An array is a central data structure of the NumPy library. An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element. It has a grid of elements that can be indexed in various ways. The elements are all of the same type, referred to as the array `dtype`

.

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.

For example:

`import numpy as np`

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

or:

`>>> a `**=** np**.**array**([[****1****,** **2****,** **3****,** **4****],** **[****5****,** **6****,** **7****,** **8****],** **[****9****,** **10****,** **11****,** **12****]])**

We can access the elements in the array using square brackets. When you’re accessing elements, remember that indexing in NumPy starts at 0. That means that if you want to access the first element in your array, you’ll be accessing element “0”.

`>>> print`**(**a**[****0****])**

[1 2 3 4]

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