具有相同X轴和多个Y轴且数据数量未定义的绘图

Question

我开发了一个软件，可以从csv文件中读取数据，并在带有多个Y轴的图表中显示数据。 对于csv文件中的每个信号，我在图表中有一条线，并且图表中的每一条线都有一个轴

如果csv文件中包含的信号数量未知，因此有六个以上（例如S1…S7、S8、S9等），如何为每个信号自动生成Y轴

现在我的程序就像我知道csv文件中的信号数量一样构建。我已经尝试创建一个for来修改关键字yaxis，但这是不可能的

我的csv文件：

DateTime;S1;S2;S3;S4;S5;S6
2020-07-17 09:57:27.119916;725.9926027110598;730.5730869210306;946.2937510737263;542.341137182406;758.5531610786929;512.2027881299339
2020-07-17 09:57:28.119916;761.1846087077208;984.1009029835216;974.8724733720549;576.8019892357476;751.6553704523698;855.5439493088621
2020-07-17 09:57:29.119916;618.7837289058051;823.9970681226491;594.2841714340789;873.3093170922189;770.0875733375253;681.1715820388949
2020-07-17 09:57:30.119916;515.9456035777555;533.017970929369;639.3409213385498;542.4405737836958;514.4985515824058;650.5229638670448
2020-07-17 09:57:31.119916;589.1350057317254;605.703259361724;602.3181712775759;860.9749699475683;801.7960812507487;562.400896160191
2020-07-17 09:57:32.119916;626.2528314431347;615.7078057434281;643.2023497200336;709.6997180536518;741.365852401098;712.4384053449293
2020-07-17 09:57:33.119916;553.9768845577024;961.7714859567449;519.8207498752649;551.8006708566627;511.7426656331682;849.3428394570542
2020-07-17 09:57:34.119916;994.8208541190293;700.59423301376;569.1853469890981;997.5842090634065;621.2070112896865;848.5079857917269
2020-07-17 09:57:35.119916;502.2301607876932;760.8787524302393;671.2907579865052;669.0718770518221;901.3788876259023;926.077760311429
2020-07-17 09:57:36.119916;578.3978109170034;811.407262562966;822.6244615030105;570.0016494663124;935.0853062150045;689.8800124555897

我的代码：

df = pd.read_csv(file_name, delimiter = ';')
        
        fig = go.Figure()

        for i in range(1, len(df.columns)):
            fig.add_trace(go.Scatter(x=df.iloc[:,0], y=df.iloc[:,i], name=df.columns[i], yaxis='y'+str(i)))
        
        fig.update_layout(
            xaxis=dict(
                domain=[0.3, 0.7]
            ),
            yaxis=dict(
                title="S1",
                titlefont=dict(
                    color="#1f77b4"
                ),
                tickfont=dict(
                    color="#1f77b4"
                ),
                side="left",
                position=0.10        
            ),
            yaxis2=dict(
                title="S2",
                titlefont=dict(
                    color="#ff7f0e"
                ),
                tickfont=dict(
                    color="#ff7f0e"
                ),
                anchor="free",
                overlaying="y",
                side="left",
                position=0.20
            ),
            yaxis3=dict(
                title="S3",
                titlefont=dict(
                    color="#d62728"
                ),
                tickfont=dict(
                    color="#d62728"
                ),
                anchor="x",
                overlaying="y",
                side="left",
                position=0.30
            ),
            yaxis4=dict(
                title="S4",
                titlefont=dict(
                    color="#6427bd"
                ),
                tickfont=dict(
                    color="#6427bd"
                ),
                anchor="free",
                overlaying="y",
                side="right",
                position=0.70
            ),
            yaxis5=dict(
                title="S5",
                titlefont=dict(
                    color="#3758bd"
                ),
                tickfont=dict(
                    color="#3758bd"
                ),
                anchor="free",
                overlaying="y",
                side="right",
                position=0.80
            ),
            yaxis6=dict(
                title="S6",
                titlefont=dict(
                    color="#2690bd"
                ),
                tickfont=dict(
                    color="#2690bd"
                ),
                anchor="free",
                overlaying="y",
                side="right",
                position=0.90
            )
        )
        
        fig.update_layout(
            title_text="Stacked y-axes",
            yaxis={'title':'Values [unit]'}, 
            xaxis={'title':'DateTime'},
            height=600, 
            width=1000
        )
        
        plot(fig)

这是我现在的结果： https://drive.google.com/file/d/1QPLG0gT2SvWd4_0-piGQsdRy_eTACFn1/view?usp=sharing

Answer 1

您可以在for循环中构建所有yaxis*参数。在下面的示例中，我创建了一个名为axes的dict，添加每个yaxis*子ct，然后调用fig.update_layouts(**axes)

df = pd.read_csv('filename.csv', delimiter = ';')
        
fig = go.Figure()

colors=["#1f77b4","#ff7f0e","#d62728","#6427bd","#3758bd","#2690bd"]

axes = {
    'xaxis': dict(domain=[0.3, 0.7])
}

for i in range(1, len(df.columns)):
    fig.add_trace(go.Scatter(x=df.iloc[:,0], y=df.iloc[:,i], name=df.columns[i], yaxis='y'+str(i)))
    s = 'yaxis'
    if i > 1:
        s = s + str(i)
    axes[s] = dict(
        title=f"S{i}",
        titlefont=dict(color=colors[i-1]),
        tickfont=dict(color=colors[i-1])
    )
    if i > 1:
        axes[s]['anchor'] = 'free'
        axes[s]['overlaying'] = 'y'
    if i > len(df.columns) / 2:
        axes[s]['side'] = 'right'
        axes[s]['position'] = 0.3 + 0.1*i
    else:
        axes[s]['side'] = 'left'
        axes[s]['position'] = 0.1*i

fig.update_layout(**axes)
fig.update_layout(
    title_text="Stacked y-axes",
    yaxis={'title':'Values [unit]'}, 
    xaxis={'title':'DateTime'},
    height=600, 
    width=1000
)