情节：如何在带有刻面的情节表达图中隐藏轴标题？

Question

有没有一种简单的方法可以使用 plotly express 隐藏多面图中的重复轴标题？我试过设置

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，我想将隐藏重复的轴标题设置为一般多面图的默认设置（或者甚至更好，只是默认为整个多面图显示单个 x 和 y 轴标题。

这里是测试代码：

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]))

最终代码（已接受的答案）。注意情节 >= 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', 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]))

# 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,
            font=dict(
                size=16, color = 'blue'
            ),
            showarrow=False,
            text="Dates",
            textangle=-0,
            xref="paper",
            yref="paper"
        )
    ]
)

fig.show()

Answer 1

这个答案有五个部分：

1. 隐藏子情节标题（虽然不是 100% 确定你想这样做...）
2. 使用fig.layout[axis].tickfont = dict(color = 'rgba(0,0,0,0)')隐藏 y 轴刻度值
3. 使用go.layout.Annotation(xref="paper", yref="paper")设置单轴标签
4. 情节人物
5. 完整的代码sn-p在最后

这里有一个非常重要的收获是，您可以使用plotly.graph_object 引用编辑使用px 函数生成的任何元素，例如go.layout.XAxis。

---

1。隐藏子情节标题

如果您对设置 fig 的方式感到满意，您可以添加

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

2。隐藏 yaxis 文本

您可以在循环中使用以下命令将 yaxis tickfont 设置为透明

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

确切的线包含在下面的 sn-p 中，它也删除了每个子图的 y 轴标题。

3。单轴标签

删除轴标签和包含单个标签需要更多工作，但这里有一个非常灵活的设置，可以完全满足您的需求，更多如果您想编辑新的任何方式的标签：

# 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,
            font=dict(
                size=16, color = 'blue'
            ),
            showarrow=False,
            text="Dates",
            textangle=-0,
            xref="paper",
            yref="paper"
        )
    ]
)

fig.show()

4。情节

5。完整代码：

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,
            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,
            font=dict(
                size=16, color = 'blue'
            ),
            showarrow=False,
            text="Dates",
            textangle=-0,
            xref="paper",
            yref="paper"
        )
    ]
)


fig.show()

Answer 2

作为旁注，我找到了一种更直接的方法，可以使用 labels 参数从 plotly express 调用中消除轴标签，并为我想要的标签提供值为 '' 的标签字典消除。

虽然这不会导致整体图形级别的单个标签，但如果图形标题足以描述“Y vs. X”，那么可能缺少轴标签可以“原谅”？ （或如@vestland 演示的那样添加）

请注意，您可以“几乎”消除在每个子批次中具有“=value”的烦人重复方面标题。即，如果您在标签字典中再添加一个条目：

'变量'：''

然后，您只需获取 facet 变量 level，而不是获取“variable=variable level”，前面是“=”，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', 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")

# make the plot
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',
    labels = {
        'Date': '',
        'value': '',
        'variable': ''
    }
)

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

fig.show()