向条形图子图添加统计显着性注释

Question

我一直在试图弄清楚如何将统计显着性条添加到特定的子图中。大多数相关问题都特定于单个图，但没有显示如何任意向子图添加统计注释。有人可以告诉我如何将第 4 个子图中显示的类似统计注释添加到其他每个子图中吗？

这是我得到的代码和输出：

zero.columns= ['Number of Research Years', 'Total Publications', 'Publications During Residency',
               'Publications Before & After Residency', 'H Index']

fig, axes = plt.subplots(2, 2, sharex= False, sharey= True, figsize=(8,8))

ax1= sns.barplot(ax=axes[0,0], x= zero['Number of Research Years'], y= zero['Total Publications'])
ax2= sns.barplot(ax=axes[0,1], x= zero['Number of Research Years'], y= zero['Publications During Residency'])
ax3= sns.barplot(ax=axes[1,0], x= zero['Number of Research Years'], y= zero['Publications Before & After Residency'])
ax4= sns.barplot(ax=axes[1,1], x= zero['Number of Research Years'], y= zero['H Index'])

x1, x2 = 1, 2   
y, h, col = 100, 5, 'k'
plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
plt.text((x1+x2)*.5, y+h, "*", ha='center', va='bottom', color=col)

x1, x2 = 0, 2  
y, h, col = 120, 5, 'k'
plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
plt.text((x1+x2)*.5, y+h, "*", ha='center', va='bottom', color=col)

ax1.text(0.05, 0.95, "A", fontweight="bold", transform=ax1.transAxes)
ax2.text(0.05, 0.95, "B", fontweight="bold", transform=ax2.transAxes)
ax3.text(0.05, 0.95, "C", fontweight="bold", transform=ax3.transAxes)
ax4.text(0.05, 0.95, "D", fontweight="bold", transform=ax4.transAxes)


sns.despine()
plt.show()

The subplots that I have created so far

Answer 1

您需要编写一个以Axes 作为参数的函数，并使用the OO-version of the functions 在该Axes 对象上绘图：

def annot_stat(star, x1, x2, y, h, col='k', ax=None):
    ax = plt.gca() if ax is None else ax
    ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
    ax.text((x1+x2)*.5, y+h, star, ha='center', va='bottom', color=col)

示例：

def annot_stat(star, x1, x2, y, h, col='k', ax=None):
    ax = plt.gca() if ax is None else ax
    ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
    ax.text((x1+x2)*.5, y+h, star, ha='center', va='bottom', color=col)
    
zero = pd.DataFrame({'Number of Research Years': [0,1,2], 
                     'Total Publications': 100*np.random.random(size=3),
                     'Publications During Residency': 100*np.random.random(size=3),
                     'Publications Before & After Residency': 100*np.random.random(size=3), 
                     'H Index': 10*np.random.random(size=3)})

fig, axes = plt.subplots(2, 2, sharex= False, sharey= True, figsize=(8,8))

ax1= sns.barplot(ax=axes[0,0], x= zero['Number of Research Years'], y= zero['Total Publications'])
ax2= sns.barplot(ax=axes[0,1], x= zero['Number of Research Years'], y= zero['Publications During Residency'])
ax3= sns.barplot(ax=axes[1,0], x= zero['Number of Research Years'], y= zero['Publications Before & After Residency'])
ax4= sns.barplot(ax=axes[1,1], x= zero['Number of Research Years'], y= zero['H Index'])

ax1.text(0.05, 0.95, "A", fontweight="bold", transform=ax1.transAxes)
ax2.text(0.05, 0.95, "B", fontweight="bold", transform=ax2.transAxes)
ax3.text(0.05, 0.95, "C", fontweight="bold", transform=ax3.transAxes)
ax4.text(0.05, 0.95, "D", fontweight="bold", transform=ax4.transAxes)

annot_stat('*', 1, 2, 100, 5, ax=ax1)
annot_stat('***', 0, 2, 120, 5, ax=ax1)

annot_stat('*', 1, 2, 100, 5, ax=ax2)
annot_stat('***', 0, 2, 120, 5, ax=ax2)

annot_stat('*', 1, 2, 100, 5, ax=ax3)
annot_stat('***', 0, 2, 120, 5, ax=ax3)

annot_stat('***', 1, 2, 10, 5, ax=ax4)
annot_stat('*', 0, 2, 20, 5, ax=ax4)


sns.despine()
plt.show()