- Converting Other Structures to NumPy Arrays
- In-built Values
- Indexing/Slicing
- arange
- 2D NumPy Arrays
- Previous Section
- Next Section

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:

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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