# NumPy linspace() function

This function is used to return evenly spaced numbers over a specified interval.

• This function is similar to Numpy arange() function with the only difference being, instead of step size, the number of evenly spaced values between the interval is specified using the `num` argument.
• With the help of this function, the step size is calculated implicitly.
• In this function, the endpoint of the interval can optionally be excluded.
• In the newest versions of NumPy, the non-scalar values of start and stop parameters(used to define the interval) are supported by this function.

### Syntax of `numpy.linspace()`:

The required syntax to use this function is as follows:

``numpy.linspace(start, stop, num, endpoint, retstep, dtype)   ``

Parameters:

The parameters of the above-mentioned function are as follows:

• start
This parameter is used to represents the starting value of the interval.
• stop
This parameter is used to represents the stopping value of the interval.
• num
This parameter indicates the amount of evenly spaced samples over the interval to be generated. The default value of this parameter is 50.
• endpoint
This parameter’s true value is used to indicate that the stopping value is included in the interval.
• retstep
The value of this parameter is a boolean value and it is used to represent the steps and samples between the consecutive numbers.
• dtype
This parameter is used to represent the data type of the array items.

Returned Values:

This function will return the array within the range specified.

This function will return the value of step in the case if `retstep` parameter is True which usually indicates the size of the spacing between the samples.

Now it’s time to take a look at the examples using this function.

## Example 1:

Below we have the code snippet explaining how to use this function:

``````import numpy as np

a = np.linspace(20, 40, 8, endpoint = True)
print("The array over the given range is ")
print(a) ``````

Output:

`The array over the given range is[20. 22.85714286 25.71428571 28.57142857 31.42857143 34.2857142937.14285714 40. ]`

## Example 2:

Below we have the code snippet for the graphical illustration of this function, using Matplotlib library:

``````import matplotlib.pyplot as plt

N = 10
y = np.zeros(N)
x1 = np.linspace(0, 20, N, endpoint=True)
x2 = np.linspace(0, 20, N, endpoint=False)
plt.plot(x1, y, '*')
plt.plot(x2, y + 0.8, 'o')
plt.show()``````

Output: