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NumPy Array Attributes Tutorial With Example

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NumPy Array Attributes Tutorial With Example is today’s topic. NumPy provides a high-performance multidimensional array and necessary tools to calculate with and manipulate these arrays. SciPy builds on this and offers a vast number of methods that operate on numpy arrays and that re useful for different types of scientific and engineering applications.

NumPy Array Attributes Tutorial With Example

If we need to know what is the shape of the NumPy array, then we can use the ndarray.shape() attribute. The shape array attribute returns the tuple consisting of array dimensions. It can also be used to resize the array. Let’s see the example in Python Jupyter Notebook.

Write the following code inside the cell to import the NumPy library.

import numpy as np

Then we need to create a NumPy array from Python List and output its shape. See the below code.

app_list = [18, 0, 21, 30, 46]
np_app_list = np.array(app_list)
np_app_list.shape

Here, we have defined one list and then convert that list into the numpy array and then figure out print it’s shape. See the below output. You can run the cell using Ctrl + Enter key.

 

NumPy Array Attributes Tutorial With Example

The above-defined array is a single dimension array. We can define the multidimensional array using the following code.

app_list = [[18, 0, 21], [30, 46, 21], [19, 21, 18]]
np_app_list = np.array(app_list)
np_app_list.shape

Here, we have defined the 3*3 dimensional Array. So the output is following.

 

NumPy Array Attributes Tutorial Example

NumPy Shape To Resize Array

In the following example, we will see how we can use the NumPy shape attribute to resize the array.

app_list = [[18, 0, 21], [30, 46, 21]]
np_app_list = np.array(app_list)
np_app_list.shape = (3, 2)
np_app_list

Now, we have defined a 2*3 array, and now we have changed its shape to the 3*2 array. See the output below.

 

NumPy Shape To Resize Array

So, we have used the shape attribute to change the shape of the array which means it will turn rows into columns and columns into rows.

numpy.itemsize

The itemsize attribute returns the length of each element of an array in bytes. See the below example.

np_app_list = np.array([18, 0, 21], dtype=np.int8)
np_app_list.itemsize

Here, when we are creating a numpy array, we have passed the second argument which is dtype which means the items datatype, and it is int8. That means the bytes of each character is 1. See the below output.

 

numpy itemsize attribute example

If we take float32, then it will return the in the output.

numpy.ndim

The ndim attribute returns the dimension of a numpy array. See the following example.

npdata = np.array([[21, 19], [18, 21]]) 
npdata.ndim

See the following output.

 

numpy ndim attribute example

Finally, NumPy Array Attributes Tutorial With Example is over.

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