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This getting started exercise introduces the `np` package, which is similar to Numpy. For more information, please see [Differences between NP on MXNet and NumPy](/api/python/docs/tutorials/getting-started/np/np-vs-numpy.html).
To get started, run the following commands to import the `np` package together with the NumPy extensions package `npx`. Together, `np` with `npx` make up the NP on MXNet front end.
In this step, create a 2D array (also called a matrix). The following code example creates a matrix with values from two sets of numbers: 1, 2, 3 and 4, 5, 6. This might also be referred to as a tuple of a tuple of integers.
You can create arrays whose values are sampled randomly. For example, sampling values uniformly between -1 and 1. The following code example creates the same shape, but with random sampling.
As with NumPy, the dimensions of each ndarray are shown by accessing the `.shape` attribute. As the following code example shows, you can also query for `size`, which is equal to the product of the components of the shape. In addition, `.dtype` tells the data type of the stored values.
An ndarray supports a large number of standard mathematical operations. Here are three examples. You can perform element-wise multiplication by using the following code example.
The ndarrays support slicing in many ways you might want to access your data. The following code example shows how to read a particular element, which returns a 1D array with shape `(1,)`.
