调用 matplotlib 子图时图例打印两次

Question

我正在 matplotlib 中编写代码以在子图网格下打印多个直方图，但是，当我最后调用 Fig.legend() 函数时，每个图的图例都会打印两次。任何有关如何解决此问题的指导将不胜感激：）这是我的代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
def get_cmap(n, name='hsv'):
    return plt.cm.get_cmap(name, n)
def isSqrt(n):
   sq_root = int(np.sqrt(n))
   return (sq_root*sq_root) == n
df = pd.read_csv('mpg.csv')
df2 = pd.read_csv('dm_office_sales.csv')
df['miles'] = df2['salary']
numericClassifier = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
newdf = df.select_dtypes(numericClassifier)
columns = newdf.columns.tolist()
n = len(columns)
cmap = get_cmap(n)
if(isSqrt(n)):
    nrows = ncols = int(np.sqrt(n))
else:
    ncols = int(np.sqrt(n))
    for i in range(ncols,50):
        if ncols*i >= n:
            nrows = i
            break
        else:
            pass
fig,ax = plt.subplots(nrows,ncols)
count = 0
print(nrows,ncols)
for i in range(0,nrows,1):
    for j in range(0,ncols,1):
        print('ncols = {}'.format(j),'nrows = {}'.format(i),'count = {}'.format(count))
        if count<=n-1:
            plt_new = sns.histplot(df[columns[count]],ax=ax[i,j],facecolor=cmap(count),kde=True,edgecolor='black',label=df[columns[count]].name)
            patches = plt_new.get_children()
            for patch in patches:
                patch.set_alpha(0.8)
            color = patches[0].get_facecolor()
            ax[i,j].set_xlabel('{}'.format(df[columns[count]].name))
            ax[i,j].xaxis.label.set_fontsize(10)
            ax[i,j].xaxis.label.set_fontname('ariel')
            ax[i,j].set(xlabel=None)
            ax[i,j].tick_params(axis='y', labelsize=8)
            count+=1
        else:
            break
    
for i in range(0,nrows,1):
    for j in range(0,ncols,1):
        if not ax[i,j].has_data():
            fig.delaxes(ax[i,j])
        else:
            pass

plt.suptitle('Histograms').set_fontname('ariel')
plt.tight_layout()
fig.legend(loc='upper right')
plt.show()

这是输出：

Answer 1

sns.histplot似乎创建了两个酒吧容器。首先是虚拟的，然后是真实的。（使用seaborn 0.12.1进行测试；这在其他版本中可能会有所不同。）因此，标签被分配给虚拟和真实的条形容器。解决方法是去除引锭杆容器的标签。

这是改编后的代码。Seaborn 的mpg数据集用于提供易于重现的示例。由于颜色图的第一个和最后一个颜色hls是红色，get_cmap(n + 1)确保n选择不同的颜色。一些多余的代码已被删除。

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

def get_cmap(n, name='hsv'):
    return plt.cm.get_cmap(name, n)

sns.set_style('darkgrid')
df = sns.load_dataset('mpg')
numericClassifier = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
newdf = df.select_dtypes(numericClassifier)
columns = newdf.columns.tolist()
n = len(columns)
cmap = get_cmap(n + 1)
ncols = int(np.sqrt(n))
nrows = int(np.ceil(n / ncols))
fig, ax = plt.subplots(nrows, ncols)
count = 0
print(nrows, ncols)
for i in range(0, nrows):
    for j in range(0, ncols):
        if count < n:
            # print('ncols = {j}; nrows = {i}; count = {count}')
            sns.histplot(df[columns[count]], ax=ax[i, j], facecolor=cmap(count), kde=True, edgecolor='black',
                         label=df[columns[count]].name)
            ax[i, j].containers[0].set_label('')  # seaborn seems to create a dummy bar container, remove its label
            for patch in ax[i, j].get_children():
                patch.set_alpha(0.8)
            ax[i, j].tick_params(axis='y', labelsize=8)
            count += 1
for i in range(0, nrows):
    for j in range(0, ncols):
        if not ax[i, j].has_data():
            fig.delaxes(ax[i, j])

plt.suptitle('Histograms').set_fontname('ariel')
fig.legend(loc='upper right')
plt.tight_layout()
plt.subplots_adjust(right=0.75) # make extra space for the legend
plt.show()

sns.histplot经过进一步调查，当使用color=而不是调用时，似乎没有创建虚拟条容器facecolor=。

代码也可以写得更“Pythonic”一点。这意味着 ao 试图避免重复代码和显式索引。实现这一目标，zip是重要的帮手。除了避免重复之外，代码变得更短并且更容易修改。一旦你习惯了它，它就会变得更容易阅读和推理。

主要部分可能如下所示：

fig, axs = plt.subplots(nrows=nrows, ncols=ncols)
for column, ax, color in zip(columns, axs.flat, cmap(range(n))):
    # using `color=` instead of `facecolor=` seems to avoid the creating of dummy bars
    sns.histplot(df[column], ax=ax, color=color, kde=True, edgecolor='black', label=column)
    for patch in ax.get_children():
        patch.set_alpha(0.8)
    ax.tick_params(axis='y', labelsize=8)
for ax in axs.flat:
    if not ax.has_data():
        fig.delaxes(ax)