Creating Numpy Arrays and Checking Attributes | Codegnan | Data Analysis Tutorial | Part-6

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.

The NumPy library contains a multidimensional array and matrix data structures. 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.

If you already have Python, you can install NumPy with:

pip install numpy

In colab we give it as !pip install numPy as already most of the libraries are preinstalled in Google Colab. An array can be indexed by a tuple of nonnegative integers, by booleans, by another array, or by integers. The rank of the array is the number of dimensions. The shape of the array is a tuple of integers giving the size of the array along each dimension.
One way we can initialize NumPy arrays is from Python lists, using nested lists for two- or higher-dimensional data.

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