Besides basic array creation via explicitly specifying the elements of the array numpy also allows automatic array creation using special creation functions. Here are two of the most common creation functions which create exclusively 1D arrays:

• arange();
• linspace().
  
  

arange()

numpy.arange() function is similar to the Python built-in range() function, however, it returns an ndarray. Basically, it creates an array with evenly spaced elements within a certain given interval.

Start, Stop, Step

Its three most important parameters are start (0 by default), stop (no default value) and step (1 by default). The first array element is equal to start, and each next one is equal to the previous element + step until the stop value is reached (stop is not included in the array) or exceeded.

Let’s see this function in action:

For array_1 we only set the stop parameter to 11, for array_2 we set both the start to 1 and stop to 11, and for array_3 we specified all of the three parameters with step=2.

As you can see with array_4, we can also specify the data type of the elements.

linspace()

While arange() can work with real numbers, it is often better to use numpy.linspace() for this purpose. With linspace() instead of the step parameter there is num parameter used to specify the number of samples (numbers) within a given interval (50 by default).

Let’s see how we can use this function:

As you can see, everything is quite simple here.

Endpoint

Let’s rather focus on the endpoint boolean parameter. Its default value is True meaning that the stop value is inclusive. Setting it to False excludes this value thus making the step lower and shifting the interval (have a closer look at array_1 and array_3).

Note

You can always explore more about these functions in their documentation: arange, linspace.