Python: Plotly 3D Surface Plot

Question

I am having a tough time drawing the Plotly 3d surface plot. I have a big data frame of 4000 rows and three columns. I did ask questions here and got some answers. When I try them, it takes hours for the code to run yet I see no plot. I want to confirm what I am doing is right. Because I am new to the surface plots.

My code:

import plotly.graph_objects as go
import plotly.graph_objs
import plotly
df = 
index     x           y           z
0        10.2        40.5        70.5            
1        30.5        30.2       570.5
.
.
4000     100.5       201.5      470.5

df['z']= [df['z'].tolist for x in df.index]
df = 
index     x           y           z
0        10.2        40.5       [70.5,570.5,..,470.5]            
1        30.5        30.2       [70.5,570.5,..,470.5]
.
.
4000     100.5       201.5      [70.5,570.5,..,470.5]

    zdata = [df['z'].tolist()]*len(df)
    plotly.offline.plot({"data":[go.Surface(x=df['x'].values,
                                            y=df['y'].values,
                                            z = df['z'].values)],
    "layout":plotly.graph_objs.Layout(title='Some data', autosize=False,
                      width=600, height=600,
                    scene = dict(xaxis_title='x',
                    yaxis_title='y',
                    zaxis_title='z'),
                      margin=dict(l=10, r=10, b=10, t=10))})

I would be grateful to have somebody clarify me that what I am doing to generate a surface plot is correct?

Answer 1

Here is a simple / stripped down example of a 3D surface plot to hopefully get you going.

The key message here is: Don't over complicate it. This same logic should be fine on a DataFrame with 4000+ rows. (Granted, it'll plot ~16M data points, so it'll take a bit of time).

The key point to remember is that z must be a 2d array of the shape [x.shape[0], y.shape[0]]. Essentially meaning, if x and y are of length 10, then z must be of the shape: [10, 10].

As I don't have your complete dataset, I've synthesised the data - hope that's OK for illustration purposes. Additionally, I've stuck with numpy for simplicity, keeping in mind that a numpy array is essentially a DataFrame column.

Simple example:

import numpy as np
from plotly.offline import plot

n = 10
x = np.arange(n)
y = x
z = np.tile(x**2, [n, 1])

data = [{'x': x,
         'y': y,
         'z': z,
         'type': 'surface'}]

plot({'data': data}, filename='/path/to/graph.html')

Output:

Something a little more fun:

n = 360
x = np.arange(n)
y = x
v = np.tile([np.sin(i*(np.pi/180)) for i in range(n)], [n, 1]).T
z = (v.T[0]*v)

data = [{'x': x,
         'y': y,
         'z': z,
         'type': 'surface'}]

plot({'data': data}, filename='/path/to/graph.html')

You'll note the plotting logic is identical.

Output: