在Python中创建“相对”和“分组”图表的组合

Question

我需要在绘图中创建“相对”和“分组”图表的组合。

我弄清楚了如何使用以下代码创建堆叠和分组：

from plotly import graph_objects as go

import plotly

pyplt = plotly.offline.plot

data = {

    "Sports_19": [15, 23, 32, 10, 23, 22, 32, 24],

    "Casual_19": [4, 12, 11, 14, 15, 12, 22, 14],

    "Yoga_19": [4, 8, 18, 6, 12, 11, 10, 4],

    "Sports_20": [11, 18, 18, 0, 20, 12, 12, 11],

    "Casual_20": [20, 10, 9, 6, 10, 11, 17, 22],

    "Yoga_20": [11, 18, 18, 0, 20, 12, 12, 11],

    "labels": ["January", "February", "March", "April", "May", 'June', 'July', "August"]

    

}

fig = go.Figure()

fig.add_trace(go.Bar(name="Sports",x=data["labels"],y=data["Sports_19"],offsetgroup=19,marker_color='lightsalmon',text=data["Sports_19"],textposition='auto'))

fig.add_trace(go.Bar(name="Casual",x=data['labels'],y=data['Casual_19'],offsetgroup=19,base=data['Sports_19'],marker_color='crimson',text=data["Casual_19"],textposition='auto'))

fig.add_trace(go.Bar(name="Yoga",x=data['labels'],y=data['Yoga_19'],marker_color='indianred',text=data["Yoga_19"],textposition='auto',offsetgroup=19,base=[val1 + val2 for val1, val2 in zip(data["Sports_19"], data["Casual_19"])]))

fig.add_trace(go.Bar(name="Sports_20",x=data["labels"],y=data["Sports_20"],offsetgroup=20,marker_color='lightsalmon',showlegend=False,text=data["Sports_20"],textposition='auto'))

fig.add_trace(go.Bar(name="Casual_20",x=data['labels'],y=data['Casual_20'],offsetgroup=20,base=data['Sports_20'],marker_color='crimson',showlegend=False,text=data["Casual_20"],textposition='auto'))

fig.add_trace(go.Bar(name="Yoga_20", x=data['labels'], y=data['Yoga_20'], marker_color='indianred', text=data["Yoga_20"], showlegend=False, textposition='auto', offsetgroup=20, base=[val1 + val2 for val1, val2 in zip(data["Sports_20"], data["Casual_20"])]))

fig.update_layout(title="2019 vs 2020 Sales by Category",yaxis_title="Sales amount in US$")

fig.show()

pyplt(fig, auto_open=True)

输出是这样的：

有什么方法可以将此图转换为“相对”和“分组”的组合吗？可能不是用plotly，而是用matplotlib或其他工具？

ps 这是“相对图”的示例（但未分组）：

Answer 1

也许最直接的方法是创建两个新的数据框df_perc_19并df_perc_20存储数据，将其标准化为每年每个月的相对百分比，四舍五入到两位数，因为.round(2)长小数会导致文本的默认方向发生变化 - 感觉您可以随意调整。text然后访问这些新数据帧中的值以进行跟踪，尽管它很难看，但您可以使用以下方法获取要显示的参数的百分比：text=[str(x)+"%" for x in df_perc_19["Casual_19"]]import pandas as pdimport plotlyfrom plotly import graph_objects as go# pyplt = plotly.offline.plotdata = {    "Sports_19": [15, 23, 32, 10, 23, 22, 32, 24],    "Casual_19": [4, 12, 11, 14, 15, 12, 22, 14],    "Yoga_19": [4, 8, 18, 6, 12, 11, 10, 4],    "Sports_20": [11, 18, 18, 0, 20, 12, 12, 11],    "Casual_20": [20, 10, 9, 6, 10, 11, 17, 22],    "Yoga_20": [11, 18, 18, 0, 20, 12, 12, 11],    # "labels": ["January", "February", "March", "April", "May", 'June', 'July', "August"]    }labels = ["January", "February", "March", "April", "May", 'June', 'July', "August"]df = pd.DataFrame(data=data,index=labels)## normalize data for the months of 2019, and the months of 2020df_perc_19 = df.apply(lambda x: 100*x[["Sports_19","Casual_19","Yoga_19"]] / x[["Sports_19","Casual_19","Yoga_19"]].sum(),axis=1).round(2)df_perc_20 = df.apply(lambda x: 100*x[["Sports_20","Casual_20","Yoga_20"]] / x[["Sports_20","Casual_20","Yoga_20"]].sum(),axis=1).round(2)fig = go.Figure()## traces for 2019fig.add_trace(go.Bar(name="Sports",x=labels,y=df_perc_19["Sports_19"],offsetgroup=19,marker_color='lightsalmon',text=[str(x)+"%" for x in df_perc_19["Sports_19"]],textposition='auto'))fig.add_trace(go.Bar(name="Casual",x=labels,y=df_perc_19['Casual_19'],offsetgroup=19,base=df_perc_19['Sports_19'],marker_color='crimson',text=[str(x)+"%" for x in df_perc_19["Casual_19"]],textposition='auto'))fig.add_trace(go.Bar(name="Yoga",x=labels,y=df_perc_19['Yoga_19'],marker_color='indianred',text=[str(x)+"%" for x in df_perc_19["Yoga_19"]],textposition='auto',offsetgroup=19,base=[val1 + val2 for val1, val2 in zip(df_perc_19["Sports_19"], df_perc_19["Casual_19"])]))## traces for 2020fig.add_trace(go.Bar(name="Sports_20",x=labels,y=df_perc_20["Sports_20"],offsetgroup=20,marker_color='lightsalmon',showlegend=False,text=[str(x)+"%" for x in df_perc_20["Sports_20"]] ,textposition='auto'))fig.add_trace(go.Bar(name="Casual_20",x=labels,y=df_perc_20['Casual_20'],offsetgroup=20,base=df_perc_20['Sports_20'],marker_color='crimson',showlegend=False,text=[str(x)+"%" for x in df_perc_20["Casual_20"]],textposition='auto'))fig.add_trace(go.Bar(name="Yoga_20", x=labels, y=df_perc_20['Yoga_20'], marker_color='indianred', text=[str(x)+"%" for x in df_perc_20["Yoga_20"]], showlegend=False, textposition='auto', offsetgroup=20, base=[val1 + val2 for val1, val2 in zip(df_perc_20["Sports_20"], df_perc_20["Casual_20"])]))fig.update_layout(title="2019 vs 2020 Sales by Category",yaxis_title="Sales amount in US$ (percentage)")fig.show()# pyplt(fig, auto_open=True)