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Python Coding using NumPy Library
Preface
I have already published three digital books on Python basics in Amazon Kindle edition.
They are 1. Python Coding for Beginners
2. Python Mini Projects
3. Python Coding with Tkinter library
Python use various libraries for various operations. Each library has its own merit and advantages in specific operations. Main libraries of Python are
1. NumPy 2. Pandas 3. Matplotlib 4. PyTorch 5. Scikit-learn
6. SciPy 7. TensorFlow 8. Keras 9. Beautiful Soup
10. Seaborn 11. Requests 12. Scrapy 13. Theano
14. PyBrain 15. Django 16. Flask 17. Pygame 18. XGBoost
20. CatBoost 20. Gensim 21. Pipenv 22. Plotly
In this book I will go ahead with Python using with Numpy
Numpy is a Python library used for working mainly with arrays. It also Has functions for working in domain of linear algebra, Fourier transforms, and matrices. Numpy was created in 2005 by Travis Oliphant. It is an open-source project and you can use it freely.
Scientist use NumPy to analyse experimental data, perform statistical analysis, and generate models.
Advantages of
Memory Efficienc Memory efficiency is essential when dealing with Extensive datasets and enables faster data access and manipulation.
Mathematical functions: NumPy’s efficient array operations and functions make it easy for Python scientific computing.
Data manipulation: NumPy provides powerful tools for data analysis and manipulation.
Ease of NumPy is easy to use and learn
NumPy arrays: offer several advantages over traditional Python lists.
Python lists: Numpy is optimised for speed and efficiency. NumPy arrays are implemented in C, which makes them faster than other Python lists.
Broadcasting: allows NumPy to operate on arrays to operate on arrays of different dimensions and shapes without the need for explicit loops.
Integration with other libraries: NumPy integrate with other Python libraries, such as Pandas, SciPy, and Matplotlib.
Simplicit NumPy’s intuitive syntax and vectorized operations simplify complex numerical computation, reducing the need for explicit looping
NumPy’s ndarray computing concepts are at the core of the scientific Python and PyData ecosystems.
NumPy provides Python front-end for highly optimized C functions allowing for a simple Python interface with the speed of compiled code.
ChaptersInstallation of Anaconda for installation of NumPy and MatplotlibCreating ArraysAttributesEmpty Arrays, Arrays with ones, & range of elementsSorting and Concatenating ArraysDimensions and Number of ElementsShaping ArraysSlicing and Indexing of ArraysFunctions vstack() and hstack()Splitting ArraysBasic Array OperationsBroadcastingCreating MatricesFunctions ones() and zeros()Unique elements, indices values, and occurrence countconver
I have already published three digital books on Python basics in Amazon Kindle edition.
They are 1. Python Coding for Beginners
2. Python Mini Projects
3. Python Coding with Tkinter library
Python use various libraries for various operations. Each library has its own merit and advantages in specific operations. Main libraries of Python are
1. NumPy 2. Pandas 3. Matplotlib 4. PyTorch 5. Scikit-learn
6. SciPy 7. TensorFlow 8. Keras 9. Beautiful Soup
10. Seaborn 11. Requests 12. Scrapy 13. Theano
14. PyBrain 15. Django 16. Flask 17. Pygame 18. XGBoost
20. CatBoost 20. Gensim 21. Pipenv 22. Plotly
In this book I will go ahead with Python using with Numpy
Numpy is a Python library used for working mainly with arrays. It also Has functions for working in domain of linear algebra, Fourier transforms, and matrices. Numpy was created in 2005 by Travis Oliphant. It is an open-source project and you can use it freely.
Scientist use NumPy to analyse experimental data, perform statistical analysis, and generate models.
Advantages of
Memory Efficienc Memory efficiency is essential when dealing with Extensive datasets and enables faster data access and manipulation.
Mathematical functions: NumPy’s efficient array operations and functions make it easy for Python scientific computing.
Data manipulation: NumPy provides powerful tools for data analysis and manipulation.
Ease of NumPy is easy to use and learn
NumPy arrays: offer several advantages over traditional Python lists.
Python lists: Numpy is optimised for speed and efficiency. NumPy arrays are implemented in C, which makes them faster than other Python lists.
Broadcasting: allows NumPy to operate on arrays to operate on arrays of different dimensions and shapes without the need for explicit loops.
Integration with other libraries: NumPy integrate with other Python libraries, such as Pandas, SciPy, and Matplotlib.
Simplicit NumPy’s intuitive syntax and vectorized operations simplify complex numerical computation, reducing the need for explicit looping
NumPy’s ndarray computing concepts are at the core of the scientific Python and PyData ecosystems.
NumPy provides Python front-end for highly optimized C functions allowing for a simple Python interface with the speed of compiled code.
ChaptersInstallation of Anaconda for installation of NumPy and MatplotlibCreating ArraysAttributesEmpty Arrays, Arrays with ones, & range of elementsSorting and Concatenating ArraysDimensions and Number of ElementsShaping ArraysSlicing and Indexing of ArraysFunctions vstack() and hstack()Splitting ArraysBasic Array OperationsBroadcastingCreating MatricesFunctions ones() and zeros()Unique elements, indices values, and occurrence countconver
74 pages, Kindle Edition
Published August 15, 2025
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