# 3d scatter plot python

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Matplotlib is a powerful library in Python for data visualization. In this tutorial, you’ll learn how to create a **3D scatter plot** using Matplotlib. While 2D scatter plots are common, 3D scatter plots can provide a new perspective and deeper understanding in some cases.

## Overview of 3D Scatter Plots in Matplotlib

Just like a 2D scatter plot, the 3D version uses dots to represent data points in three-dimensional space. The major difference, of course, is the addition of a third axis (z-axis) to visualize data in a three-dimensional space. For those familiar with 2D scatter plots, transitioning to 3D is straightforward with only a few tweaks in the code.

## Setting Up for a 3D Scatter Plot

Before we delve into creating the 3D scatter plot, it’s essential to import the necessary module. The `axes3d`

module from `mpl_toolkits.mplot3d`

is a must:

1 | from mpl_toolkits.mplot3d import axes3d |

Once imported, it’s time to give your data a z-axis and set the figure to project in 3D:

1 | ax = fig.gca(projection='3d') |

## A Comprehensive Example of a 3D Scatter Plot

Let’s walk through a detailed example to create a vibrant 3D scatter plot:

1 | import numpy as np |

In this example, we’ve followed several crucial steps:

- Created vectors (
`g1`

,`g2`

,`g3`

) representing data. - Defined a list of groups for labeling purposes.
- Used the plotting functions to generate the visual representation.

Once executed, the `plt.show()`

command will display the final 3D scatter plot.

Curious about more Matplotlib visualizations? Navigate through:

Previous Tutorial or Next Tutorial.

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