#PythonLibraries

[anandshivam2411]

Overview of Pandas vs. NumPy

Pandas and NumPy are two important tools in Python for working with data. While they may seem similar at first, they have different purposes and special features that make them helpful for various tasks.

NumPy is mainly used for handling numbers. It helps you work with large groups of numbers, like lists and arrays. With many built-in math functions, NumPy is great for doing complex calculations quickly and easily. This makes it popular among scientists, engineers, and anyone who needs to perform math operations on data. If you are doing tasks that require fast calculations, NumPy is the library to use.

On the other hand, Pandas is focused on data analysis and organization. It offers simple tools like Series and DataFrames, which let you work with organized data without much trouble. Pandas is excellent for cleaning, changing, and exploring data, especially when dealing with messy or incomplete information. You can easily filter, group, and visualize data, making it a favorite among data analysts and researchers.

You can use both libraries together to improve how you work with data. While NumPy provides speed and efficiency for calculations, Pandas gives you the tools to manage and analyze data well.

I recently read a blog that explains everything about Pandas and NumPy in an easy-to-understand way. I think everyone should check it out to learn how these libraries can help with data work.

[cleonixtechnical]

The Power of Python in Ecommerce Website Development

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Best Framework for Mobile/Android App Development in Python

Discover the pinnacle of mobile app development with Python. Our experts wield the best Python frameworks to create powerful, cross-platform applications. From Kivy to BeeWare, we harness the potential of Python for seamless and engaging mobile experiences.

[tutortacademy1]

Top Python Libraries for Data Science in 2024 | Tutort Academy

Python is one of the most popular programming languages used across various tech disciplines, especially in data science and machine learning. Python offers an easy-to-code, object-oriented, high-level language with a broad collection of libraries for a multitude of use cases. It has over 137,000 libraries

[fortunatelycoldengineer]

Python Design Pattern . . . visit: http://bit.ly/3iP8Inf for more information

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(via Youtube Short - Python Type Casting Tutorial for Beginners | Type Casting in Python Explained)

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TOP 10 PYTHON TOOLS PROGRAMMERS SHOULD KNOW IN 2022

Python is an excellent language for both beginners and experienced coders, which makes it the best choice for most development projects.

Table of Content

- Keras - Beautiful soup - Jupyter notebook - Theano - Scikit-learn - Sublime Text - Sphinx - Pip - LXML - Selenium Python is very popular with programmers and is the most sought-after language around the globe. Because of this, demand has been increasing at a rapid pace in recent times. Python is an excellent language for both beginners and experienced coders, which makes it the best choice for most development projects. To become a skilled Python programmer, it's important to know the available Python tools. As you read this article, review the most popular 10 must-known Python tools programmers should know in 2022.

1. Keras

Keras is an open-source tool that provides a Python interface for artificial neural networks. It is free and modular, with the ability to reuse code across multiple projects. Other advantages of this framework are that it supports machine learning development with TensorFlow, CNTK and Theano. Keras is a machine learning library that has the capability to emulate the human brain. It also simplifies the creation of deep neural networks and currently supports TensorFlow, Microsoft's Cognitive Toolkit, Theano, and PlaidML among other frameworks.

2. Beautiful soup

Beautiful Soup is a Python package for parsing, finding, and editing parse trees that really take advantage of certain Python features. Convert documents automatically with our new tool. This can save you hours of work & means you don't have to keep answering the same questions again and again.

3. Jupyter notebook

Jupyter Notebook is a favourite destination for data scientists and machine learning engineers, as it helps you write and execute code quickly. All you have to do is run one cell to try it out. Additionally, this Python tool will be one of the best options to consider when it comes to 3D modelling.

4. Theano

If you need to do some mathematical computation and it involves many dimensions, then Theano is perfect for you. It can handle differentiation, different types of computations, testing etc.

5. Scikit-learn

One of the best free Python tools is Scikit-learn. It has features like regression, classification and clustering, which make it invaluable for data analysis models.

6. Sublime Text

Sublime Text is one of those free Python tools that can be used to develop code. It has plugins that you can add to your editor, which provide a range of features, such as auto-complete and much more.

7. Sphinx

Documentation is like a puzzle where you need the right pieces to make it work. Sphinx, a Python tool, can generate numerous files using just a few pieces of information about your project or company. The HTML file for instance gathers all your documentation concerning code and can be browsed in any machine with an Internet connection.

8. Pip

Getting started with Pip is easy. All that you have to do is download it and install it later, preferably with a sound internet connection.

9. LXML

LXML is a Python library specifically for handling XML documents, written in the C programming language. It supports both libxml2 and libxslt (the XML libraries from which it was originally extracted) and has no shortage of features that are missing from other Python libraries. One such example is its support for XPath expressions, without any need to configure the Expressions component explicitly

10. Selenium

Selenium is an open-source automation framework. One of its features is a tool for creating applications and databases. Thus, it can be used widely for computer applications such as computer-assisted learning or multimedia products. Using this helpful tool, you can now test scripts in a number of different coding languages – from Java to Python, Ruby and more! I hope you will like the content and it will help you to learn TOP 10 PYTHON TOOLS PROGRAMMERS SHOULD KNOW IN 2022 If you like this content, do share. Read the full article

[arshikasingh]

Standard Libraries of Python

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[zenesys]

Top 10 Python Libraries for Machine Learning

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Machine learning is the science of programming computers by which they are able to learn from data. In this article we discuss on the top Python libraries used to perform machine learning tasks.

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[fortunatelycoldengineer]

Python Turtle Programming . . . visit: http://bit.ly/3kmuyyR for more information