Code and Consult- what is python, Use case in Data analytics, pros and cons & Important libraries for data analytics.
Understanding python in a layman language, use case in data analytics, pros and cons, and important libraries for data analytics.
Arigato, hashnode community for this platform to impart knowledge and learnings within the developer community. Well, it's my first blog over this platform, I would like to be a keen observer of my growing knowledge from public policy and consulting to tech world of programming and the readiness of the same in the world of development.
Let's delve deeper into the concept of python, if I try to explain in the layman language then python is a dynamic programming language but think of it more as a tool that lets you impart instructions to the computer. Imagine python as your friendly assistant in this digital world, it's like having a magical pen that can make your computer do all the complex work for you irrespective of the quantity of the task. Python through the eyes of a developer can be expressed as a dynamic programming language, its design philosophy highlights code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. Conclusively it features a dynamic type of system and automatic memory management and has a large and comprehensive standard library.
Now let's delve deeper into use case of data analytics and its pros and cons-
think of data as a messy room filled with information, Python helps you become a cleaning wizard. It provides tools to organize and tidy up your data, making it easier to understand. Python does an excellent job at exploring the data, finding interesting trends, and answering questions hidden in the chaos.
Pros:
Readability: Codes of python are designed in such a way that they are human-readable, like writing a story. It's not just about getting the computer to understand; it's also about making it clear for other people to understand what you did.
Extensive Libraries: Python has a treasure trove of specialized tools called libraries. Each library is like a superpower for a specific task. Need to do complex math? NumPy is your go-to. Handling and analyzing data? Pandas has got your back. Machine learning? Scikit-Learn is the wizard you're looking for.
Community Support: Imagine you're on a quest, and whenever you face a challenge, there's a massive community ready to help. That's the Python community. Whether you're a beginner or an experienced coder, there's always someone willing to share knowledge, answer questions, and guide you through the adventure.
Cons:
Performance: Python might not be the fastest runner in some cases. While it's great for most tasks, for highly demanding operations, it might take a bit more time compared to languages like C++.
Global Interpreter Lock (GIL): In Python, there's a rule – only one operation can happen at a time, like a single person talking in a group. For certain tasks, this can slow things down.
Mobile Development: Python may not be the first choice if you're looking to build mobile applications. Other languages like Java or Swift are often preferred for that particular adventure.
Important Libraries for Data Analytics:
NumPy: It's like having a magical calculator for handling large amounts of numerical data with ease.
Pandas: Think of Pandas as your personal data butler, helping you clean, organize, and analyze your data effortlessly.
Matplotlib: Imagine being an artist who can draw beautiful charts and graphs to visually represent your data.
Seaborn: It's like having a stylist for your charts – making them look more elegant and appealing.
Scikit-Learn: If you want to teach your computer to learn from data and make predictions, this library is your knowledgeable guide.
TensorFlow and PyTorch: These are like the brains behind intelligent machines. They allow you to build and train complex models for tasks like image recognition or language understanding.
Statsmodels: Think of Statsmodels as your statistical advisor, helping you understand the significance of your findings and making sure they were not just a stroke of luck.
Unlike our cluttered lives, data should not be cluttered for a developer.
Signing off from my first blog at hoshnode, looking forward to delving deeper into this world.
Read me at Codeandconsult.myblog.dev