Plotly:如何添加多个 y 轴? [英] Plotly: How to add multiple y-axes?
问题描述
我有 5 个不同列的数据，它们的值各不相同.
实际发电存储太阳能发电总发电频率1464 1838 1804 18266 512330 2262 518 4900 512195 923 919 8732 492036 1249 1316 3438 482910 534 1212 4271 47857 2452 1272 6466 502331 990 2729 14083 512604 767 2730 19037 47993 2606 705 17314 512542 213 548 10584 522030 942 304 11578 52562 414 2870 840 521111 1323 337 19612 491863 2498 1992 18941 481575 2262 1576 3322 481223 657 661 10292 471850 1920 2986 10130 482786 1119 933 2680 522333 1245 1909 14116 481606 2934 1547 13767 51因此，在此数据中，我想绘制一个具有 3 y 轴的图形.第一个用于频率，第二个用于Total Gen，第三个用于Actual gen、Storage 和太阳能发电.频率应在次 Y 轴(右侧)，其余应在左侧.
对于频率，您可以看到值在 47 到 52 之间非常随机，这就是为什么它应该在右侧，如下所示:
与其他人相比，总 Gen 值非常高，因为它们从 100 到 20000，所以我无法将它们与其他人合并，如下所示:我要:
Y 轴标题 1 = 实际发电量、存储量和太阳能发电量
Y 轴标题 2 = 总代
Y 轴标题 3 = 频率
我的方法:
导入日志将熊猫导入为 pd导入 plotly.graph_objs as go导入 plotly.offline 作为 pyo将 xlwings 导入为 xw从 plotly.subplots 导入 make_subplotsapp = xw.App(visible=False)尝试:wb = app.books.open('2020 10 08 0000 (Float).xlsx')sheet = wb.sheets[0]actual_gen = sheet.range('A2:A21').value频率 = sheet.range('E2:E21').valuestorage = sheet.range('B2:B21').valuetotal_gen = sheet.range('D2:D21').valuesolar_gen = sheet.range('C2:C21').value除了作为 e 的例外:logging.exception(发生了可怕的事情！")打印(e)最后:应用程序退出()app.kill()# 创建带有辅助 y 轴的图形fig = make_subplots(specs=[[{secondary_y": True}]])# 添加痕迹fig.add_trace(go.Scatter(y=storage, name=BESS(KW)"),)fig.add_trace(go.Scatter(y=actual_gen, name=Act(KW)"),)fig.add_trace(go.Scatter(y=solar_gen, name=太阳能发电"))fig.add_trace(go.Scatter(x=x_values, y=total_gen, name=Total Gen",yaxis = 'y2'))fig.add_trace(go.Scatter(y=frequency, name=Frequency",yaxis = 'y1'),)fig.update_layout( title_text = '8th oct BESS',yaxis2=dict(title=BESS(KW)",titlefont=dict(color=red"),tickfont=dict(color=red")),yaxis3=dict(title=Actual Gen(KW)",titlefont=dict(color=orange"),tickfont=dict(color=orange"),anchor=free",overlaying=";y2", side=left"),yaxis4=dict(title=Solar Gen(KW)",titlefont=dict(color=粉红色"),tickfont=dict(color=粉红色"),anchor=x2",overlaying=";y2", side=left"),yaxis5=dict(title=Total Gen(KW)",titlefont=dict(color=cyan"),tickfont=dict(color=cyan"),anchor=free",overlaying=";y2", side=left"),yaxis6=dict(title=Frequency",titlefont=dict(color=purple"),tickfont=dict(color=purple"),anchor=free",overlaying=y2", side=正确"))图.show()有人可以帮忙吗?
以下是如何创建多级 y 轴的示例.
本质上，关键在于:
- 在
layoutdict 中为每个轴创建一个键，然后为该轴分配一条轨迹. - 将
xaxisdomain设置为比[0, 1]窄(例如[0.2, 1]code>)，从而将图形的左边缘向右推，为多级 y 轴腾出空间.
评论(TL;DR):
此处显示的示例代码使用较低级别的 Plotly API，而不是诸如 graph_objects 或 express 之类的便利包装器.原因是我(个人)认为向用户展示幕后"发生的事情会有所帮助，而不是使用方便的包装器来掩盖底层代码逻辑.
这样，当用户需要修改图形的更精细细节时，他们将更好地理解Plotly正在构建的list和dict用于底层图形引擎 (orca).
I have data with 5 different columns and their value varies from each other.
Actual gen Storage Solar Gen Total Gen Frequency
1464 1838 1804 18266 51
2330 2262 518 4900 51
2195 923 919 8732 49
2036 1249 1316 3438 48
2910 534 1212 4271 47
857 2452 1272 6466 50
2331 990 2729 14083 51
2604 767 2730 19037 47
993 2606 705 17314 51
2542 213 548 10584 52
2030 942 304 11578 52
562 414 2870 840 52
1111 1323 337 19612 49
1863 2498 1992 18941 48
1575 2262 1576 3322 48
1223 657 661 10292 47
1850 1920 2986 10130 48
2786 1119 933 2680 52
2333 1245 1909 14116 48
1606 2934 1547 13767 51
So in from this data I want to plot a graph with 3 y-axis. One for the frequency, second for the Total Gen and third is for Actual gen, Storage and Solar Gen.
Frequency should be on the secondary Y-axis(Right side) and the Rest of them should be on the left side.
For frequency as you can see the values are very random between 47 to 52 that's why it should be on the right side, like this:
For Total Gen value are very high as compared to others as they are from 100-20000 so that's I can't merge them with others, something like this: Here I want:
Y-axis title 1 = Actual gen, Storage, and Solar gen
Y-axis title 2 = Total gen
Y-axis title 3 = Frequency
My approach:
import logging
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
import xlwings as xw
from plotly.subplots import make_subplots
app = xw.App(visible=False)
try:
wb = app.books.open('2020 10 08 0000 (Float).xlsx')
sheet = wb.sheets[0]
actual_gen = sheet.range('A2:A21').value
frequency = sheet.range('E2:E21').value
storage = sheet.range('B2:B21').value
total_gen = sheet.range('D2:D21').value
solar_gen = sheet.range('C2:C21').value
except Exception as e:
logging.exception("Something awful happened!")
print(e)
finally:
app.quit()
app.kill()
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(
go.Scatter(y=storage, name="BESS(KW)"),
)
fig.add_trace(
go.Scatter(y=actual_gen, name="Act(KW)"),
)
fig.add_trace(
go.Scatter(y=solar_gen, name="Solar Gen")
)
fig.add_trace(
go.Scatter(x=x_values, y=total_gen, name="Total Gen",yaxis = 'y2')
)
fig.add_trace(
go.Scatter(y=frequency, name="Frequency",yaxis = 'y1'),
)
fig.update_layout( title_text = '8th oct BESS',
yaxis2=dict(title="BESS(KW)",titlefont=dict(color="red"), tickfont=dict(color="red")),
yaxis3=dict(title="Actual Gen(KW)",titlefont=dict(color="orange"),tickfont=dict(color="orange"), anchor="free", overlaying="y2", side="left"),
yaxis4=dict(title="Solar Gen(KW)",titlefont=dict(color="pink"),tickfont=dict(color="pink"), anchor="x2",overlaying="y2", side="left"),
yaxis5=dict(title="Total Gen(KW)",titlefont=dict(color="cyan"),tickfont=dict(color="cyan"), anchor="free",overlaying="y2", side="left"),
yaxis6=dict(title="Frequency",titlefont=dict(color="purple"),tickfont=dict(color="purple"), anchor="free",overlaying="y2", side="right"))
fig.show()
Can someone please help?
Here is an example of how multi-level y-axes can be created.
Essentially, the keys to this are:
- Create a key in the
layoutdict, for each axis, then assign a trace to the that axis. - Set the
xaxisdomainto be narrower than[0, 1](for example[0.2, 1]), thus pushing the left edge of the graph to the right, making room for the multi-level y-axis.
A link to the official Plotly docs on the subject.
To make reading the data easier for this demonstration, I have taken the liberty of storing your dataset as a CSV file, rather than Excel - then used the pandas.read_csv() function to load the dataset into a pandas.DataFrame, which is then passed into the plotting functions as data columns.
Example:
Read the dataset:
df = pd.read_csv('energy.csv')
Sample plotting code:
layout = {'title': '8th Oct BESS'}
traces = []
traces.append({'y': df['storage'], 'name': 'Storage'})
traces.append({'y': df['actual_gen'], 'name': 'Actual Gen'})
traces.append({'y': df['solar_gen'], 'name': 'Solar Gen'})
traces.append({'y': df['total_gen'], 'name': 'Total Gen', 'yaxis': 'y2'})
traces.append({'y': df['frequency'], 'name': 'Frequency', 'yaxis': 'y3'})
layout['xaxis'] = {'domain': [0.12, 0.95]}
layout['yaxis1'] = {'title': 'Actual Gen, Storage, Solar Gen', 'titlefont': {'color': 'orange'}, 'tickfont': {'color': 'orange'}}
layout['yaxis2'] = {'title': 'Total Gen', 'side': 'left', 'overlaying': 'y', 'anchor': 'free', 'titlefont': {'color': 'red'}, 'tickfont': {'color': 'red'}}
layout['yaxis3'] = {'title': 'Frequency', 'side': 'right', 'overlaying': 'y', 'anchor': 'x', 'titlefont': {'color': 'purple'}, 'tickfont': {'color': 'purple'}}
pio.show({'data': traces, 'layout': layout})
Graph:
Given the nature of these traces, they overlay each other heavily, which could make graph interpretation difficult.
A couple of options are available:
Change the
rangeparameter for each y-axis so the axis only occupies a portion of the graph. For example, if a dataset ranges from 0-5, set the correspondingyaxisrangeparameter to[-15, 5], which will push that trace near the top of the graph.Consider using subplots, where like-traces can be grouped ... or each trace can have it's own graph. Here are Plotly's docs on subplots.
Comments (TL;DR):
The example code shown here uses the lower-level Plotly API, rather than a convenience wrapper such as graph_objects or express. The reason is that I (personally) feel it's helpful to users to show what is occurring 'under the hood', rather than masking the underlying code logic with a convenience wrapper.
This way, when the user needs to modify a finer detail of the graph, they will have a better understanding of the lists and dicts which Plotly is constructing for the underlying graphing engine (orca).
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