情节：如何在具有刻面的情节表达人物中隐藏轴标题？ 此答案分为五个部分： 1隐藏子图标题 2隐藏yaxis文字 3单轴标签 4情节 5完整的代码：

编程问答 2022-11-01

问题描述

是否有一种简单的方法可以使用plotly express在多面图中隐藏重复的轴标题？我尝试设置

visible=True

在下面的代码中，但这也隐藏了y轴刻度标签（值）。理想情况下，我想将隐藏重复轴的标题设置为一般情况下多面图的默认值（甚至更好，只是默认为整个多面图显示一个x和y轴标题。

这是测试代码：

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

# create a dataframe
cols = list(string.ascii_letters)
n = 50

df = pd.DataFrame({'Date': pd.date_range('2021-01-01',periods=n)})

# create data with vastly different ranges
for col in cols:
    start = np.random.choice([1,10,100,1000,100000])
    s = np.random.normal(loc=0,scale=0.01*start,size=n)
    df[col] = start + s.cumsum()

# melt data columns from wide to long
dfm = df.melt("Date")

fig = px.line(
    data_frame=dfm,x = 'Date',y = 'value',facet_col = 'variable',facet_col_wrap=6,facet_col_spacing=0.05,facet_row_spacing=0.035,height = 1000,width = 1000,title = 'Value vs. Date'
)

fig.update_yaxes(matches=None,showticklabels=True,visible=True)
fig.update_annotations(font=dict(size=16))
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))

enter image description here

最终密码（可接受的答案）。请注意> = 4.9 

import pandas as pd
import numpy as np
import plotly.express as px
import string
import plotly.graph_objects as go

# create a dataframe
cols = list(string.ascii_letters)
n = 50

df = pd.DataFrame({'Date': pd.date_range('2021-01-01',visible=True)
fig.update_annotations(font=dict(size=16))
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))

# hide subplot y-axis titles and x-axis titles
for axis in fig.layout:
    if type(fig.layout[axis]) == go.layout.YAxis:
        fig.layout[axis].title.text = ''
    if type(fig.layout[axis]) == go.layout.XAxis:
        fig.layout[axis].title.text = ''
        
# keep all other annotations and add single y-axis and x-axis title:
fig.update_layout(
    # keep the original annotations and add a list of new annotations:
    annotations = list(fig.layout.annotations) + 
    [go.layout.Annotation(
            x=-0.07,y=0.5,font=dict(
                size=16,color = 'blue'
            ),showarrow=False,text="single y-axis title",textangle=-90,xref="paper",yref="paper"
        )
    ] +
    [go.layout.Annotation(
            x=0.5,y=-0.08,text="Dates",textangle=-0,yref="paper"
        )
    ]
)

fig.show()

解决方法

此答案分为五个部分：

  1. 隐藏子图标题（尽管不是100％确定要这样做...）
  2. 使用fig.layout[axis].tickfont = dict(color = 'rgba(0,0)')隐藏y轴刻度值
  3. 使用go.layout.Annotation(xref="paper",yref="paper")
    4. 设置单轴标签
  4. 情节人物
  5. 最后完成代码段

这里非常重要的一点是，您可以使用px引用（例如plotly.graph_object）编辑使用go.layout.XAxis函数生成的任何元素。

1。隐藏子图标题

如果您对设置fig的方式感到满意，则只需添加

for anno in fig['layout']['annotations']:
    anno['text']=''
    
fig.show()

2。隐藏yaxis文字

您可以循环执行以下操作，将yaxis标记字体设置为透明

fig.layout[axis].tickfont = dict(color = 'rgba(0,0)')

该精确的行包含在下面的代码段中，该行也删除了每个子图的y轴标题。

3。单轴标签

删除轴标签并包含单个标签需要更多的工作，但是这是一种非常灵活的设置，它可以完全满足您的需要，并且更多，如果您要编辑新的标签的任何方式：

# hide subplot y-axis titles and x-axis titles
for axis in fig.layout:
    if type(fig.layout[axis]) == go.layout.YAxis:
        fig.layout[axis].title.text = ''
    if type(fig.layout[axis]) == go.layout.XAxis:
        fig.layout[axis].title.text = ''
        
# keep all other annotations and add single y-axis and x-axis title:
fig.update_layout(
    # keep the original annotations and add a list of new annotations:
    annotations = list(fig.layout.annotations) + 
    [go.layout.Annotation(
            x=-0.07,y=0.5,font=dict(
                size=16,color = 'blue'
            ),showarrow=False,text="single y-axis title",textangle=-90,xref="paper",yref="paper"
        )
    ] +
    [go.layout.Annotation(
            x=0.5,y=-0.08,text="Dates",textangle=-0,yref="paper"
        )
    ]
)

fig.show()

4。情节

enter image description here

5。完整的代码：

import pandas as pd
import numpy as np
import plotly.express as px
import string
import plotly.graph_objects as go

# create a dataframe
cols = list(string.ascii_letters)
cols[0]='zzz'
n = 50

df = pd.DataFrame({'Date': pd.date_range('2021-01-01',periods=n)})

# create data with vastly different ranges
for col in cols:
    start = np.random.choice([1,10,100,1000,100000])
    s = np.random.normal(loc=0,scale=0.01*start,size=n)
    df[col] = start + s.cumsum()

# melt data columns from wide to long
dfm = df.melt("Date")

fig = px.line(
    data_frame=dfm,x = 'Date',y = 'value',facet_col = 'variable',facet_col_wrap=6,#facet_col_spacing=0.05,#facet_row_spacing=0.035,height = 1000,width = 1000,title = 'Value vs. Date'
)

fig.update_yaxes(matches=None,showticklabels=True,visible=True)
fig.update_annotations(font=dict(size=16))
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))

# subplot titles
for anno in fig['layout']['annotations']:
    anno['text']=''

# hide subplot y-axis titles and x-axis titles
for axis in fig.layout:
    if type(fig.layout[axis]) == go.layout.YAxis:
        fig.layout[axis].title.text = ''
    if type(fig.layout[axis]) == go.layout.XAxis:
        fig.layout[axis].title.text = ''
        
# keep all other annotations and add single y-axis and x-axis title:
fig.update_layout(
    # keep the original annotations and add a list of new annotations:
    annotations = list(fig.layout.annotations) + 
    [go.layout.Annotation(
            x=-0.07,yref="paper"
        )
    ]
)


fig.show()
,

作为对此的补充说明，我找到了一种更直接的方法，即使用labels参数从可绘制的快速调用中消除轴标签，并为我想要的标签提供一个带有''值的标签字典消除。

虽然这不会在整个图形级别上产生单个标签，但是如果图形标题足够描述“ Y vs. X”，那么可能会“借口”缺少轴标签吗？ （或添加为@vestland演示）

请注意，您可以“几乎”消除每个子分区中具有“ = value”的烦人的重复构面标题。也就是说，如果您再向标签dict添加一个条目：

'variable'：”

然后只获取构面变量级别，而不是“ variable = variable level”，如下图所示。

完整代码 

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

# create a dataframe
cols = list(string.ascii_letters)
n = 50

df = pd.DataFrame({'Date': pd.date_range('2021-01-01',size=n)
    df[col] = start + s.cumsum()

# melt data columns from wide to long
dfm = df.melt("Date")

# make the plot
fig = px.line(
    data_frame=dfm,facet_col_spacing=0.05,facet_row_spacing=0.035,title = 'Value vs. Date',labels = {
        'Date': '','value': '','variable': ''
    }
)

# ensure that each chart has its own y rage and tick labels
fig.update_yaxes(matches=None,visible=True)

fig.show()

enter image description here

axis-labelsexpressfacetplotlypython

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