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NumPy - 2.1
- np.array()
- np.asarray()
- np.zeros()
- np.ones()
- np.empty()
- np.arange()
- np.linspace()
- np.eye()
- np.full()
- np.fromfunction()
- np.empty_like()
- np.identity()
- np.ones_like()
- np.zeros_like()
- np.full_like()
- np.frombuffer()
- np.from_dlpack()
- np.fromfile()
- np.fromiter()
- np.fromstring()
- np.loadtxt()
- np.rec.array()
- np.logspace()
- np.geomspace()
- np.meshgrid()
- np.mgrid()
- np.ogrid()
- np.diag()
- np.diagflat()
- np.tri()
- np.tril()
- np.triu()
- np.vander()
NumPy | Getting Started
A library in Python used for working with numbers and data.
NumPy helps you create arrays that can hold many numbers in a structured way, allowing for easy calculations.
NumPy is much faster than regular Python lists, especially when working with large amounts of data.
What is NumPy?
N-dimensional Arrays: NumPy introduces a data structure called the ndarray (n-dimensional array), which allows you to store and manipulate large arrays of data efficiently. These arrays can be one-dimensional (like lists), two-dimensional (like matrices), or multi-dimensional.
Performance: NumPy is optimized for speed and performance. It is written in C, which makes it much faster for numerical computations compared to using standard Python lists.
Interoperability: It works well with other libraries like Pandas (for data manipulation), Matplotlib (for plotting and visualization), and SciPy (for additional scientific computing tools).
Why use NumPy?
Vectorization: NumPy allows you to perform operations on entire arrays without writing loops, making your code cleaner and faster.
Broadcasting: The broadcasting feature allows for operations on arrays of different shapes, making it easier to perform calculations without manual adjustments.
Matrices and Tensors: Ideal for handling data in multiple dimensions, which is essential in fields like machine learning (e.g., for neural networks) and image processing.