seaborn 条形图中有两个类别的移位误差线？

Question

我正在尝试 plot 在 seaborn 条 plot 上使用catplot制作一些错误条，如下所示。 来自.to_clipboard(sep=',', index=True) DF output。

熔化DF：

,Parameter,Output,Sobol index,Value
0,$μ_{max}$,P,Total-effect,0.956747485
1,$μ_{max}$,D,Total-effect,-3.08778755e-08
2,$μ_{max}$,I,Total-effect,0.18009523
3,$μ_{max}$,N,Total-effect,0.234568344
4,$μ_{max}$,Mean,Total-effect,0.3428527570305311
5,$m$,P,Total-effect,0.159805431
6,$m$,D,Total-effect,1.13115268
7,$m$,I,Total-effect,0.256635185
8,$m$,N,Total-effect,0.00556940644
9,$m$,Mean,Total-effect,0.38829067561
10,$\alpha_D$,P,Total-effect,0.00115455519
11,$\alpha_D$,D,Total-effect,-0.000953479004
12,$\alpha_D$,I,Total-effect,0.157263821
13,$\alpha_D$,N,Total-effect,0.197434829
14,$\alpha_D$,Mean,Total-effect,0.08872493154649999
15,$N_{max}$,P,Total-effect,0.112155751
16,$N_{max}$,D,Total-effect,0.795253131
17,$N_{max}$,I,Total-effect,0.380872014
18,$N_{max}$,N,Total-effect,0.384059029
19,$N_{max}$,Mean,Total-effect,0.41808498124999993
20,$\gamma_{cell}$,P,Total-effect,0.237220261
21,$\gamma_{cell}$,D,Total-effect,-0.00380320853
22,$\gamma_{cell}$,I,Total-effect,0.00371553068
23,$\gamma_{cell}$,N,Total-effect,0.0010752371
24,$\gamma_{cell}$,Mean,Total-effect,0.059551955062499995
25,$K_d$,P,Total-effect,0.062129271
26,$K_d$,D,Total-effect,-1.46430838e-12
27,$K_d$,I,Total-effect,0.94771372
28,$K_d$,N,Total-effect,0.985790701
29,$K_d$,Mean,Total-effect,0.4989084229996339
30,$μ_{max}$,P,First-order,0.655145193
31,$μ_{max}$,D,First-order,-6.84274992e-20
32,$μ_{max}$,I,First-order,0.00537009868
33,$μ_{max}$,N,First-order,0.000186118183
34,$μ_{max}$,Mean,First-order,0.16517535246575
35,$m$,P,First-order,0.000688037316
36,$m$,D,First-order,0.379773978
37,$m$,I,First-order,0.0065270132
38,$m$,N,First-order,0.00471697043
39,$m$,Mean,First-order,0.0979264997365
40,$\alpha_D$,P,First-order,5.88800317e-07
41,$\alpha_D$,D,First-order,3.59595179e-21
42,$\alpha_D$,I,First-order,0.00405428355
43,$\alpha_D$,N,First-order,4.23811678e-05
44,$\alpha_D$,Mean,First-order,0.00102431337952925
45,$N_{max}$,P,First-order,0.00369586495
46,$N_{max}$,D,First-order,1.71266774e-09
47,$N_{max}$,I,First-order,0.0150083874
48,$N_{max}$,N,First-order,0.00143697969
49,$N_{max}$,Mean,First-order,0.005035308438166935
50,$\gamma_{cell}$,P,First-order,0.0116538163
51,$\gamma_{cell}$,D,First-order,-8.51017704e-24
52,$\gamma_{cell}$,I,First-order,0.00180446631
53,$\gamma_{cell}$,N,First-order,-1.1891221e-07
54,$\gamma_{cell}$,Mean,First-order,0.0033645409244475
55,$K_d$,P,First-order,0.000593392401
56,$K_d$,D,First-order,1.24032496e-17
57,$K_d$,I,First-order,0.314711173
58,$K_d$,N,First-order,0.393636611
59,$K_d$,Mean,First-order,0.17723529410025002

错误的 DF：

,Parameter,Output,Error,value
0,$μ_{max}$,P,Total Error,0.00532374344
1,$μ_{max}$,D,Total Error,3.27057237e-08
2,$μ_{max}$,I,Total Error,0.00589710262
3,$μ_{max}$,N,Total Error,0.0114684117
4,$μ_{max}$,Mean,Total Error,0.005672322616430925
5,$m$,P,Total Error,0.00043759689
6,$m$,D,Total Error,0.20494168
7,$m$,I,Total Error,0.00562181392
8,$m$,N,Total Error,0.00280516814
9,$m$,Mean,Total Error,0.0534515647375
10,$\alpha_D$,P,Total Error,0.0005054549
11,$\alpha_D$,D,Total Error,0.00103547316
12,$\alpha_D$,I,Total Error,0.00461107762
13,$\alpha_D$,N,Total Error,0.0048878586
14,$\alpha_D$,Mean,Total Error,0.00275996607
15,$N_{max}$,P,Total Error,0.00278612157
16,$N_{max}$,D,Total Error,0.228255264
17,$N_{max}$,I,Total Error,0.0122461237
18,$N_{max}$,N,Total Error,0.00975799636
19,$N_{max}$,Mean,Total Error,0.0632613764075
20,$\gamma_{cell}$,P,Total Error,0.00183489881
21,$\gamma_{cell}$,D,Total Error,0.00380320865
22,$\gamma_{cell}$,I,Total Error,0.000257448243
23,$\gamma_{cell}$,N,Total Error,0.0014860645
24,$\gamma_{cell}$,Mean,Total Error,0.00184540505075
25,$K_d$,P,Total Error,0.00198581744
26,$K_d$,D,Total Error,4.60436562e-12
27,$K_d$,I,Total Error,0.0061495373
28,$K_d$,N,Total Error,0.0104738223
29,$K_d$,Mean,Total Error,0.004652294261151092
30,$μ_{max}$,P,First Error,0.00251411484
31,$μ_{max}$,D,First Error,5.81033819e-20
32,$μ_{max}$,I,First Error,0.000157513959
33,$μ_{max}$,N,First Error,0.000242607282
34,$μ_{max}$,Mean,First Error,0.00072855902025
35,$m$,P,First Error,0.000102871751
36,$m$,D,First Error,0.114462613
37,$m$,I,First Error,0.000544114122
38,$m$,N,First Error,0.00208775169
39,$m$,Mean,First Error,0.029299337640750003
40,$\alpha_D$,P,First Error,2.85055333e-06
41,$\alpha_D$,D,First Error,2.97531464e-21
42,$\alpha_D$,I,First Error,0.000382494131
43,$\alpha_D$,N,First Error,6.18549533e-05
44,$\alpha_D$,Mean,First Error,0.00011179990940750001
45,$N_{max}$,P,First Error,0.000359843541
46,$N_{max}$,D,First Error,1.71266774e-09
47,$N_{max}$,I,First Error,0.00147221438
48,$N_{max}$,N,First Error,0.000931579111
49,$N_{max}$,Mean,First Error,0.000690909686166935
50,$\gamma_{cell}$,P,First Error,0.000245701873
51,$\gamma_{cell}$,D,First Error,9.57051247e-24
52,$\gamma_{cell}$,I,First Error,0.000318847918
53,$\gamma_{cell}$,N,First Error,1.1718385e-07
54,$\gamma_{cell}$,Mean,First Error,0.0001411667437125
55,$K_d$,P,First Error,0.000284942556
56,$K_d$,D,First Error,2.3820604e-18
57,$K_d$,I,First Error,0.00320241658
58,$K_d$,N,First Error,0.00880561072
59,$K_d$,Mean,First Error,0.0030732424640000006

代码：

    # Get dataframes
    df = analysis.sensitivity_analysis_result.get_dataframe(analysis.scenario)
    err_df = df.melt(id_vars=["Parameter", "Output"], value_vars=["Total Error", "First Error"], var_name="Error")
    df = df.melt(id_vars=["Parameter", "Output"], value_vars=["Total-effect", "First-order"], var_name="Sobol index", value_name="Value")

    # Plot
    grid = sns.catplot(data=df, x="Parameter", y="Value", col="Output", col_wrap=2, hue="Sobol index", kind="bar", aspect=1.8, legend_out=False)

    # Add error lines and values
    for ax, var in zip(grid.axes.ravel(), ["P", "D", "I", "N"]):
        # Error bars
        ax.errorbar(x=df[df["Output"] == var]["Parameter"], y=df[df["Output"] == var]["Value"],
                    yerr=err_df[err_df["Output"] == var]["value"], ecolor='black', linewidth=0, capsize=2)

        # Value labels   
        for c in ax.containers:
            if type(c) == matplotlib.container.BarContainer:
                ax.bar_label(c, labels=[f'{v.get_height():.2f}' if v.get_height() >= 0.01 else "<0.01" for v in c], label_type='edge')

    grid.tight_layout()

我面临的问题是错误栏上限大小未以实际栏为中心。 这是我得到的 output。 我怎样才能解决这个问题？ 我传递给error bar的x参数不是浮点数，而是数据框中的一列，因此对我来说如何移动它并不明显。

Answer 1

你 plot 分类值（x 值是字符串），条形组（的中心）的 x 位置和你绘制它们的方式的误差条是range(n) ，其中 n 是参数的数量。 这些 x 位置也可以通过ax.xaxis.get_majorticklocs()检索。

要将误差条放在条的中间，您需要考虑每个条相对于条组中心的偏移量。 此偏移量取决于条形宽度（默认值为0.8 ）和每组条形的数量。 对于通用解决方案，您将区分每组的奇数条和偶数条。

下面显示了每组 2 个色调或条的情况（ df['Sobol index'].nunique() == 2 ）：

# Plot
width = 0.8
grid = sns.catplot(data=df, x="Parameter", y="Value", col="Output", col_wrap=2, hue="Sobol index", kind="bar", aspect=1.8, legend_out=False, width=width)

# Add error lines and values
for ax, var in zip(grid.axes.ravel(), ["P", "D", "I", "N"]):
    # Error bars
    ticklocs = ax.xaxis.get_majorticklocs()
    offset = 0.5 * width / 2  # 2 = number of hues or bars per group
    ax.errorbar(x=np.append(ticklocs - offset, ticklocs + offset), y=df[df["Output"] == var]["Value"],
                yerr=err_df[err_df["Output"] == var]["value"], ecolor='black', linewidth=0, capsize=2, elinewidth=1)

    # Value labels   
    for c in ax.containers:
        if type(c) == matplotlib.container.BarContainer:
            ax.bar_label(c, labels=[f'{v.get_height():.2f}' if v.get_height() >= 0.01 else "<0.01" for v in c], label_type='edge')

grid.tight_layout()