Jupyter notebooks are widely used in the data science and programming community for their interactivity and ease of creating and sharing code. However, one common question that arises is whether it's possible to view 3D plots within Jupyter.

The answer is yes! Jupyter supports 3D plotting through various libraries and tools. One of the most popular libraries for 3D visualization in Python is Matplotlib, which provides a 3D plotting toolkit called mpl_toolkits.mplot3d. With this toolkit, you can create a variety of 3D plots, including scatter plots, surface plots, and wireframe plots, all within a Jupyter notebook.

Another powerful tool for 3D visualization in Jupyter is Plotly, a graphing library that makes interactive, publication-quality graphs online. Plotly's Python library allows you to create interactive 3D plots that can be embedded in Jupyter notebooks and viewed directly in the browser. With Plotly, you can create 3D surface plots, scatter plots, and even 3D subplots with ease.

In addition to Matplotlib and Plotly, there are other libraries such as Mayavi and Vispy that offer advanced 3D visualization capabilities in Jupyter. These libraries are especially useful for scientific computing and visualizing complex 3D data.

To start creating 3D plots in a Jupyter notebook, you'll first need to install the appropriate libraries using Python's package manager, pip. Once installed, you can import the libraries into your Jupyter notebook and start creating stunning 3D visualizations.

Here's a simple example of creating a 3D scatter plot using Matplotlib in a Jupyter notebook:

```python

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

# Create random data

x = np.random.normal(size=500)

y = np.random.normal(size=500)

z = np.random.normal(size=500)

# Create 3D scatter plot

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

ax.scatter(x, y, z)

# Show plot

plt.show()

```

In this example, we generate random data for the x, y, and z coordinates and then create a 3D scatter plot using Matplotlib's Axes3D class. This code can be easily executed within a Jupyter notebook, and the 3D plot will be displayed inline.

In conclusion, viewing 3D plots in Jupyter is definitely possible using various libraries and tools. Whether you're visualizing scientific data, mathematical functions, or machine learning models, Jupyter provides a versatile platform for creating and sharing interactive 3D plots in Python.