CBSE Class 10th AI (Artificial Intelleigence) | 15. Python Packages by Abraham | Learn Smarter
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15. Python Packages

Python packages play a crucial role in organizing and reusing code efficiently. They allow developers to access a wide range of powerful tools and libraries that enhance productivity in Python programming, particularly in fields like Artificial Intelligence and Data Science. Additionally, creating custom packages helps streamline code management, ensuring better organization and reusability.

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Sections

  • 15

    Python Packages

    Python packages facilitate code reusability and modularity, making programming more efficient.

    Learning Practice

  • 15.1

    Introduction

    This section introduces Python packages, explaining their importance in programming efficiency and code organization.

    Learning Practice

  • 15.2

    What Are Python Packages?

    Python packages are collections of modules that help organize and manage code more efficiently.

    Learning Practice

  • 15.2.1

    Structure Of A Package

    The structure of a Python package consists of a directory containing an __init__.py file and multiple modules, facilitating efficient code organization and reuse.

    Learning Practice

  • 15.2.2

    Difference Between Module And Package

    This section explains the key differences between a Python module and a package.

    Learning Practice

  • 15.3

    Installing Python Packages

    This section explains how to install Python packages using pip, allowing users to enhance their Python programming environment with external libraries.

    Learning Practice

  • 15.4

    Importing And Using Python Packages

    This section explains how to import and use Python packages in your code.

    Learning Practice

  • 15.5

    Common Python Packages In Ai

    This section covers the most commonly used Python packages in Artificial Intelligence, outlining their functionalities and usage.

    Learning Practice

  • 15.5.1

    Numpy

    NumPy is a Python library used for numerical operations and array handling, excelling in speed and efficiency for mathematical computations.

    Learning Practice

  • 15.5.2

    Pandas

    Pandas is a powerful library in Python used for data manipulation and analysis, especially with tabular data formats.

    Learning Practice

  • 15.5.3

    Matplotlib

    Matplotlib is a powerful Python library used for data visualization, enabling the creation of a variety of static, animated, and interactive plots.

    Learning Practice

  • 15.5.4

    Scikit-Learn

    Scikit-learn is a powerful Python library that provides essential tools for machine learning applications, including classification and regression.

    Learning Practice

  • 15.5.5

    Tensorflow / Pytorch

    TensorFlow and PyTorch are advanced libraries primarily used for deep learning and building neural networks.

    Learning Practice

  • 15.6

    Creating Your Own Python Package (Basic)

    This section outlines the steps to create your own Python package, emphasizing the folder structure and key components.

    Learning Practice

  • 15.7

    Best Practices With Packages

    This section covers best practices for using Python packages, emphasizing virtual environments, meaningful aliases, and organization.

    Learning Practice

References

Chapter_15_Pytho.pdf

Class Notes

Memorization

What we have learnt

  • A Python package is a colle...
  • Pip is the standard tool fo...
  • You can import packages int...

Final Test

Revision Tests