## ⏱ Quick start

The `mplot3d`

toolkit of `matplotlib`

is used here.

- The projection parameter of the add_subplot() function is set to
`3d`

- The usual
`scatter()`

function can now be called with 3 data inputs for the X, Y and Z axis - The camera position can be set thanks to the
`view_init()`

function

```
# libraries
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Dataset
df=pd.DataFrame({'X': range(1,101), 'Y': np.random.randn(100)*15+range(1,101), 'Z': (np.random.randn(100)*15+range(1,101))*2 })
# plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df['X'], df['Y'], df['Z'], c='skyblue', s=60)
ax.view_init(30, 185)
plt.show()
```

## ⚠️ Mind the 3d

Three dimensional objects are very popular but negatively affect the __accuracy__ and __speed__ at which one can interpret a graphic in most cases.

In the example below, the brown section in front looks much bigger than the pink section in the back, even tough their real values are 30% vs 35%. Data is distorted.

__Note__: remember pie charts should be avoided most of the time

## Three-dimensional scatterplots with `Matplotlib`

As described in the quick start section above, a three dimensional can be built with python thanks to the`mplot3d`

toolkit of `matplotlib`

. The example below will guide you through its usage to get this figure:

This technique is useful to visualize the result of a PCA (Principal Component Analysis). The following example explains how to run a PCA with python and check its result with a 3d scatterplot:

## Surface plot with `Matplotlib`

A surface plot considers the X and Y coordinates as latitude and longitude, and Z as the altitude. It represents the dataset as a surface by interpolating positions between data points.

This kind of chart can also be done thanks to the `mplot3d`

toolkit of `matplotlib`

. The posts linked below explain how to use and customize the `trisurf()`

function that is used for surface plots.