在海上matplotlib中为多个地块创建单个图例

问题描述

我正在使用"iris.csv"数据制作盒子图。我试图通过测量将数据分解成多个数据帧(即花瓣长度、花瓣宽度、花瓣长度、花瓣宽度)，然后在forloop上制作盒子图，从而添加子图。

最后，我想一次为所有的盒子图添加一个通用图例。但是，我不能做这件事。我已经使用几个堆栈溢出问题尝试了几个教程和方法，但我无法修复它。

以下是我的代码：

import seaborn as sns 
from matplotlib import pyplot

iris_data = "iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(iris_data, names=names)


# Reindex the dataset by species so it can be pivoted for each species 
reindexed_dataset = dataset.set_index(dataset.groupby('class').cumcount())
cols_to_pivot = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width']

# empty dataframe 
reshaped_dataset = pd.DataFrame()
for var_name in cols_to_pivot:
    pivoted_dataset = reindexed_dataset.pivot(columns='class', values=var_name).rename_axis(None,axis=1)
    pivoted_dataset['measurement'] = var_name
    reshaped_dataset = reshaped_dataset.append(pivoted_dataset, ignore_index=True)


## Now, lets spit the dataframe into groups by-measurements.
grouped_dfs_02 = []
for group in reshaped_dataset.groupby('measurement') :
    grouped_dfs_02.append(group[1])


## make the box plot of several measured variables, compared between species 

pyplot.figure(figsize=(20, 5), dpi=80)
pyplot.suptitle('Distribution of floral traits in the species of iris')

sp_name=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
setosa = mpatches.Patch(color='red')
versi = mpatches.Patch(color='green')
virgi = mpatches.Patch(color='blue')

my_pal = {"Iris-versicolor": "g", "Iris-setosa": "r", "Iris-virginica":"b"}
plt_index = 0


# for i, df in enumerate(grouped_dfs_02):
for group_name, df in reshaped_dataset.groupby('measurement'):

    axi = pyplot.subplot(1, len(grouped_dfs_02), plt_index + 1)
    sp_name=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
    df_melt = df.melt('measurement', var_name='species', value_name='values')

    sns.boxplot(data=df_melt, x='species', y='values', ax = axi, orient="v", palette=my_pal)
    pyplot.title(group_name)
    plt_index += 1


# Move the legend to an empty part of the plot
pyplot.legend(title='species', labels = sp_name, 
         handles=[setosa, versi, virgi], bbox_to_anchor=(19, 4),
           fancybox=True, shadow=True, ncol=5)


pyplot.show()

情节如下：

如何在主画面外的"Main字幕"旁添加常用图例？

---

解决方案

若要定位图例，请务必将loc参数设置为锚点。(缺省的loc是'best'，这意味着您事先不知道它会在哪里结束)。这些位置从0,0是当前AX的左下角，到1,1：当前AX的左上角。这不包括标题等的填充，因此值可能会超出0, 1范围。"当前AX"是最后一个激活的AX。

请注意，您还可以使用plt.gcf().legend，而不是plt.legend(使用轴)，plt.gcf().legend使用"Figure"。然后，坐标0,0在整个图的左下角("图")和1,1在右上角。缺点是不会为图例创建额外的空间，因此您需要手动设置顶部填充(例如plt.gcf().subplots_adjust(top=0.8))。缺点是您不能再使用plt.tight_layout()，并且更难将图例与轴对齐。

import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import patches as mpatches
import pandas as pd

dataset = sns.load_dataset("iris")

# Reindex the dataset by species so it can be pivoted for each species
reindexed_dataset = dataset.set_index(dataset.groupby('species').cumcount())
cols_to_pivot = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']

# empty dataframe
reshaped_dataset = pd.DataFrame()
for var_name in cols_to_pivot:
    pivoted_dataset = reindexed_dataset.pivot(columns='species', values=var_name).rename_axis(None, axis=1)
    pivoted_dataset['measurement'] = var_name
    reshaped_dataset = reshaped_dataset.append(pivoted_dataset, ignore_index=True)

## Now, lets spit the dataframe into groups by-measurements.
grouped_dfs_02 = []
for group in reshaped_dataset.groupby('measurement'):
    grouped_dfs_02.append(group[1])

## make the box plot of several measured variables, compared between species
plt.figure(figsize=(20, 5), dpi=80)
plt.suptitle('Distribution of floral traits in the species of iris')

sp_name = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
setosa = mpatches.Patch(color='red')
versi = mpatches.Patch(color='green')
virgi = mpatches.Patch(color='blue')

my_pal = {"versicolor": "g", "setosa": "r", "virginica": "b"}
plt_index = 0

# for i, df in enumerate(grouped_dfs_02):
for group_name, df in reshaped_dataset.groupby('measurement'):
    axi = plt.subplot(1, len(grouped_dfs_02), plt_index + 1)
    sp_name = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
    df_melt = df.melt('measurement', var_name='species', value_name='values')

    sns.boxplot(data=df_melt, x='species', y='values', ax=axi, orient="v", palette=my_pal)
    plt.title(group_name)
    plt_index += 1

# Move the legend to an empty part of the plot
plt.legend(title='species', labels=sp_name,
           handles=[setosa, versi, virgi], bbox_to_anchor=(1, 1.23),
           fancybox=True, shadow=True, ncol=5, loc='upper right')
plt.tight_layout()
plt.show()