## Introduction

Python is a powerful programming language that has become increasingly popular for data analysis and visualization. One of the most useful tools for visualizing data is Matplotlib, a Python library that allows you to create a wide range of plots and charts. In particular, if you’re looking to create visualizations of three-dimensional data, a 3D scatter plot in Python is an excellent way to do so. In this blog post, we’ll walk you through the process of creating a 3D scatter plot using Python and Matplotlib. Whether you’re new to Python or an experienced user, this tutorial will provide you with everything you need to know to create stunning visualizations of your data using a 3D scatter plot in Python. We’ll even learn how to make animated 3D Scatter Plots that are interactive like the one shown below:

So, let’s dive in!

## Understanding 3D Scatter Plots in Python

Let’s work through a basic example of a 3D Scatter Plot in Python by utilizing NumPy to create a simple example.

```
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
# Generate random data
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)
z = np.random.normal(0, 1, 100)
# Create a 3D scatter plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, c=z)
# Set labels and title
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.title('3D Scatter Plot')
plt.show()
```

This will produce a plot that looks like this:

Here are the steps needed to create the plot:

- First, we import the necessary libraries:
`matplotlib.pyplot`

for plotting,`mpl_toolkits.mplot3d`

for 3D projections, and`numpy`

for generating random data. - We generate random data for the x, y, and z coordinates using the
`numpy.random.normal()`

function. This function generates random numbers from a normal (Gaussian) distribution with a given mean and standard deviation. In this case, we generate 100 random numbers for each coordinate with a mean of 0 and a standard deviation of 1. - We create a
`fig`

object and an`ax`

object using the`plt.subplots()`

function. The`projection`

parameter is set to`'3d'`

to indicate that we want a 3D plot. - We use the
`scatter()`

function to plot the data points in 3D space. The`x`

,`y`

, and`z`

parameters are arrays containing the coordinates for each point, and the`c`

parameter is set to`'z'`

to indicate that we want the color of each point to be determined by its z-coordinate. - We set the labels for the x, y, and z axes using the
`set_xlabel()`

,`set_ylabel()`

, and`set_zlabel()`

functions. - Finally, we set the title of the plot using the
`title()`

function and display the plot using the`show()`

function.

## 2D Data Combined with 3D Python Plot

We can also combine 2D data with a 3D Python plot to show points on a plane along with a curve. For example, consider the following code:

```
import numpy as np
import matplotlib.pyplot as plt
# Create a 3D plot
ax = plt.figure(dpi=150).add_subplot(projection='3d')
# Plot a sin curve using the x and y axes.
x = np.linspace(0, 1, 100)
y = np.sin(x * 3 * np.pi) / 2 + 0.6
ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
# Plot scatterplot data (20 2D points per colour) on the x and z axes.
colors = ('#0000FF', '#FF00FF', '#800080', '#8A2BE2')
# Fixing random state for reproducibility
np.random.seed(19680801)
# Generate random data points
x = np.random.sample(20 * len(colors))
y = np.random.sample(20 * len(colors))
# Create a list of colors corresponding to the data points
c_list = []
for c in colors:
c_list.extend([c] * 20)
# By using zdir='y', the y value of these points is fixed to the zs value 0
# and the (x, y) points are plotted on the x and z axes.
ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x, z)')
# Make legend, set axes limits and labels
ax.legend(loc=(1,1))
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_zlim(0, 1)
# Customizing Viewing Angle
ax.view_init(elev=20., azim=-35, roll=0)
# Show the plot
plt.show()
```

This would result in a plot that looks like this:

## Saving and Sharing Python 3D Scatter Plots

The examples above we saved the plots as static images, but wouldn’t it be cool to let these plots be interactive? With the Plotly Express library we can easily pass in the data with a Pandas DataFrame and then let it be interactive for users.

First make sure to install the Plotly Express library with:

`pip install plotly`

Then we can simply adjust our code to pass in the data to a Pandas DataFrame and then pass that into Plotly’s scatter_3d call. For example:

```
import plotly.express as px
import numpy as np
# Generate random data
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)
z = np.random.normal(0, 1, 100)
# Create a DataFrame with the data
data = {'x': x, 'y': y, 'z': z}
df = pd.DataFrame(data)
# Create the 3D scatter plot using plotly.express
fig = px.scatter_3d(df, x='x', y='y', z='z', color='z')
# Set labels and title
fig.update_layout(scene=dict(
xaxis_title='X Label',
yaxis_title='Y Label',
zaxis_title='Z Label'
))
fig.update_layout(title='3D Scatter Plot')
# Display the plot
fig.show()
```

This will allow you to have an interactive graph as shown in the video below:

## Conclusion

In conclusion, 3D scatter plots are a powerful tool in data visualization and Python’s Matplotlib library makes it very easy to create them. By adding the third dimension, we can visualize complex relationships in our data that may not be immediately evident in 2D plots. With the help of Matplotlib’s customization options, we can also make our plots informative and visually appealing. Whether you’re exploring a new dataset or presenting insights to colleagues, 3D scatter plots are a great way to showcase your findings. So why not give it a try and see what insights you can uncover? If you’re interested in learning more, check out our Python for Data Science course!

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