4 Reasons You Should Learn Python
4 Reasons You Should Learn Python
There are lots of reasons to learn Python — such as its simplicity and readability — but these are some of the most compelling.
Join the DZone community and get the full member experience.Join For Free
Have you already learned Python? If not, then this is the best time to start. Python is a very popular programming language being used by many big tech giants, including Google and Facebook. Python is well-known for its simplicity and readability. Also, the availability of a wide range of mature libraries (Python modules) makes Python top choice for almost all kinds of software development (yes, including AI and machine learning). In this blog post, I will discuss the benefits of learning Python.
There are lots of reasons to learn Python, but the most compelling ones I found are the following.
1. Fast Learning Curve and Reduced Development Time
If you are new to software development, then Python is the best programming language to pick. Python is much easier to learn than most other popular object-oriented programming languages (like Java). Language syntax and semantics in Python are much more clean and neat compared to other languages. The code is much more readable, as well. Lines of code need to be written in Python to solve specific problems. In most cases, this will be much less code than is required in other popular languages. Modular architecture in Python makes it easy to just import and use a module instead of writing a large block of code. A mature ecosystem of modules is already available for your use (I will explain how to import and use modules in Python in my future posts). Thus, the code looks clean and readable. More readable the code is, the easier it will be for other developers to maintain it. Also, development time for your project will reduce as you have to write and maintain fewer lines of code. This will enable you to market your product sooner.
2. Automating Your Daily Repetitive Tasks
Every one of us remains very busy throughout a typical work day, trying to complete all of the different types of tasks assigned to us. The majority of the jobs assigned to us are not cognitively demanding. With "cognitively demanding," I'm referring to the tasks that need decision-making that can be replicated, i.e. that is repetitive in nature. For example, a software project manager might need to produce a report on testing status every day, which involves downloading the test case execution report and defect list from different cloud-based platforms and then combining that information to generate different summary reports. For this task, the task of generating the summary reports in a specified format is well-defined and anybody can replicate the steps if you train them for a few days. Part of this task that will need cognitive input is to do the risk assessment based on current status and specifying mitigation actions.
In Python, you can write a script to do all these things automatically, from downloading reports in CSV/Excel format from different sources to creating a consolidated Excel report in the specified format. You can even schedule the script to execute at defined times and mail it to your email address. This is just an example. A software developer can scan for a requirement reference from a list of Word or Excel documents or can compare the change in the requirement in different document version. Both of these tasks are time-consuming if done manually.
3. Popular Choice for Data Science and Analytics
Python is one of the most sought-after programming languages for data science and analytics. The availability of mature and feature-rich modules for Python makes it easier to support data analysis in an efficient manner. Some of the popular modules in the analytics stack used for different purposes are described below for your reference:
- NumPy: This module provides a huge library of mathematical functions along with support for multidimensional arrays, which is essential in data science. This module provides numerical arrays and matrix data types and functions to operate on and get insight from that data.
- SciPy: This utility provides a collection of algorithms that can be used for different domain-specific data processing. This includes statistical algorithms, digital signal processing algorithms, and more. See the official website for SciPy for more details and official documentation.
- matplotlib: This is a popular plotting package that can be used for 2D as well as basic 3D plotting. This package is important as data visualization is very important in data science.
- pandas: This is an open-source and easy-to-use library that provides high-performance data structures and data analysis tools.
4. Future of AI and Machine Learning
Machine learning is everywhere, from self-driving cars to deep learning AI that can beat the best human players in complex strategy games. The list goes on and on. New possibilities are virtually endless. Machine learning algorithms that you develop may revolutionize some industry! Python is the language of choice for machine learning projects and AI-related projects. Many big tech companies, including Google, use Python for machine learning projects. Here are some popular modules and frameworks for machine learning:
- Tensorflow: This is a neural network library used for programming neural network architectures. It was developed by Google and is currently being used by a large number of developers working on machine learning projects. Tensorflow provides high-level functions so that you don’t have to reinvent the wheel. It has a flexible architecture that will allow you to create meaningful solutions.
- Scikit-learn: This is also a popular ML library built on NumPy and SciPy that is used extensively by developers. This is a comparatively low-level library and is used to develop other popular high-level libraries.
- Theano: This is also a popular Python library that is used to work with multi -dimensional arrays effectively. Some of the best features of Theano are tight integration with NumPy and the transparent use of GPU.
That’s all for this post. I have plans to post more Python-related topics in coming weeks, so keep an eye out.
Published at DZone with permission of Arani Chatterjee . See the original article here.
Opinions expressed by DZone contributors are their own.