Numpy python Tutorial| Libraries in Python|Numpy tutorial for beginners|Learn numpy|Codewithtuqi

how to install NumPy in #jupyternotebook?
!pip install NumPy
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#NumPy (Numerical Python) is an open-source #Python #library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.
It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. NumPy can be used to perform a wide variety of mathematical operations on arrays. It adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices.
why we use numpy?
NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further
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import numpy as np
a = np.array([1, 2, 3, 4, 5, 6])
How to create a basic array
np.array(), np.zeros(), np.ones(), np.empty(), np.arange()
import numpy as np
a = np.array([1, 2, 3])
np.zeros(2)
array([0., 0.])
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np.arange(4)
array([0, 1, 2, 3])
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