如何解决情节: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:
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]))
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:
- 隐藏子图标题(尽管不是100%确定要这样做...)
- 使用
fig.layout[axis].tickfont = dict(color = 'rgba(0,0)')
隐藏y轴刻度值 - 使用
go.layout.Annotation(xref="paper",yref="paper")
设置单轴标签
- 情节人物
- 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
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()
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。