# Python NumPy For Your Grandma - 2.3 Creating NumPy Arrays

In this secion, we’ll look at different ways to create a NumPy array.

As we saw earlier, you can make an array by typing np dot array and then passing in a list of values.

```
import numpy as np
np.array(['a', 'b', 'c'])
## array(['a', 'b', 'c'], dtype='<U1')
```

You can also make a 2-dimensional array from a list of lists like this.

```
np.array([
['a', 'b'],
['c', 'd'],
['e', 'f']
])
## array([['a', 'b'],
## ['c', 'd'],
## ['e', 'f']], dtype='<U1')
```

You can also make a three dimensional array from a list of lists of lists.

```
np.array([
[
['a', 'b'],
['c', 'd'],
['e', 'f']
],
[
['g', 'h'],
['i', 'j'],
['k', 'l']
]
])
## array([[['a', 'b'],
## ['c', 'd'],
## ['e', 'f']],
##
## [['g', 'h'],
## ['i', 'j'],
## ['k', 'l']]], dtype='<U1')
```

… and so on.

Now let’s say you want to make a three by five array filled with zeros. This’ll be a good time so check out the NumPy documentation. It turns out, there’s a function called `zeros()`

that does exactly what we want. The only argument that doesn’t have a default value is `shape`

, so we’ll need to pass in the `shape`

as an int or tuple of ints.

If we call `np.zeros(shape = 3)`

we’ll get back a 1d array with three zeros.

```
np.zeros(shape = 3)
## array([0., 0., 0.])
```

But, our goal was to get a three by five array so I’m gonna set the shape to a tuple like this.

```
np.zeros(shape = (3,5))
## array([[0., 0., 0., 0., 0.],
## [0., 0., 0., 0., 0.],
## [0., 0., 0., 0., 0.]])
```

Now, what about the generic case? Say, instead of zeros you want a three by five array filled with the word ‘cat’. For this, we can use a function called `full()`

. We just need to pass in a `shape`

and `fill_value.`

I’ll do that here.

```
np.full(shape = (3,5), fill_value = 'cat')
## array([['cat', 'cat', 'cat', 'cat', 'cat'],
## ['cat', 'cat', 'cat', 'cat', 'cat'],
## ['cat', 'cat', 'cat', 'cat', 'cat']], dtype='<U3')
```

Another very common array to build is a sequence array like `[1,2,3,4]`

. The function for this is `np.arange()`

where you pass in `start`

, `stop`

, and `step`

values. For example,

```
np.arange(start = 1, stop = 5, step = 1)
## array([1, 2, 3, 4])
```

(Note that start is inclusive while stop is exclusive.)

Alternatively, if you just do `np.arange()`

and pass in an integer like `10`

, you’ll get back a sequence of ten integers starting from zero.

```
np.arange(10)
## array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
```

By the way, the name “arange” stands for “array range” because it’s like the array version of a python range.

Another useful function is `np.random.randint()`

which let’s you build an array of random integers selected with replacement. Here we’ll make a two by three array of random ints between one (inclusive) and seven (exclusive). You can think of this as two iterations of rolling three dice.

```
np.random.randint(low = 1, high = 7, size = (2, 3))
## array([[5, 4, 1],
## [6, 1, 6]])
```

Obviously there’s a bunch of other methods to build arrays, and we’ll see a lot of them later the course. But hopefully this was enough to get your feet wet and introduce you to the NumPy documentation.

## Course Curriculum

**Introduction**

1.1 Introduction**Basic Array Stuff**

2.1 NumPy Array Motivation

2.2 NumPy Array Basics

2.3 Creating NumPy Arrays

2.4 Indexing 1-D Arrays

2.5 Indexing Multidimensional Arrays

2.6 Basic Math On Arrays

2.7 Challenge: High School Reunion

2.8 Challenge: Gold Miner

2.9 Challenge: Chic-fil-A**Intermediate Array Stuff**

3.1 Broadcasting

3.2 newaxis

3.3`reshape()`

3.4 Boolean Indexing

3.5 nan

3.6 infinity

3.7 random

3.8 Challenge: Love Distance

3.9 Challenge: Professor Prick

3.10 Challenge: Psycho Parent**Common Operations**

4.1`where()`

4.2 Math Functions

4.3`all()`

and`any()`

4.4`concatenate()`

4.5 Stacking

4.6 Sorting

4.7`unique()`

4.8 Challenge: Movie Ratings

4.9 Challenge: Big Fish

4.10 Challenge: Taco Truck**Advanced Array Stuff**

5.1 Advanced Array Indexing

5.2 View vs Copy

5.3 Challenge: Population Verification

5.4 Challenge: Prime Locations

5.5 Challenge: The Game of Doors

5.6 Challenge: Peanut Butter**Final Boss**

6.1`as_strided()`

6.2`einsum()`

6.3 Challenge: One-Hot-Encoding

6.4 Challenge: Cumulative Rainfall

6.5 Challenge: Table Tennis

6.6 Challenge: Where’s Waldo

6.7 Challenge: Outer Product