密谋：创建具有类别x轴抖动和多级轴的散点图

debugcn 发表于 Dev

丹尼尔·扎帕塔（Daniel Zapata）

我想制作一个具有多级x轴的图形，如下图所示：

import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(
  go.Scatter(
    x = [df['x'], df['x1']],
    y = df['y'],
    mode='markers'
  )
)

但我也想像下一张图片一样在x轴上放置抖动：

到目前为止，我可以使用以下代码独立制作每个图形：

import plotly.express as px
fig = px.strip(df,
               x=[df["x"], df['x1']], 
               y="y",
               stripmode='overlay')

是否可以在一个图中组合抖动和多级轴？

这是用于重现数据集的代码：

import numpy as np
import pandas as pd
import random

'''Create DataFrame'''
price = np.append(
  np.random.normal(20, 5, size=(1, 50)), np.random.normal(40, 2, size=(1, 10))
)
quantity = np.append(
  np.random.randint(1, 5, size=(50)), np.random.randint(8, 12, size=(10))
)

firstLayerList = ['15 in', '16 in']
secondLayerList = ['1/2', '3/8']
vendorList = ['Vendor1','Vendor2','Vendor3']

data = {
  'Width':  [random.choice(firstLayerList) for i in range(len(price))],
  'Length': [random.choice(secondLayerList) for i in range(len(price))],
  'Vendor': [random.choice(vendorList) for i in range(len(price))],
  'Quantity': quantity,
  'Price':  price
}
df = pd.DataFrame.from_dict(data)

S3DEV

首先-感谢您的挑战！这些天来，Plotly的挑战性问题并不多。

创建具有抖动的散点图的关键元素是：

使用mode: 'box'-创建箱形图，而不是散点图。
设置'boxpoints': 'all'-绘制所有点。
Using 'pointpos': 0 - to center the points on the x-axis.
Removing (hiding!) the whisker boxes using:
- 'fillcolor': 'rgba(255,255,255,0)'
- 'line': {'color': 'rgba(255,255,255,0)'}

DataFrame preparation:

This code simply splits the main DataFrame into a frame for each vendor, thus allowing a trace to be created for each, with their own colour.

df1 = df[df['Vendor'] == 'Vendor1']
df2 = df[df['Vendor'] == 'Vendor2']
df3 = df[df['Vendor'] == 'Vendor3']

Plotting code:

The plotting code could use a for-loop if you like. However, I've intentionally kept it more verbose, so as to increase clarity.

import plotly.io as pio

layout = {'title': 'Categorical X-Axis, with Jitter'}
traces = []

traces.append({'x': [df1['Width'], df1['Length']], 'y': df1['Price'], 'name': 'Vendor1', 'marker': {'color': 'green'}})
traces.append({'x': [df2['Width'], df2['Length']], 'y': df2['Price'], 'name': 'Vendor2', 'marker': {'color': 'blue'}})
traces.append({'x': [df3['Width'], df3['Length']], 'y': df3['Price'], 'name': 'Vendor3', 'marker': {'color': 'orange'}})

# Update (add) trace elements common to all traces.
for t in traces:
    t.update({'type': 'box',
              'boxpoints': 'all',
              'fillcolor': 'rgba(255,255,255,0)',
              'hoveron': 'points',
              'hovertemplate': 'value=%{x}<br>Price=%{y}<extra></extra>',
              'line': {'color': 'rgba(255,255,255,0)'},
              'pointpos': 0,
              'showlegend': True})

pio.show({'data': traces, 'layout': layout})

Graph:

The data behind this graph was generated using np.random.seed(73), against the dataset creation code posted in the question.

Comments (TL;DR):

此处显示的示例代码使用较低级别的Plotly API，而不是诸如graph_objects或的便利包装express。原因是我（个人）认为，对用户“幕后”展示正在发生的事情有所帮助，而不是用便捷包装掩盖底层的代码逻辑。

这样，当用户需要修改图形的更详细信息时，他们将更好地理解Plotly为基础图形引擎（orca）构建的lists和dicts。

这个用例是这种推理的主要示例，因为它已经超出了（当前）设计点。

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编辑于2021-04-5

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