python之如何使用 factorplot 用分类值注释条形图或绘制 4 个变量

我有一个要绘制的数据框。我想到了 2 个选项(检查图像)。

对于选项 1，我需要注释一个分类值(“Elec”)。

对于选项 2，我仍然需要使用“factorplot”，但我不知道如何修复我得到的错误。

#CODE FOR THE DATAFRAME 
raw_data = {'Max_Acc': [90.71, 87.98, 92.62, 78.93, 73.69, 73.66, 72.29, 
                        92.62, 94.17, 92.62, 83.81, 79.76, 74.40, 72.38], 
            'Stage': ['AWA', 'Rem', 'S1', 'S2', 'SWS', 'SX', 'ALL', 
                      'AWA', 'Rem', 'S1', 'S2', 'SWS', 'SX', 'ALL'], 
             'Elec': ['Fp1', 'Fp2', 'C4', 'Cz', 'Pz', 'P4', 'T3', 
                      'C4', 'T3', 'Fp1', 'P4', 'Fp2', 'Fz', 'Fz'], 
            'Clf': ['RF', 'RF', 'RF', 'RF', 'RF', 'RF', 'RF', 
                    'XG', 'XG', 'XG', 'XG', 'XG', 'XG', 'XG']} 
 
df_m=pd.DataFrame(raw_data, columns = ['Max_Acc', 'Stage', 'Elec', 'Clf']) 
 
df_m 
 
 
#CODE FOR THE PLOT (OPTION 1) 
sns.set(style="white")  
g = sns.factorplot(x="Stage", y="Elec", hue='Clf', data=df, size=2, aspect=3, kind="bar", 
               legend=False)  
 
 
ax=g.ax  
def annotateBars(row, ax=ax):  
    for p in ax.patches: 
        ax.annotate("%.2f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()), 
         ha='center', va='center', fontsize=11, color='gray', rotation=90, xytext=(0, 20), 
         textcoords='offset points')   
 
 
plot = df_m.apply(annotateBars, ax=ax, axis=1) 
 
 
 
#CODE FOR THE PLOT (OPTION 2) 
g = sns.factorplot(x="Clf", y="Max_Acc", hue='Elec', col='Stage', data=df, size=2, aspect=3, kind="bar", 
               legend=False) 

选项 1(用分类值注释)

选项 2(绘制 4 个变量)

请您参考如下方法:

我没有使用“factorplot”。 我刚刚插入了第二个 x 轴。

#To use seaborn palette 
palette = sns.color_palette("Set1", 8) 
sns.set(style="white") 
 
uelec, uind = np.unique(df["Elec"], return_inverse=1) 
cmap = plt.cm.get_cmap("Set1") 
 
colors= [ palette[i] for i in uind] 
fig, ax=plt.subplots(figsize=(15, 5))  
l = len(df) 
pos = np.arange(0,l) % (l//2) + (np.arange(0,l)//(l//2)-1)*0.4 
 
ax.bar(pos, df["Max_Acc"], width=0.4, align="edge", ec="k", color=colors) 
 
handles=[plt.Rectangle((0,0),1,1, color=palette[i], ec="k") for i in range(len(uelec))] 
 
legend=ax.legend(bbox_to_anchor=(0., 1.15, 1., .102), handles=handles, labels=list(uelec), 
       prop ={'size':10}, loc=9, ncol=8, title=r'Best algorithm using Max_Acc after undersampling' ) 
 
legend.get_frame().set_linewidth(0.0)  
plt.setp(legend.get_title(),fontsize='24') 
 
ax.set_xticks(range(l//2)) 
ax.set_xticklabels(df["Stage"][:l//2]) 
ax.set_ylim(0, 110) 
ax.get_yaxis().set_visible(False) 
ax.spines['top'].set_visible(False)  
 
#Double x-axis 
ax.set_xticks(pos+0.2, minor=True) 
clf=df['Clf'].tolist()    
ax.set_xticklabels(clf, minor=True) 
plt.setp(ax.get_xticklabels(), rotation=0) 
ax.tick_params(axis='x', which='major', pad=25, size=0) 
 
ax=ax  
def annotateBars(row, ax=ax):  
    for p in ax.patches: 
        ax.annotate("%.2f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()), 
                 ha='center', va='center', fontsize=11, color='gray', rotation=90, xytext=(0, 20), 
                 textcoords='offset points')   
 
plot = df.apply(annotateBars, ax=ax, axis=1)