Python Libraries: Definition, Types, and Most Popular Ones.

In a series of programming tutorials, especially focusing on the Python language, we learned about the Python language in general, understood the basics of code, and classified the language type. Today, we will talk about the best libraries for the "Python" language.

Definition of Libraries in Python

In the beginning, the definition of Python language libraries: They are a collection of pre-written functions and tools that can be used to expand the capabilities of the language and simplify programming tasks.

Libraries are an essential part of the Python environment and make it easier for developers to create advanced applications quickly. Libraries provide specific services, so each library has its own set of services, codes, and features different from other libraries. This means that Python consists of many libraries, each designed for a specific task.

Types of Python Libraries

There are three types of libraries in the Python language:

1.Standard Libraries

These libraries are developed by external groups or organizations, meaning individuals not affiliated with the official Python company. These libraries are developed to provide diverse tools to help solve a wide range of problems. Examples include NumPy, Pandas, Scikit-learn, and Requests.

2.External Libraries

These libraries are a basic part of Python as they come bundled with the language. These libraries provide developers with essential and important functions, such as mathematical operations and file handling. Examples include os, math, datetime, and random.

3.Custom Libraries

Python allows developers to create their own libraries for their applications in case of similar codes or code reuse.

Most Popular Python Libraries

1.Pandas: For data processing and analysis, such as reading from and writing to various sources like CSV files and Excel. 2.Matplotlib: For plotting data and creating professional and interactive visualizations. 3.Scikit-learn: For machine learning and deep learning, offering a variety of algorithms and tools for data analysis and building predictive models. 4.TensorFlow and PyTorch: For building and training deep learning models, such as artificial neural networks. 5.Flask and Django: For developing web applications using Python, each offering different approaches to application development based on needs. 6.Requests: For making HTTP requests easily, such as fetching web content or accessing application programming interfaces (APIs).

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