python - 如何使所有子图在情节中具有相同的 xticks 和 yticks？
问题描述
我一直在尝试更改 xticks 和 yticks 好几个小时，但仍然无法使所有子图都具有相同的 xticks 和 yticks 范围。
必需的
- 所有子图 xticks 必须是
Jan Feb Mar Apr May Jun. - 所有子图 yticks 必须是
0.0% to 0.8% including both。
到目前为止我的代码
import numpy as np
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = pd.DataFrame({'Month': ['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01'],
'Site A': [0.0006171, 0.0007480000000000001, 0.00041139999999999997, 0.0005422999999999999, 9.35e-05, 0.0011407],
'Site B': [0.0003927000000000001, 0.0026, 0.0008041000000000001, 0.0005797, 0.0008789000000000001, 0.0004301000000000001],
'Site C': [0.0075548, 0.0045815000000000005, 0.0033473, 0.0016455999999999999, 0.0023375, 0.00229],
'Site D': [0.0007854000000000001, 0.0003927000000000001, 0.0013277, 0.0005235999999999999, 0.0008227999999999999, 0.0016082000000000002],
'Site E': [0.0, 0.0007480000000000001, 0.0, 0.0015520999999999998, 0.0005984000000000001, 0.00014],
'Site F': [0.0, 0.0007292999999999999, 0.0, 0.0002431, 0.0, 0.0],
'Site G': [0.0006919000000000001, 0.0008976000000000001, 0.0005422999999999999, 0.0007667, 0.0008414999999999999, 0.0008],
'Site H': [0.00257, 0.00324, 0.00512, 0.00197, 0.0009199999999999999, 0.0004301000000000001],
'Site I': [0.0013277, 0.0, 0.0, 0.0, 0.0, 0.0013277]})
df['Month'] = pd.to_datetime(df['Month'])
df = df.set_index('Month')
cols = ("Site H", "Site E", "Site B",
"Site C", "Site G", "Site F",
"Site D", "Site I", "Site A",
)
colors = [['darkgreen','limegreen','lightgreen'],
['black','gray','silver'],
['darkred','tomato','lightsalmon'],
]
fig = make_subplots(rows=3,cols=3,
start_cell='top-left',
column_widths = [1200]*3,
x_title = 'Month',
subplot_titles=cols
)
fig.add_scatter(x=df.index, y=df['Site H'], row=1, col=1, showlegend=False, line=dict(color=colors[0][0]), mode='lines+markers', name='Site H')
fig.add_scatter(x=df.index, y=df['Site E'], row=1, col=2, showlegend=False, line=dict(color=colors[0][1]))
fig.add_scatter(x=df.index, y=df['Site B'], row=1, col=3, showlegend=False, line=dict(color=colors[0][2]))
fig.add_scatter(x=df.index, y=df['Site C'], row=2, col=1, showlegend=False, line=dict(color=colors[1][0]))
fig.add_scatter(x=df.index, y=df['Site G'], row=2, col=2, showlegend=False, line=dict(color=colors[1][1]))
fig.add_scatter(x=df.index, y=df['Site F'], row=2, col=3, showlegend=False, line=dict(color=colors[1][2]))
fig.add_scatter(x=df.index, y=df['Site D'], row=3, col=1, showlegend=False, line=dict(color=colors[2][0]))
fig.add_scatter(x=df.index, y=df['Site I'], row=3, col=2, showlegend=False, line=dict(color=colors[2][1]))
fig.add_scatter(x=df.index, y=df['Site A'], row=3, col=3, showlegend=False, line=dict(color=colors[2][2]))
fig.update_xaxes(tickangle=90, tickformat="%b")
fig.update_yaxes(tickformat=".1%")
tickvals = [i/1000 for i in range(1,9)]
# fig.update_yaxes(row=1, col=1, title='Success', title_font_color="darkgreen", autorange = True, dtick=0.001)
fig.update_yaxes(row=1, col=1, title='Success', title_font_color=colors[0][0], autorange = True, tickmode='array', tickvals=tickvals)
fig.update_yaxes(row=2, col=1, title='Status Quo', title_font_color=colors[1][0], autorange = True, dtick=0.001)
fig.update_yaxes(row=3, col=1, title='Watch', title_font_color=colors[2][0], autorange = True, dtick=0.001)
fig.update_layout(
title='2020 Monthy Error Rate by Site',
title_x=0.5,
autosize=False,
width=800,
height=800,
margin=dict(
l=120,
r=30,
b=80,
t=80,
pad=0
),
paper_bgcolor="LightSteelBlue",
)
fig.add_annotation(dict(font=dict(color="black",size=14),
x=-0.16,
y=0.5,
showarrow=False,
text='Error Rate (%)',
textangle=-90,
xref="paper",
yref="paper"
)
)
fig.show()
我的输出
解决方案
我们可以分别处理每个部分。该.strftime('%b')方法将从日期时间中提取月份缩写。我们可以使用该.update方法设置fig['layout']['yaxis1'],...,fig['layout']['yaxis9']允许您访问每个子图的范围，并且我们希望将参数设置autorange为False. 把它放在一个循环中是最好的。
import numpy as np
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = pd.DataFrame({'Month': ['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01'],
'Site A': [0.0006171, 0.0007480000000000001, 0.00041139999999999997, 0.0005422999999999999, 9.35e-05, 0.0011407],
'Site B': [0.0003927000000000001, 0.0026, 0.0008041000000000001, 0.0005797, 0.0008789000000000001, 0.0004301000000000001],
'Site C': [0.0075548, 0.0045815000000000005, 0.0033473, 0.0016455999999999999, 0.0023375, 0.00229],
'Site D': [0.0007854000000000001, 0.0003927000000000001, 0.0013277, 0.0005235999999999999, 0.0008227999999999999, 0.0016082000000000002],
'Site E': [0.0, 0.0007480000000000001, 0.0, 0.0015520999999999998, 0.0005984000000000001, 0.00014],
'Site F': [0.0, 0.0007292999999999999, 0.0, 0.0002431, 0.0, 0.0],
'Site G': [0.0006919000000000001, 0.0008976000000000001, 0.0005422999999999999, 0.0007667, 0.0008414999999999999, 0.0008],
'Site H': [0.00257, 0.00324, 0.00512, 0.00197, 0.0009199999999999999, 0.0004301000000000001],
'Site I': [0.0013277, 0.0, 0.0, 0.0, 0.0, 0.0013277]})
df['Month'] = pd.to_datetime(df['Month'])
df['Month'] = [mydate.strftime('%b') for mydate in df['Month']]
df = df.set_index('Month')
cols = ("Site H", "Site E", "Site B",
"Site C", "Site G", "Site F",
"Site D", "Site I", "Site A",
)
colors = [['darkgreen','limegreen','lightgreen'],
['black','gray','silver'],
['darkred','tomato','lightsalmon'],
]
fig = make_subplots(rows=3,cols=3,
start_cell='top-left',
column_widths = [1200]*3,
x_title = 'Month',
subplot_titles=cols
)
fig.add_scatter(x=df.index, y=df['Site H'], row=1, col=1, showlegend=False, line=dict(color=colors[0][0]), mode='lines+markers', name='Site H')
fig.add_scatter(x=df.index, y=df['Site E'], row=1, col=2, showlegend=False, line=dict(color=colors[0][1]))
fig.add_scatter(x=df.index, y=df['Site B'], row=1, col=3, showlegend=False, line=dict(color=colors[0][2]))
fig.add_scatter(x=df.index, y=df['Site C'], row=2, col=1, showlegend=False, line=dict(color=colors[1][0]))
fig.add_scatter(x=df.index, y=df['Site G'], row=2, col=2, showlegend=False, line=dict(color=colors[1][1]))
fig.add_scatter(x=df.index, y=df['Site F'], row=2, col=3, showlegend=False, line=dict(color=colors[1][2]))
fig.add_scatter(x=df.index, y=df['Site D'], row=3, col=1, showlegend=False, line=dict(color=colors[2][0]))
fig.add_scatter(x=df.index, y=df['Site I'], row=3, col=2, showlegend=False, line=dict(color=colors[2][1]))
fig.add_scatter(x=df.index, y=df['Site A'], row=3, col=3, showlegend=False, line=dict(color=colors[2][2]))
fig.update_xaxes(tickangle=90, tickformat="%b", type='category')
fig.update_yaxes(tickformat=".1%")
tickvals = [i/1000 for i in range(1,9)]
# fig.update_yaxes(row=1, col=1, title='Success', title_font_color="darkgreen", autorange = True, dtick=0.001)
fig.update_yaxes(row=1, col=1, title='Success', title_font_color=colors[0][0], autorange = True, tickmode='array', tickvals=tickvals)
fig.update_yaxes(row=2, col=1, title='Status Quo', title_font_color=colors[1][0], autorange = True, tickmode='array', tickvals=tickvals)
fig.update_yaxes(row=3, col=1, title='Watch', title_font_color=colors[2][0], autorange = True, tickmode='array', tickvals=tickvals)
# updates fig['layout']['yaxis1']... fig['layout']['yaxis9']
for i in range(1,10):
fig['layout']['yaxis' + str(i)].update(range=[0, 0.008], autorange=False)
fig.update_layout(
title='2020 Monthy Error Rate by Site',
title_x=0.5,
autosize=False,
width=800,
height=800,
margin=dict(
l=120,
r=30,
b=80,
t=80,
pad=0
),
paper_bgcolor="LightSteelBlue",
)
fig.add_annotation(dict(font=dict(color="black",size=14),
x=-0.16,
y=0.5,
showarrow=False,
text='Error Rate (%)',
textangle=-90,
xref="paper",
yref="paper"
)
)
fig.show()
推荐阅读
- node.js - 订阅不适用于 Apollo Server Express
- django - 我正在尝试使用 gcs 谷歌云存储在 heroku 上部署我的 django 应用程序
- python - 如何使用 matplotlib 反转函数的轴
- javascript - bot 的用户名未定义
- python - RDFLIB Python 是否支持 Geosparql 查询？
- javascript - 在不使用 JSON.stringify 的情况下将数组转换为有效的 JSON 字符串？
- php - 如何从不同的表中获取相关的记录项
- arrays - 从数组中获取某些元素
- c# - 尝试使用 selectedChanged 在 C# 的 WPF 中的文本框中显示组合框元素
- google-apps-script - 通过脚本打印到谷歌电子表格聊天/记录所有用户可见的值