# Creating numpy arrays with fixed values

Martin McBride, 2019-09-15
Tags arrays data types
Categories numpy In this section we will look at how to create numpy arrays with fixed content (such as all zeros).

Here is a video covering this topic:

We will first look at the `zeros` function, that creates an array full of zeros. We will use that to see how to:

• Create arrays of different shapes.
• Create arrays using different data types (such as floats and ints).
• Create like arrays (arrays that copy the shape and type of another array).

We will the look at some other fixed value functions: `ones`, `full`, `empty`, `identity`.

## 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 (see data types).

```[ 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:

```a3 = np.zeros((2, 2), dtype=np.int32)
print(a3)
```

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:

```b1 = np.array([[1, 2], [3, 4]])
c1 = np.zeros_like(a2_ints)
```

`c1` 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:

```o1 = np.ones((3, 2))
print(o1)
```

gives:

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

You can choose the select the type, in the same way as `zeros`. 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:

```f1 = np.full((2, 3), 7)
print(f1)
```

Giving:

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

Again, there is also a `full_like` function:

```b1 = np.array([[1, 2], [3, 4]])
f2 = np.full_like(b1, 3)
print(f2)
```

giving

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

## identity

The `identity` function creates an identity matrix. This is always a square, 2-dimensional array. You just need to specify the size and optional type:

```i = np.identity(3)
```

This creates a 3 by 3 array with the diagonal elements set to 1 and everything else set to 0:

```[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
```

You can also checkout the `eye` function, which also creates a diagonal matrix, but with extra control (for example, creating a non-square array).

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If you found this article useful, you might be interested in the book NumPy Recipes or other books by the same author.

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