Python NumPy Tutorial 8 - Copy vs View in NumPy Array
Python NumPy Tutorial 8 - Copy vs View in NumPy Array
In this video by Programming for beginners we will see Copy vs View in NumPy Array Library for beginners. This video series will help you to learn NumPy library used for machine learning, data science and artificial intelligence (AI ML). We will see many examples and projects related to Machine learning and data science in upcoming videos.
Copy of an array is another array, and changes made in original are NOT reflected in copy array
View of an array is the view of the original array, and changes made in original are reflected in view array
Copy of an array is another array that does not impact the original array
View of an array is the view of the original array, and changes made in view are reflected in original array
Examples:
arr1 = np.array([1,2,3])
copy = arr1.copy()
view = arr1.view()
base property is used to know if the array is a copy or a view
copy owns the data, where as view does not own the data
So copy returns none when it owns the data for the base property
The base property returns the original object for the view created
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NumPy, short for Numerical Python, is a fundamental library in Python for numerical and scientific computing. It provides support for multi-dimensional arrays, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is widely used in data analysis, machine learning, and scientific research due to its performance and ease of use.
At the core of NumPy is the ndarray, a homogeneous multi-dimensional array that allows for efficient storage and manipulation of large datasets. NumPy arrays are significantly faster than Python lists for numerical operations because they are implemented in C and optimized for performance.
Key features of NumPy include:
Efficient array operations:
NumPy provides a wide range of vectorized operations that can be applied to entire arrays without the need for explicit loops.
Broadcasting:
NumPy allows operations between arrays of different shapes, making it easier to perform calculations on data with varying dimensions.
Mathematical functions:
NumPy includes a rich set of mathematical functions for linear algebra, Fourier analysis, random number generation, and more.
Integration with other libraries:
NumPy is a core dependency for many other scientific computing libraries in Python, such as Pandas, SciPy, and scikit-learn.
Open source:
NumPy is free and open-source, with a large and active community of developers and users.
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#NumPyTutorial #MachineLearning #DataScience
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