Plotly：创建一个具有分类x轴抖动和多级轴的散点图

Question

Plotly：创建一个具有分类x轴抖动和多级轴的散点图

12

我想制作一个类似下面图片中多级X轴的图表：

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

但是我也想像下图一样在x轴上加入抖动：

到目前为止，我可以使用以下代码单独制作每个图：

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

能否将抖动图和多级坐标轴合并在一个图中？

以下是可复现数据集的代码：

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)

- Daniel Zapata

1

还有@vestland - Plotly问题总是领先一步。我刚想在这个问题上标记你...但我看到没有必要。 :-) - S3DEV

@S3DEV 哈哈 =）这个问题对于一个周六晚上来说有点棘手。我得把它搁置一段时间。除非楼主提供数据样本。因为那就是问题所在...我总是花更多的时间重新创建问题，而不是解决问题，所以好问题和适当的数据样本总是更难让人离开。顺便说一句，我真的很喜欢你最近在[y-axes]（https://dev59.com/Br7pa4cB1Zd3GeqPxF1_#65039164）上的工作。 - vestland

1

@vestland - 同意！有时候那些合成数据集很棘手。非常感谢，你真是太好了。最后图表有点混乱，但问题解决了。以后再聊。 - S3DEV

1

我添加了一些代码来创建样本数据。谢谢！ - Daniel Zapata

谢谢你，丹尼尔。为了让我们的生活更轻松，合并 x 轴标签是一个选项吗？例如，“15 3/8”，“15 1/2”，“16”等。 - S3DEV

显示剩余3条评论

3个回答

2

另一个简单的解决方案可能是使用：plotly.express.strip和stripmode="overlay"（有关参数的更多信息）

这里我给您展示了一个鸢尾花数据的示例。Plotly版本为4.4.1。

import plotly.express as px
df = px.data.iris()
fig = px.strip(df, 
               x="species", y="sepal_width", color="species", 
               title="This is a stripplot!", 
               stripmode = "overlay"   # Select between "group" or "overlay" mode
)
fig.show()

这是结果（运行代码片段）

<div>
        
                <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
        <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>    
            <div id="70e0d94a-4a4c-40fc-af77-95274959151b" class="plotly-graph-div" style="height:100%; width:100%;"></div>
            <script type="text/javascript">
                
                    window.PLOTLYENV=window.PLOTLYENV || {};
                    
                if (document.getElementById("70e0d94a-4a4c-40fc-af77-95274959151b")) {
                    Plotly.newPlot(
                        '70e0d94a-4a4c-40fc-af77-95274959151b',
                        [{"alignmentgroup": "True", "boxpoints": "all", "fillcolor": "rgba(255,255,255,0)", "hoverlabel": {"namelength": 0}, "hoveron": "points", "hovertemplate": "species=%{x}<br>sepal_width=%{y}", "legendgroup": "species=setosa", "line": {"color": "rgba(255,255,255,0)"}, "marker": {"color": "#636efa"}, "name": "species=setosa", "offsetgroup": "species=setosa", "orientation": "v", "pointpos": 0, "showlegend": true, "type": "box", "x": ["setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa"], "x0": " ", "xaxis": "x", "y": [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.1, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3], "y0": " ", "yaxis": "y"}, {"alignmentgroup": "True", "boxpoints": "all", "fillcolor": "rgba(255,255,255,0)", "hoverlabel": {"namelength": 0}, "hoveron": "points", "hovertemplate": "species=%{x}<br>sepal_width=%{y}", "legendgroup": "species=versicolor", "line": {"color": "rgba(255,255,255,0)"}, "marker": {"color": "#EF553B"}, "name": "species=versicolor", "offsetgroup": "species=versicolor", "orientation": "v", "pointpos": 0, "showlegend": true, "type": "box", "x": ["versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor", "versicolor"], "x0": " ", "xaxis": "x", "y": [3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8], "y0": " ", "yaxis": "y"}, {"alignmentgroup": "True", "boxpoints": "all", "fillcolor": "rgba(255,255,255,0)", "hoverlabel": {"namelength": 0}, "hoveron": "points", "hovertemplate": "species=%{x}<br>sepal_width=%{y}", "legendgroup": "species=virginica", "line": {"color": "rgba(255,255,255,0)"}, "marker": {"color": "#00cc96"}, "name": "species=virginica", "offsetgroup": "species=virginica", "orientation": "v", "pointpos": 0, "showlegend": true, "type": "box", "x": ["virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica"], "x0": " ", "xaxis": "x", "y": [3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0], "y0": " ", "yaxis": "y"}],
                        {"boxmode": "overlay", "legend": {"tracegroupgap": 0}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "This is a stripplot!"}, "xaxis": {"anchor": "y", "categoryarray": ["setosa", "versicolor", "virginica"], "categoryorder": "array", "domain": [0.0, 1.0], "title": {"text": "species"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "sepal_width"}}},
                        {"responsive": true}
                    )
                };
                
            </script>
        </div>

- Álvaro H.G

0

一些plotly.express函数提供了一个facet argument，允许你创建一个类似于你的示例中多级x轴的图形，而不需要调用低级API。

从一些样本数据和导入开始：

import plotly.express as px
import pandas as pd
import numpy as np

# Create data
df = pd.DataFrame(data={"y": np.random.uniform(low=0, high=45, size=100)})
df["x"] = [*["3/8"] * 25, *["1/2"] * 25, *["3/8"] * 25, *["1/2"] * 25]
df["x1"] = [*["16 in"] * 50, *["15 in"] * 50]
print(df.head().to_markdown())

	y	x	x1
0	27.1279	3/8	16英寸
1	13.8564	3/8	16英寸
2	11.8026	3/8	16英寸
3	25.3769	3/8	16英寸
4	12.1194	3/8	16英寸

您可以从数据框中提供一列作为facet_col参数：

px.strip(df, x=["x", "x1"], y="y", stripmode='overlay', facet_col="x1")

这会导致：

如果您喜欢的话，可以进一步调整子图标题的外观。

- Saaru Lindestøkke

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- S3DEV · Accepted Answer

首先，感谢您的挑战！现在很少有具有挑战性的Plotly问题。

创建带有jitter的散点图的关键要素是：

- 使用 `mode: 'box'` - 创建箱线图，而不是散点图。 - 设置 `boxpoints': 'all'` - 这样所有点都会被绘制。 - 使用 `pointpos: 0` - 将点居中于x轴上。 - 隐藏（隐藏！）须盒使用： - `'fillcolor': 'rgba(255,255,255,0)'` - `'line': {'color': 'rgba(255,255,255,0)'}` DataFrame准备工作：

此代码将主DataFrame拆分为每个供应商的框架，从而允许为每个供应商创建一个跟踪，并具有自己的颜色。

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

绘图代码：

如果您喜欢，绘图代码可以使用for循环。然而，我故意保持了更加冗长的形式，以增加清晰度。

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})

图表:

这个图表的数据是使用np.random.seed(73)生成的，针对问题中发布的数据集创建代码。

评论（简而言之）：

此示例代码使用较低级别的Plotly API，而不是像graph_objects或express这样的方便包装器。原因是我认为向用户展示正在发生的“底层”操作比使用方便的包装器掩盖底层代码逻辑更有帮助。

这样，当用户需要修改图表的细节时，他们将能够更好地理解Plotly正在构建的list和dict，以供底层图形引擎（orca）使用。

这种使用情况是此推理的一个典型例子，因为它将Plotly推向了其（当前）设计点之外。