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 34.28571429

37.14285714 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: