Seaborn barplot-Python中的错误条问题

（我们已经研究过类似的问题，但他们没有回答这个问题 （查询）

我有一个数据帧df1，结构如下

{'token': {0: '180816_031', 1: '180816_031', 2: '180816_031', 3: '180816_031', 4: '180816_031', 5: '180816_031', 6: '180816_031', 7: '180816_031', 8: '180816_031', 9: '180816_031'}, 'variable': {0: 'Unnamed: 0', 1: 'adj_active_polymerase', 2: 'adj_functional_sequencing_pores', 3: 'adj_high_quality_reads', 4: 'adj_single_pores', 5: 'cell_mask_bilayers_sum', 6: 'num_align_high_quality_reads', 7: 'num_total_cells', 8: 'potential_pore', 9: 'short_pass'}, 'value': {0: 21.0, 1: 615850.51515151514, 2: 615850.51515151514, 3: 486008.39393939392, 4: 803784.06060606055, 5: 1665347.5757575757, 6: 468638.03030303027, 7: 2097152.0, 8: 1158527.0, 9: 2067189.2424242424}}

我使用下面的代码重新创建数据，然后显示条形图

    df1 = df.groupby(['token','variable']).agg({'value':['mean','std']})
    df1.reset_index(inplace=True)
    df1.sort_values('value',inplace=True,ascending=False)
    
    fig,ax = plt.subplots()
    fig.set_size_inches(16,8)
    #to get different colors for each of the variable assign the variable to hue
    g = sns.factorplot(x='token',y='value',data=df1, hue='variable',ax=ax,  yerr='std')
   #g.map_dataframe(plt.errorbar, x="followup (months)", y="probability", yerr='sd')
    #Code for to put legend outside the plot
    box = ax.get_position()
    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
    # Put a legend to the right of the current axis
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
    # Adding respective values to the top of each bar
    for p in ax.patches: 
        ax.annotate("%d" % p.get_height(), (p.get_x() + p.get_width() / 2, p.get_height()),
                    ha='center', va='center', fontsize=11, color='black', xytext=(0, 10), 
                    textcoords='offset points',fontweight='bold')
    plt.show()

但我无法在输出中获得所需的错误条

在我的数据帧中只使用mean之前，我已经生成了一个输出，但是我也需要errorbars，所以我考虑添加std&在yerr中使用它（在阅读了这么多之后）

请帮忙

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


d = {'token': {361: '180816_031', 119: '180816_031', 101: '180816_031', 135: '180816_031', 292: '180816_031',
               133: '180816_031', 99: '180816_031', 270: '180816_031', 19: '180816_031', 382: '180816_031',
               414: '180816_031', 267: '180816_031', 218: '180816_031', 398: '180816_031', 287: '180816_031',
               155: '180816_031', 392: '180816_031', 265: '180816_031', 239: '180816_031', 237: '180816_031'},
     'station': {361: 'deneb', 119: 'callisto', 101: 'callisto', 135: 'callisto', 292: 'callisto', 133: 'deneb',
                 99: 'callisto', 270: 'callisto', 19: 'deneb', 382: 'callisto', 414: 'deneb', 267: 'callisto',
                 218: 'deneb', 398: 'callisto', 287: 'deneb', 155: 'deneb', 392: 'deneb', 265: 'callisto',
                 239: 'callisto', 237: 'callisto'},
     'cycle_number': {361: 'cycle09', 119: 'cycle06', 101: 'cycle04', 135: 'cycle01', 292: 'cycle04', 133: 'cycle05',
                      99: 'cycle06', 270: 'cycle07', 19: 'cycle04', 382: 'cycle08', 414: 'cycle04', 267: 'cycle10',
                      218: 'cycle07', 398: 'cycle08', 287: 'cycle09', 155: 'cycle08', 392: 'cycle06', 265: 'cycle02',
                      239: 'cycle09', 237: 'cycle07'},
     'variable': {361: 'adj_high_quality_reads', 119: 'short_pass', 101: 'short_pass', 135: 'cell_mask_bilayers_sum',
                  292: 'adj_active_polymerase', 133: 'cell_mask_bilayers_sum', 99: 'short_pass',
                  270: 'adj_active_polymerase', 19: 'Unnamed: 0', 382: 'adj_high_quality_reads',
                  414: 'num_align_high_quality_reads', 267: 'adj_active_polymerase', 218: 'adj_single_pores',
                  398: 'num_align_high_quality_reads', 287: 'adj_active_polymerase', 155: 'cell_mask_bilayers_sum',
                  392: 'num_align_high_quality_reads', 265: 'adj_active_polymerase', 239: 'adj_single_pores',
                  237: 'adj_single_pores'},
     'value': {361: 99704.0, 119: 2072785.0, 101: 2061059.0, 135: 1682208.0, 292: 675306.0, 133: 1714292.0,
               99: 2072785.0, 270: 687988.0, 19: 19.0, 382: np.nan, 414: 285176.0, 267: 86914.0, 218: 948971.0,
               398: 405196.0, 287: 137926.0, 155: 1830032.0, 392: 480081.0, 265: 951689.0, 239: 681452.0,
               237: 882671.0}}
df = pd.DataFrame(d)


g = sns.barplot('token', 'value', data=df, hue='variable', capsize=0.1)

df5 = pd.DataFrame(df.groupby(['variable'])['value'].mean().reset_index())
i = 0
for p in g.patches:
    height = p.get_height()

    g.text(p.get_x() + p.get_width() / 2.,
           height + 3,
           "%.3f" % df5.at[i, 'value'],
           ha="center")
    i += 1
plt.show()

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请帮助，如果有任何问题，请指出我将更换HI团队，请任何人对此进行调查并告诉我我做错了什么。如果我问Q的方式有错误，请告诉我是否也要显示平均值，如果出现错误，请共享代码Nihal，您为此使用的代码谢谢Nihal，只需一个提示，即使我对值使用了排序，但我的输出并没有按照我共享的预期输出从高到低排序。请检查（在输出中也未排序）df1。排序_值（'value'，inplace=True，升序=False）然后打印代码不起作用。请注意，打印必须通过标记值和变量着色来完成。我认为上面的输出显示了第一个值，因为它不是groupby means，因为我认为groupby根据标记不同