In this section we will look at how to create numpy arrays with fixed content (such as all zeros).
We will first look at the
zeros function, that creates an array full of zeros. We will use that to see how to:
We will the look at some other fixed value functions:
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.]
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
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)
[[0 0] [0 0]]
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 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)
[[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
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
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)
[[ 7. 7. 7.] [ 7. 7. 7.]]
Again, there is also a
b1 = np.array([[1, 2], [3, 4]]) f2 = np.full_like(b1, 3) print(f2)
[[3 3] [3 3]]
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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