Multi-dimensional Array in NumPy libray

Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing.

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.

What is a Python NumPy?

NumPy is a Python package which stands for ‘Numerical Python’. It is the core library for scientific computing, which contains a powerful n-dimensional array object, provide tools for integrating C, C++ etc.

It is also useful in linear algebra, random number capability etc. NumPy array can also be used as an efficient multi-dimensional container for generic data. Now, let me tell you what exactly is a python numpy array.

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NumPy Array:

Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize numpy arrays from nested Python lists and access it elements.

The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.

Let us see how it is implemented in PyCharm:

Single-dimensional Numpy Array:

import numpy as np

a=np.array([1,2,3])

print(a)

Output – [1 2 3]

Multi-dimensional Array:

a=np.array([(1,2,3),(4,5,6)])

print(a)

Output – [[ 1 2 3]

[4 5 6]]

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