Creating NumPy Arrays in Python

You have already learnt how to convert lists or tuples to arrays using np.array() in the previous segments. Coming to the next method, the following ways are commonly used to create arrays when you know the length of the required arrays:

• np.ones(): Create an array of 1s.
• np.zeros(): Create an array of 0s.
• np.arange(): Create an array with increments of fixed step size.
• np.linspace(): Create an array of fixed length.

Let’s look into each of these methods one by one. You can download the notebook provided below for your reference.

Now, you will be able to create arrays using the functions: np.arange() and np.zeros(). Let’s see some more functions to initialise arrays.

Apart from the functions mentioned in the video, there are a few more useful functions for creating arrays, such as: 

• np.full(): Create a constant array of any number ‘n’.
• np.tile(): Create a new array, by repeating an existing array, for a fixed number of times.
• np.eye(): Create an identity matrix of any dimension.
• np.random.random(): Create an array of random numbers between 0 and 1.
• np.random.randint(): Create a random array of integers within a particular range.

These functions, along with appropriate examples, have been mentioned at the end of the Jupyter Notebook ‘05 - Creating NumPy Arrays’ used in this segment. 

Let's see some other important functions that you can use while creating your arrays. These functions will help you modify the elements created using the commands mentioned above.

You saw the following functions in the video above: 

• np.power(): Calculating powers of the array elements.
• np.absolute(): Converting all the elements in the absolute form.
• np.sin()or np.cos(): Takes the sine/cosine of elements present in the array.
• np.log(): Takes the log of the elements in the array.

These functions will help you alter the arrays according to your requirements.

Another important feature offered by NumPy is of empty arrays. You can initialise an empty array and later use it to store the output of your operations.

Once you have created an array, you may also want to run aggregation operations over the data stored in it. An aggregation function helps you summarise the numerical data. Let's have a look at them in the video below.

Using the reduce() and aggregate() functions, you can easily summarise the data available in arrays. The reduce()function results in a single value, whereas the aggregate()function helps you to apply your aggregation sequentially on each element of the array. These functions require a base function to aggregate the data, for example, add() in the above case.

In the next video, Vaidehi summarises other features of NumPy arrays which you may come across in future modules.

In the next segment, you will come across some advanced operations that can be performed over NumPy arrays.