如何在破折号上绘制重叠/重叠条形图？

Question

我需要改变堆积条形图宽度重叠。 我发现了这个问题和解决方案How to plot a superimposed bar chart using matplotlib in python?，我想在DASH Plotly python框架上复制相同的图表。在

代码如下：

import matplotlib.pyplot as plt
import numpy as np

width = 0.8

highPower   = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,
               1419.34,1415.13,1182.70,1165.17]
lowPower    = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,
               1138.70,1104.12,1012.95,1000.36]

indices = np.arange(len(highPower))

plt.bar(indices, highPower, width=width, 
        color='b', label='Max Power in mW')
plt.bar([i+0.25*width for i in indices], lowPower, 
        width=0.5*width, color='r', alpha=0.5, label='Min Power in mW')

plt.xticks(indices+width/2., 
           ['T{}'.format(i) for i in range(len(highPower))] )

plt.legend()
plt.show()

问：如何编辑以适应短跑原则？ 例如，在破折号上，bar不接受width=0.5*width adn alpha=0.5 谢谢。在

我自己的代码如下：

^{pr2}$

@Teoretic提出的在两个记录道上使用base=0并使用barmode='stack'的解决方案不起作用。 谢谢。在

Answer 1

编辑编辑答案以使用添加到问题中的新数据

您可以通过以下两个步骤绘制重叠条形图：
1） 将布局中的barmode设置为“stack”
2） 将每个条形图的基数设置为0
3） 设置为X值的小数值

您可能还想玩一下：
1） 将第二个条形图的“width”参数设置为适合您的值
2） 使“X”轴数据的标签更适合您

示例代码（在Jupyter笔记本中运行）：

from plotly.offline import init_notebook_mode, iplot
from plotly import graph_objs as go
init_notebook_mode(connected = True)
import pandas as pd
import numpy as np


dfb=pd.read_csv('https://www.dropbox.com/s/90y07129zn351z9/test_data.csv?dl=1', encoding="latin-1", infer_datetime_format=True, parse_dates=['date'], skipinitialspace=True)
dfb["date"]=pd.to_datetime(dfb['date']) 
dfb["site"]=dfb["site"].astype("category")
cm_inc=dfb[dfb.site == 5].pivot_table(index='date', values = 'site', aggfunc = {  'site' : 'count' }  )
dfb['cm_target'] = [40]*len(dfb)  
dfb.to_csv('test_data.csv', index=False)

# You need small int indexes for "width" and "base" = 0 trick to work
indexes = [int(i.timestamp()) / 10000 for i in cm_inc.index] 
# For string dates labels
dates_indexes = [str(i) for i in cm_inc.index]

data = [
    go.Bar(x=indexes, 
           y=dfb['cm_target'], 
           name='Target Site A', 
           base=0
          ),
    go.Bar(x=indexes, 
           y=cm_inc['site'],
           name='Enroll Site A', 
           base=0,
           width=5  # Width value varies depending on number of samples in data
           )
]

layout = go.Layout(
    barmode='stack',
    xaxis=dict(
        showticklabels=True,
        ticktext=dates_indexes,
        tickvals=[i for i in indexes],
    )
)

fig = dict(data = data, layout = layout)
iplot(fig, show_link=False)

Answer 2

from plotly.offline import init_notebook_mode, iplot
from plotly import graph_objs as go
init_notebook_mode(connected = True)
import pandas as pd
import numpy as np
from datetime import timedelta, datetime, tzinfo
import time
from datetime import datetime as dt


dfb=pd.read_csv('https://www.dropbox.com/s/90y07129zn351z9/test_data.csv?dl=1', encoding="latin-1", infer_datetime_format=True, parse_dates=['date'], skipinitialspace=True)
dfb["date"]=pd.to_datetime(dfb['date']) 

dfb["site"]=dfb["site"].astype("category")
cm_inc=dfb[dfb.site == 5].pivot_table(index='date', values = 'site', aggfunc = {  'site' : 'count' }  )
dfb['cm_target'] = [40]*len(dfb)  
dfb.to_csv('test_data.csv', index=False)

# You need small int indexes for "width" and "base" = 0 trick to work
#indexes = [int(i.timestamp()) / 10000 for i in cm_inc.index] 
indexes =pd.to_datetime(cm_inc.index) 
# For string dates labels
#dates_indexes = [str(i) for i in cm_inc.index]
dates_indexes = pd.to_datetime(cm_inc.index) 

data = [
    go.Bar(x=indexes, 
           y=dfb['cm_target'], 
           name='Target Site A', 
           base=0
          ),
    go.Bar(x=indexes, 
           y=cm_inc['site'],
           name='Enroll Site A', 
           base=0,
           #width=2  # Width value varies depending on number of samples in data
           )
]

layout = go.Layout(
    barmode='stack',
    xaxis=dict(
        showticklabels=True,
        ticktext=dates_indexes,
        tickvals=[i for i in indexes],
    )
)

fig = dict(data = data, layout = layout)
iplot(fig, show_link=False)