具有多个分布的海运距离图/离散图 [英] seaborn distplot / displot with multiple distributions

问题描述

我正在使用海运绘制分布图。我想在同一张图上用不同的颜色绘制多个分布：

下面是我开始绘制分布图的方式：

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],columns= iris['feature_names'] + ['target'])

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
0                5.1               3.5                1.4               0.2     0.0
1                4.9               3.0                1.4               0.2     0.0
2                4.7               3.2                1.3               0.2     0.0
3                4.6               3.1                1.5               0.2     0.0
4                5.0               3.6                1.4               0.2     0.0

sns.distplot(iris[['sepal length (cm)']], hist=False, rug=True);

'target'列包含3个值：0、1、2。

我希望看到一个萼片长度分布图，其中target ==0、target ==1和target ==2共有3个分布图。

推荐答案

重要的是按target为0、1或2的值对数据帧进行排序。

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns

iris = load_iris()
iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                    columns=iris['feature_names'] + ['target'])

# Sort the dataframe by target
target_0 = iris.loc[iris['target'] == 0]
target_1 = iris.loc[iris['target'] == 1]
target_2 = iris.loc[iris['target'] == 2]

sns.distplot(target_0[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_1[['sepal length (cm)']], hist=False, rug=True)
sns.distplot(target_2[['sepal length (cm)']], hist=False, rug=True)

plt.show()

输出如下：

如果您不知道target可能有多少值，请在target列中找到唯一的值，然后对数据帧进行切片并相应地添加到绘图中。

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns

iris = load_iris()
iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                    columns=iris['feature_names'] + ['target'])

unique_vals = iris['target'].unique()  # [0, 1, 2]

# Sort the dataframe by target
# Use a list comprehension to create list of sliced dataframes
targets = [iris.loc[iris['target'] == val] for val in unique_vals]

# Iterate through list and plot the sliced dataframe
for target in targets:
    sns.distplot(target[['sepal length (cm)']], hist=False, rug=True)

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