def bar(x,y,text,marker,orientation,name):
    barchart=[go.Bar(x=x,y=y,text=text,marker=marker,orientation=orientation,name=name)]
    ........

fig = go.Figure()

# Add Traces

fig.add_trace(
    go.Scatter(x=list(df.index),
               y=list(df.High),
               name="High",
               line=dict(color="#33CFA5")))

fig.add_trace(
    go.Scatter(x=list(df.index),
               y=[df.High.mean()] * len(df.index),
               name="High Average",
               visible=False,
               line=dict(color="#33CFA5", dash="dash")))

fig.add_trace(
    go.Scatter(x=list(df.index),
               y=list(df.Low),
               name="Low",
               line=dict(color="#F06A6A")))fig.update_layout(
    updatemenus=[
        go.layout.Updatemenu(
            active=0,
            buttons=list([
                dict(label="None",
                     method="update",
                     args=[{"visible": [True, False, True, False]},
                           {"title": "Yahoo",
                            "annotations": []}]),
                dict(label="High",
                     method="update",
                     args=[{"visible": [True, True, False, False]},
                           {"title": "Yahoo High",
                            "annotations": high_annotations}]),
                dict(label="Low",
                     method="update",
                     args=[{"visible": [False, False, True, True]},
                           {"title": "Yahoo Low",
                            "annotations": low_annotations}]),

            ]),
        )
    ])

# Set title
fig.update_layout(title_text="Yahoo")

fig.show()

您可以轻松地遍历数据框的各列，并为它们的每一个创建跟踪，如下面的代码片段所示。

# crate traces
traces={}
for col in df.columns:
    traces['trace_' + col]=go.Bar(x=df.index, name=col, y=df[col])

# convert data to form required by plotly
data=list(traces.values())

# build figure
fig=go.Figure(data)
fig.show()

---

与OP进行评论和聊天之后，编辑建议。

如果没有可重现的数据样本，很难提出理想的解决方案。但这是可重复使用的建议，其含义是：

（1）： 关于源数据帧中的列数，它很灵活，并使用for循环根据请求添加跟踪，

（2）： 计算每列的max（）和min（），

（3）： 它是一个函数结构，可以轻松应用于任何数据框。

我整理了一些示例数据，如下所示：

情节1：

代码1：

# Imports
import pandas as pd
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import numpy as np

# data
humid = pd.Series(np.random.uniform(low=25, high=40, size=6).tolist())
windy = pd.Series(np.random.uniform(low=40, high=60, size=6).tolist())
df = pd.concat([humid,windy], axis = 1)
df.columns=['Humidity', 'windspeed']
df.index = ['Shanghai', 'Houston', 'Venice', 'Munich', 'Milan', 'Turin']


def plotMaxMin(df):
    for col in df.columns:
        #print(df[col].max())
        df[col+'_max']=df[col].max()
        df[col+'_min']=df[col].min()

    # crate traces
    traces={}
    for col in df.columns:
        traces['trace_' + col]=go.Scatter(x=df.index, name=col, y=df[col])

    # convert data to form required by plotly
    data=list(traces.values())

    # build figure
    fig=go.Figure(data)
    fig.show()

plotMaxMin(df=df)

使用编辑的数据框测试可重用性：

情节2：

代码2：

df2=df.copy(deep=True)
df2['Temperature']=pd.Series(np.random.uniform(low=-5, high=40, size=6).tolist())

plotMaxMin(df2)

我们会错过的updatemnu()。实际上，仅单击系列名称，该情节仍然具有很强的交互性。

使用go.layout.Updatemenu（）测试

这需要更多的调整才能变得完美，但是主要功能似乎已经到位，因此我希望您能够添加一些东西以使其像数据集一样完美。

情节3：

代码3：

# Imports
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# data
humid = pd.Series(np.random.uniform(low=25, high=40, size=6).tolist())
windy = pd.Series(np.random.uniform(low=40, high=60, size=6).tolist())
df = pd.concat([humid,windy], axis = 1)
df.columns=['Humidity', 'windspeed']
df.index = ['Shanghai', 'Houston', 'Venice', 'Munich', 'Milan', 'Turin']


def plotMaxMin(df):
    for col in df.columns:
        #print(df[col].max())
        df[col+'_max']=df[col].max()
        df[col+'_min']=df[col].min()

    # crate traces
    traces={}
    for col in df.columns:
        traces['trace_' + col]=go.Scatter(x=df.index, name=col, y=df[col])

    # convert data to form required by plotly
    data=list(traces.values())

    # build figure
    fig=go.Figure(data)

    # add dropdown functionality

    fig.update_layout(
    updatemenus=[
        go.layout.Updatemenu(
            active=0,
            buttons=list([
                dict(label="None",
                     method="update",
                     args=[{"visible": [True, False, True, False]},
                           {"title": "Yahoo",
                            "annotations": []}]),
                dict(label="High",
                     method="update",
                     args=[{"visible": [True, True, False, False]},
                           {"title": "Yahoo High",
                            "annotations": high_annotations}]),
                dict(label="Low",
                     method="update",
                     args=[{"visible": [False, False, True, True]},
                           {"title": "Yahoo Low",
                            "annotations": high_annotations}]),

            ]),
        )
    ])



    fig.show()

plotMaxMin(df=df)

编辑2：有关如何使用更多参数扩展功能以自定义图形的示例

情节4：

代码4：

# Imports
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# data
humid = pd.Series(np.random.uniform(low=25, high=40, size=6).tolist())
windy = pd.Series(np.random.uniform(low=40, high=60, size=6).tolist())
df = pd.concat([humid,windy], axis = 1)
df.columns=['Humidity', 'Windspeed']
df.index = ['Shanghai', 'Houston', 'Venice', 'Munich', 'Milan', 'Turin']


def plotMaxMin(df, colors):
    """Adds max and min for all df columns and plots the data using plotly

    Arguments:
    ==========
    df - pandas dataframe
    colors - dictionary with single word to identify line category and assign color

    Example call:
    =============
    plotMaxMin(df=df, colors={'wind':'#33CFA5', 'humidity':'#F06A6A'})

    """


    # add max and min for each input column
    for col in df.columns:
        df[col+'_max']=df[col].max()
        df[col+'_min']=df[col].min()

    # sort df columns by name
    df = df.reindex(sorted(df.columns), axis=1)

    # crate traces
    traces={}
    for col in df.columns:

        # format traces
        if 'Humid' in col:
            linecolor = colors['humidity']

        if 'Wind' in col:
            linecolor = colors['wind']

        traces['trace_' + col]=go.Scatter(x=df.index, name=col, y=df[col], line=dict(color=linecolor))

    # convert data to form required by plotly
    data=list(traces.values())

    # build figure
    fig=go.Figure(data)

    # uncomment bloew section to add dropdown functionality

    #fig.update_layout(
    #updatemenus=[
    #    go.layout.Updatemenu(
    #        active=0,
    #        buttons=list([
    #            dict(label="None",
    #                 method="update",
    #                 args=[{"visible": [True, False, True, False]},
    #                       {"title": "Yahoo",
    #                        "annotations": []}]),
    #            dict(label="High",
    #                 method="update",
    #                 args=[{"visible": [True, True, False, False]},
    #                       {"title": "Yahoo High",
    #                        "annotations": high_annotations}]),
    #            dict(label="Low",
    #                 method="update",
    #                 args=[{"visible": [False, False, True, True]},
    #                       {"title": "Yahoo Low",
    #                        "annotations": high_annotations}]),
    #        ]),
    #    )
    #])



    fig.show()

plotMaxMin(df=df, colors={'wind':'#33CFA5', 'humidity':'#F06A6A'})