# Examples of numpy.linspace() in Python

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The **numpy.linspace()** function returns a ndarray with equally spaced intervals between the **start** and **stop** values. That ndarray is a vector space, also known as a linear space. That’s why the function is named linspace.

This article goes over the syntax as well as a few examples of using the **numpy.linspace() **function in Python programs.

Table of Contents

## Syntax

First, let’s take a look at **numpy.linspace()** syntax:

```
numpy.linspace(
start,
stop,
num=50,
endpoint=True,
retstep=False,
dtype=None,
axis=0
)
```

Parameters:

Name | Required | Default | Description |
---|---|---|---|

start | required | The starting value of the sequence. | |

stop | required | The end value of the sequence, unless the endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 evenly spaced samples, so that stop is excluded. Note that the step size changes when the endpoint is False. | |

num | optional | 50 | A number of samples to generate. Must be a non-negative integer. |

endpoint | optional | True | If True, stop is the last sample. Otherwise, it is not included. |

retstep | optional | False | If True, return (samples, step), where the step is the spacing between samples. |

dtype | optional | None | The type of the output array. |

axis | optional | 0 | The axis in the result stores the samples. Relevant only if start or stop are array-like. By default, the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. |

## Example 1: Basic Usage

The code:

```
import numpy as np
x1 = np.linspace(start = 0, stop = 100, num = 11)
print(x1)
# set endpoint to False
x2 = np.linspace(start = 0, stop = 100, num = 11, endpoint = False)
print(x2)
# set retstep to True
x3 = np.linspace(0, 100, num = 11, retstep = True)
print(x3)
```

Output (x1, x2, and x3 in the corresponding order):

```
[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
[ 0. 9.09090909 18.18181818 27.27272727 36.36363636 45.45454545
54.54545455 63.63636364 72.72727273 81.81818182 90.90909091]
(array([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100.]), 10.0)
```

## Example 2: Making a bar chart with np.linspace() and Matplotlib

The code:

```
import numpy as np
import matplotlib.pyplot as plt
N = 11
x = np.linspace(0, 100, num = N)
y = x ** 3 - 2 * x + 7
ax = plt.subplot()
ax.bar(x, y, width=4)
ax.set_xlabel('X')
ax.set_ylabel('Y')
plt.show()
```

Output:

## Example 3: endpoint = True vs endpoint = False graphical representation

The code:

```
import numpy as np
import matplotlib.pyplot as plt
N = 9
x1 = np.linspace(0, 8, num = N)
x2 = np.linspace(0, 8, num = N, endpoint = False)
y = np.ones(N)
plt.plot(x1, y, 'o')
plt.plot(x2, y + 1, 'o')
plt.ylim([0, 3])
plt.show()
```

Output:

You can find more information about numpy.linspace in the Numpy official manual.

## Conclusion

At this point, you should have a better understanding of some of the major use cases of **np.linspace()**. If you’d like to learn more new and interesting stuff in the Python world, take a look at the following articles:

- List, Dict, and Set Comprehensions in Python 3
- Changing dtype of a NumPy array
- Tensorflow – Converting Tensors to Numpy Arrays
- Python: Categorizing Given Words by Their First Letters
- Python 3: Formatting a DateTime Object as a String

You can also check out our Machine Learning category page or Python category page for more tutorials and examples.