## Creating numpy arrays

In this section we will look at how to create numpy arrays. This article shows various ways to create arrays with constant content (such as all zeros), the next article shows how to create arrays with varying data content.

## zeros function

The **zeros** function creates a new array containing zeros. For example:

```
import numpy as np
a1 = np.zeros(4)
print(a1)
```

This will create `a1`

, one dimensional array of length 4. By default the array will contain data of type `float64`

, ie a double float.

```
[ 0. 0. 0. 0.]
```

## Creating zero arrays of different shape

The shape of an array specifies the number of dimensions and the size of the array in each dimension. For example a shape of `(2, 3)`

specifies a 2 dimensional array consisting of 2 rows of 3 columns:

```
a2 = np.zeros((2, 3))
print(a2)
```

Here is the output:

```
[[ 0. 0. 0.]
[ 0. 0. 0.]]
```

The shape of the array is often specified by a tuple eg `(2, 3)`

. Of course you can use a list, or any other sequence of integers (even another numpy array if you want to).

For one dimensional arrays, you can optionally use a single integer, rather than a tuple of one item - this is what we did when we created `a1`

above.

## Setting the data type

By default, the array is created with a data type of `float64`

. You can use the optional parameter `dtype`

to specify a different data type. For a numpy array, all the elements must be the same type.

In the code below, `a2_ints`

is an integer array. See the article on data types for a full list of data types:

```
a2_ints = np.zeros((2, 2), dtype=np.int32)
print(a2_ints)
```

This gives:

```
[[0 0]
[0 0]]
```

## zeros_like

The `zeros_like`

function will create a new array of zeros that is the same shape and data type as the supplied array:

```
b2_ints = np.zeros_like(a2_ints)
```

`b2_ints`

now contains a new 2 by 2 array of integer zeros.

## ones

The `ones`

function is exactly the same as `zeros`

, except that it fills the array with value 1. For example:

```
o2 = np.ones((3, 2))
print(o2)
```

gives:

```
[[1. 1.]
[1. 1.]
[1. 1.]]
```

There is also a `ones_like`

function that works in an analogous way to `zeros_like`

.

## empty

The `empty`

function is also similar to `zeros`

, except that it leaves the data array uninitialised. `empty`

might be slightly faster than `zeros`

, so it can be used if you are intending to immediately initialise the array in some other way.

Bear in mind that the uninitialised memory might not be random. It will just be whatever happens to be in the memory, so it might potentially contain user data left behind from the last time the memory was used.

There is, of course, a corresponding `empty_like`

function.

## full

The `full`

function takes an extra parameter, the fill value, and uses that value to initialise the array elements:

```
f2 = np.full((2, 3), 7)
print(f2)
```

Giving:

```
[[ 7. 7. 7.]
[ 7. 7. 7.]]
```

Again, there is also a `full_like`

function, using our previous `a2`

array:

```
g2 = np.full_like(a2, 3)
print(g2)
```

giving

```
[[3. 3. 3.]
[3. 3. 3.]]
```