In this tutorial, we will look at how to check if a numpy array is 1d (one-dimensional), 2-d (two-dimensional), or a higher-dimensional array with some examples.
How to get the dimensions of a numpy array?
You can use a numpy array’s
ndim property to get the number of dimensions in the array. For a 1d array, it returns 1, for a 2d array it returns 2, and so on.
The following is the syntax –
# get the dimensions of a numpy array df.ndim
Alternatively, you can also use the
shape property of a numpy array to determine the dimensionality of an array. It returns a tuple with each element representing the length of the respective dimension in the array.
Let’s now look at some examples of using the above syntax –
Example 1 – Check if array is 1d, 2d, or a higher dimensional array using
Let’s create a 1d array, a 2d array, and a 3d array and see what we get with the
ndim property for each of these arrays.
import numpy as np # create a 1d array ar1 = np.array([1, 2, 3]) # create a 2d array ar2 = np.array([[1, 2, 3], [4, 5, 6]]) # create a 3d array of zeros with 2 rows, 2 columns, and 3 depth ar3 = np.zeros((2, 2, 3)) # get the dimensions of the arrays print(ar1.ndim) print(ar2.ndim) print(ar3.ndim)
1 2 3
We get the correct result. You can similarly use the
ndim property to determine the dimensionality of a numpy array for higher dimensional arrays as well.
Example 2 – Using the
A numpy array’s
shape property also gives information about the dimensions in the array. Let’s take the same arrays used in the above example.
import numpy as np # create a 1d array ar1 = np.array([1, 2, 3]) # create a 2d array ar2 = np.array([[1, 2, 3], [4, 5, 6]]) # create a 3d array of zeros with 2 rows, 2 columns, and 3 depth ar3 = np.zeros((2, 2, 3)) # get the shape of the arrays print(ar1.shape) print(ar2.shape) print(ar3.shape)
(3,) (2, 3) (2, 2, 3)
shape returns a tuple with the number of values in each dimension of a numpy array. For
ar1 we only get a single value as there’s only one dimension in the array. Thus, you can determine the dimensions by calculating the length of the
# get the dimensions of the arrays print(len(ar1.shape)) print(len(ar2.shape)) print(len(ar3.shape))
1 2 3
We get the same results as above.
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