There are many ways of creating Numpy arrays that can contain any number of elements. Let’s start with the simplest one: an array of zero dimensions (0d), which contains a single element of integer data type (dtype).

arr = np.array(4)

Here we use the np.array function to initialize our array with a single argument ( 4 ). The result is an array that contains just one array object: 4. That’s simple enough, but not very useful.

We can create a regular one-dimensional array (1D) by giving the np.array function a list as an argument.

arr = np.array([1,2,3,4])

Or

arr = np.array((1, 2, 3, 4))

Both of which give us the following new array:

[1 2 3 4]

Note that the second example uses a tuple data structure as an argument, which is also a syntax that works.

We can create a numpy array with any number of dimensions (multidimensional arrays, denoted numpy.ndarray) we want simply by giving it an argument of that dimension (2D array, 3D array…nD array).

For example, to create a two-dimensional array (2D) with:

arr = np.array([[1,2],[3,4]])

Or

arr = np.array(((1,2),(3,4)))

Both of which give us the following 2D array:

[[1 2]

[3 4]]

Another functionality of np.array function allows us to create any kind of numpy array with any dimension without specifically providing that dimensioned array as an argument. For example, we can create a 5-dimensional Numpy Array from just a regular 1d array, effectively reshaping it.

arr = np.array([1, 2, 3, 4], ndmin=2)
print(arr.ndim) 
print(arr.shape)

Here the print statement will print 2 as the dimension, and our array will be a 2-dimensional array with only the given list array data. The print statement printing the shape will print (1,4) as the rest of the array is empty (ie., np.empty).

The output looks like this:

2

(1, 4)

There are some intrinsic functions numpy provides for easy array creation, as well. Let’s take a look at the “zeros” and “ones” (bool) functions:

arr = np.zeros((2,2))
arr = np.ones((2,2))

The above code snippet will create two different resulting arrays:

• The first array will contain only zeros in a 2×2 array
  [[0 0]

 [0 0]]

• The second array will contain only ones in a 2×2 array
  [[1 1]

[1 1]]

 As you can see from the code the argument given as a tuple will define the size and number of array elements.

For use in linear algebra, numpy provides the following function:

arr = np.eye(3)

The above code snippet will create the following returned array:

[[1 0 0]

 [0 1 0]

 [0 0 1]]

Finally, let’s look at the arange() function:

arr = np.arange(10)

The above code snippet will print generate a 1D array of elements from 0 to 9: 

[0 1 2 3 4 5 6 7 8 9]

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The following tutorials will provide you with step-by-step instructions on how to work with Numpy, including:

• What is Numpy used for in Python?
• How to convert Pandas to Numpy
• How to import Numpy in Python?
• How to turn a Numpy array into a list?

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