情节:如何制作3D堆叠直方图? [英] Plotly: How to make a 3D stacked histogram?

问题描述

我成功使用 plotly 绘制了几个直方图，如下所示:

I have several histograms that I succeded to plot using plotly like this:

fig.add_trace(go.Histogram(x=np.array(data[key]), name=self.labels[i]))

我想创建类似此3D堆叠直方图的东西，但是要有所不同里面的每个2D直方图都是真实的直方图，而不仅仅是硬编码的行(我的数据的格式为[0.5 0.4 0.5 0.7 0.4]，因此直接使用直方图非常方便)

I would like to create something like this 3D stacked histogram but with the difference that each 2D histogram inside is a true histogram and not just a hardcoded line (my data is of the form [0.5 0.4 0.5 0.7 0.4] so using Histogram directly is very convenient)

请注意，我要问的内容与 this 类似，因此也与与此相同.在matplotlib示例中，数据直接以2D数组形式显示，因此直方图是第3维.就我而言，我想提供一个包含许多已经计算出的直方图的函数.

Note that what I am asking is not similar to this and therefore also not the same as this. In the matplotlib example, the data is presented directly in a 2D array so the histogram is the 3rd dimension. In my case, I wanted to feed a function with many already computed histograms.

推荐答案

下面的代码段同时负责图形的装箱和格式设置，因此它使用多个go.Scatter3D和np.Histogram痕迹显示为堆叠的3D图表. 输入是使用np.random.normal(50, 5, size=(300, 4))具有随机数的数据框 如果您可以使用此方法，我们可以详细讨论其他细节:

The snippet below takes care of both binning and formatting of the figure so that it appears as a stacked 3D chart using multiple traces of go.Scatter3D and np.Histogram. The input is a dataframe with random numbers using np.random.normal(50, 5, size=(300, 4)) We can talk more about the other details if this is something you can use:

图1:角度1

图2:角度2

完整代码:

# imports
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio

pio.renderers.default = 'browser'

# data
np.random.seed(123)
df = pd.DataFrame(np.random.normal(50, 5, size=(300, 4)), columns=list('ABCD'))

# plotly setup
fig=go.Figure()

# data binning and traces
for i, col in enumerate(df.columns):
    a0=np.histogram(df[col], bins=10, density=False)[0].tolist()
    a0=np.repeat(a0,2).tolist()
    a0.insert(0,0)
    a0.pop()
    a1=np.histogram(df[col], bins=10, density=False)[1].tolist()
    a1=np.repeat(a1,2)
    fig.add_traces(go.Scatter3d(x=[i]*len(a0), y=a1, z=a0,
                                mode='lines',
                                name=col
                               )
                  )
fig.show()

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