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3.6 NumPy 1: 1D and 2D NumPy Arrays

NumPy is our Linear Algebra or Matrix Algebra library. We will soon get into its functionalities - it has a lot of them -, but here we are going to discuss a NumPy array. A NumPy array is used in a lot of plotting functionalities, and we'll usually convert data from a Pandas Dataframe to a NumPy array do more of the core ML part.

Converting Other Structures to NumPy Arrays

First of all, let's consider a list.

a = [1, 2, 3, 4, 5]

We can make this a NumPy array by the following function call:

numpya = np.array(a)

When we want to convert a dataframe column to a NumPy Array, we can use the np.asanyarray function:

numpyArray = np.asanyarray(df[["col"]])

In-built Values

If a is a numpyArray:

a.size

produces the number of elements of the numpy array,

a.shape

produces a tuple with the size in each dimension

Indexing/Slicing

The NumPy array has a fixed size, but we can change the already existing elements of a numpy array. Let's take one called numpyArray.

numpyArray[n] = m

puts the value m into the nth index in numpyArray.

Additionally, if you input a list of values in place of the number n, you can produce a NumPy array that contains the values at those indices (plural for index).

numpyArray[1,2,3] = [a1, a2, a3]

Where a1, a2 and a3 are respectively the elements of numpyArray at the indices 1, 2 and 3.

arange

np.arange(a,b)

produces a list of integers between a and b, exclusive of b.

np.arange(1,5)

: 1,2,3,4

2D NumPy Arrays

2D NumPy Arrays are a lot like 1D ones. It's just that they are created in the following way:

image

is created by:

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

In essence, we create the corresponding 2D list, and then convert them to NumPy arrays.

In the next Lesson, we'll discuss the useful operations using NumPy arrays provides.

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