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Python Libraries

Popular Python libraries.

Python Language

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Fundamentals

  1. 1 Introduction
  2. 2 Setup Installation
  3. 3 Program Structure
  4. 4 Data Types & Variables
  5. 5 Reserved Keywords
  6. 6 Operators

Control Flow

  1. 7 Flow Control
  2. 8 if-else Statement
  3. 9 if-elif-else Statement
  4. 10 for-in Loop
  5. 11 while Loop
  6. 12 Jump Statements

String

  1. 13 Python String
  2. 14 String Manipulation

Functions

  1. 15 Function Calling
  2. 16 Function Arguments
  3. 17 Function Return
  4. 18 Lambda Function
  5. 19 Scope Variables

Data Structures

  1. 20 Lists: Methods & Slicing
  2. 21 Dictionaries: Operations
  3. 22 Tuples: Immutability
  4. 23 Sets: Operations

Modules

  1. 24 Importing Modules
  2. 25 Concept of pip
  3. 26 Commonly Used Libraries

Arrays

  1. 27 Array Module
  2. 28 Array Dimensions

Files

  1. 29 File I/O
  2. 30 Files Modifying
  3. 31 File Errors Handling

OOP

  1. 32 OOP Concepts

Machine Learning

  1. 33 Data Science
  2. 34 Deep Learning
  3. 35 Machine Learning
  4. 36 Build AI Models

Exception Handling

  1. 37 try-except-finally Blocks
  2. 38 Custom Exceptions

Asynchronous

  1. 39 async & await

QnA

  1. 40 Interview QnA

Commonly Used Libraries:

Python has a vast ecosystem of libraries covering various domains such as web development, data analysis, machine learning, natural language processing, and more. Here's a list of some commonly used Python libraries across different fields:

1. NumPy: Fundamental package for numerical computing with Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

2. Pandas: Provides high-performance data manipulation and analysis tools, especially for structured data. It offers data structures like DataFrame for efficient data handling.

3. Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations in Python. It's often used along with NumPy and Pandas for data visualization.

4. Scikit-learn: Simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and matplotlib. It provides simple and efficient tools for data mining and data analysis.

5. TensorFlow / PyTorch: Widely used deep learning libraries for building and training neural networks. TensorFlow and PyTorch are both capable of handling deep learning tasks, but they have different syntax and approaches.

6. Keras: High-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It allows for easy and fast experimentation with deep learning models.

7. Requests: An elegant and simple HTTP library for Python, allowing you to send HTTP/1.1 requests with Python. It's great for working with APIs and web services.

8. Beautiful Soup: A library for parsing HTML and XML documents. It provides tools for navigating, searching, and modifying the parse tree, making it useful for web scraping tasks.

9. Django / Flask: Web frameworks for developing web applications in Python. Django is a high-level framework that encourages rapid development and clean, pragmatic design. Flask is a lightweight framework that is more flexible and easy to get started with.

10. NLTK (Natural Language Toolkit): A leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources, such as WordNet.

11. SciPy: A collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds power to interactive computing and provides extensive functionality for tasks such as optimization, integration, interpolation, and more.

12. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

13. Pygame: A cross-platform set of Python modules designed for writing video games. It includes computer graphics and sound libraries designed to be used with the Python programming language.

14. OpenCV: A library of programming functions mainly aimed at real-time computer vision, providing tools for image processing, object detection, and more.

15. Plotly: A graphing library that makes interactive, publication-quality graphs online. It's particularly well-suited for creating web-based visualizations.

Note: These are just a few examples, and there are many more Python libraries available for various purposes. The choice of libraries depends on the specific requirements of your project and the domain you are working in.

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