-side (backend code) systems. These are required as they make it easier to incorporate complex business logic with the client-facing code and in a more secure, maintainable and scalable manner.
Benefits of using a Server-Side Framework
- This involves linking (and returning) of webpages in a complex manner on appropriate client (front-end or browser) requests; acting as an intermediary between databases and the client, or between any third system and the client.
- They abstract a lot of details while exposing functionality to the client (aka end-user). The need to just focus on what’s visible on the screen, like buttons, links, images; and not be bothered about how the content itself is generated, stored, linked or given access to. All that can be handled with ease by the backend frameworks
Python can be used in Data Science & Data Analysis
Data Science & Data Analysis is a broad term and they have various components as described below.
What is Machine learning?
Machine learning, at a high-level, is predicting recurring patterns in underlying observations, about virtually anything. The patterns could be in the typing speed of a person, the reaction time of a person, weather prediction, or even recognizing the object shown in a picture.
The Need for Machine Learning
One could argue that a programmer can write case statements by self-realizing the different scenarios and intuitively formulating rules for prediction. Implementing this high-level use case could in-fact take years of effort to improve the prediction performance seen. Writing a code that performs this by studying all the cases yourself, is time-consuming, error-prone and very tough to alter as variations are found.
On the other hand, machine learning libraries use fast iterative computations to realize patterns in the underlying test cases, at a much faster rate when the data set (samples) are high enough in number (millions to billions easily).
Uses of Machine Learning
Machine learning is being used by almost all of the big tech companies like Amazon, Google, Netflix, Microsoft, Facebook, Linkedin, Youtube, etc. Some of the use-cases are:
1. Recommendation Systems
Given a history of interaction with specific products (like videos, movies, etc), predict and showcase un-seen content which has a high chance of appreciation by the user. Amazon, Youtube, Netflix, etc.
2. Face Recognition
In a picture or a video frame, identify the person by identifying their facial features. Used by Facebook, by phones for unlocking by face recognition.
3. Voice Recognition
Identity the content during the speech, map its words in the appropriate language, and if required validate the person’s identity.
4. Search-Engine predictions
Used for finding the right content given a search string, by ranking all the available results in the order seemed most desirable by the user. Google, Bing, Yahoo are some of the companies using this.
5. Network Recognition Systems
These are complex systems, generally graph-database oriented, to find strong links between the underlying entities (currently mostly people). Facebook, LinkedIn, Instagram are few companies actively using this type of machine learning techniques.
Python for machine learning
The most popular libraries currently leading advancements in machine learning are Scikit-learn and TensorFlow. Between the two of them, they cover most of the popular Machine Learning and Data Science Algorithms.
Scripting and Automation
Python’s initial use-case and it’s most untold, but specific one is automation by scripting small utility scripts. You can automate many small tasks and save yourself time, energy and maybe lots of wasted motivation on mundane tasks.
Few use cases:
- Browser Automation
Selenium framework allows automating interactions with web-browsers and web-site. This could be used for automated web-site testing, for automating tasks done yourself, selecting a set of filters in a website, web-scraping, etc.
- Network & Command-line Automation
Python is also growingly being used for network automation. Some of the tasks used for quick time saving could be automatically establishing an SSL connection to a remote machine requiring two-factor authentication or even two layers of two-factor authenticated SSL connections.
Conclusion
Python is a powerful language, to stay and dominate the tech-industry for at-least a couple of years. Its importance and use-levels are only increasing and are the leader in innovation for fields ever-growing in number. It’s a skill to possess and to be maintained.
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