Plotly：如何更改 3D 表面 plot 的配色方案？

Question

我想在 plotly Z23EEEB4347BDD26BFC6B7EE9A3B755 Plotly 默認分配配色方案，如下圖所示。

這是我的代碼

import import plotly.graph_objects as go
import pandas as pd

data = pd.read_csv('\Data.csv')
data.set_index("years", inplace = True)
figure = go.Figure(data=[go.Surface(z=data.values)])
figure.update_layout(
            scene = dict(
                xaxis = dict(
                    title = 'Months',
                    #nticks = 5,
                    autorange='reversed',
                    showgrid=True, 
                    gridwidth=1, 
                    gridcolor='Blue',
                    ticktext = data.columns,
                    tickvals= list(range(0,data.shape[1]))),
                
                    
                yaxis = dict(
                    title = 'years',
                    showgrid=True, 
                    gridwidth=1, 
                    gridcolor='Blue',
                    ticktext = data.index,
                    tickvals= list(range(0,data.shape[0]))),
              
                zaxis = dict(
                    title = 'Discharge (Cumecs)',
                    #showgrid=True, 
                    gridwidth=1,
                    gridcolor='Blue')),
           tilte = 'Plot 1'
)

Answer 1

您可以通過colorscale輕松更改配色方案

go.Surface(colorscale ='<color>')

這是一個使用colorscale='Blues的示例：

fig = go.Figure(data=[go.Surface(z=z_data.values, colorscale ='Blues')])

Plot

您的色階選項是（但不一定限於）：

Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis。

示例代碼：

import plotly.graph_objects as go

import pandas as pd

# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')

fig = go.Figure(data=[go.Surface(z=z_data.values, colorscale ='Blues')])

fig.update_layout(title='Mt Bruno Elevation', autosize=False,
                  width=500, height=500,
                  margin=dict(l=65, r=50, b=65, t=90))
f = fig.full_figure_for_development(warn=False)


fig.show()