在 Python 中具有 4 个面板（2 x 2）的 Seaborn 直方图

Question

我正在尝试使用sklearn.datasets.load_iris 和seaborn 重新创建此图像。我真的很喜欢做fig, ax = plt.subplots() 然后使用seaborn 的ax=ax 属性的想法。我不知道如何重新创建这个情节：

我检查了 stackoverflow 并发现了这一点，但它覆盖了它们 How To Plot Multiple Histograms On Same Plot With Seaborn

这是我的代码和情节：

# Iris Dataset
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

%matplotlib inline 

DF_data = pd.DataFrame(load_iris().data, 
                       columns = load_iris().feature_names, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])])

Se_targets = pd.Series(load_iris().target, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])], 
                       name = "Targets")

#Visualizing Iris Data
D_targets = {0: 'Iris-Setosa',
            1: 'Iris-Versicolor',
            2: 'Iris-Virgnica'}

D_features = {0: 'sepal length [cm]',
              1: 'sepal width [cm]',
              2: 'petal length [cm]',
              3: 'petal width [cm]'}

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 6))

idx_feature = 0

#Plot on 2 x 2 ax object

for i in range(ax.shape[0]):
    for j in range(0, ax.shape[1]):
        for idx_target, label_target  in list(D_targets.items()):
            sns.distplot(DF_data.as_matrix()[Se_targets==idx_target, idx_feature],
                         label=D_features[idx_feature],
                         kde=False,
                         bins=10,
                         ax=ax[i][j])        
        idx_feature += 1 

plt.legend(loc='upper right', fancybox=True, fontsize=8)

plt.tight_layout()
plt.show()

我的情节看起来很糟糕：

更新：

响应@Cel 的回答，我已经完成了这个情节，但我无法修复标签并使情节周围的线条变暗。

Answer 1

这里的问题是您使用布尔系列而不是布尔 numpy 数组来索引 numpy 数组。

sns.distplot(DF_data.as_matrix()[Se_targets==idx_target, idx_feature],
             label=D_targets[idx_target],
             kde=False,
             bins=10,
             ax=ax[i][j])

我同意这是非常不直观的。事实上，numpy 已经告诉你，这将在未来改变：

DF_data.as_matrix()[Se_targets==idx_target, 2]

/Users/ch/miniconda/envs/sci34/lib/python3.4/site-packages/IPython/kernel/ma​​in.py:1：FutureWarning：未来，布尔数组- likes 将作为布尔数组索引处理

现在，这应该适合你：

sns.distplot(DF_data.as_matrix()[Se_targets.as_matrix()==idx_target, idx_feature],
             label=D_features[idx_feature],
             kde=False,
             bins=10,
             ax=ax[i][j])

这是完整的代码：

# Iris Dataset
import pandas as pd
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
sns.set_style('whitegrid')

%matplotlib inline 

DF_data = pd.DataFrame(load_iris().data, 
                       columns = load_iris().feature_names, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])])

Se_targets = pd.Series(load_iris().target, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])], 
                       name = "Targets")

#Visualizing Iris Data
D_targets = {0: 'Iris-Setosa',
            1: 'Iris-Versicolor',
            2: 'Iris-Virgnica'}

D_features = {0: 'sepal length [cm]',
              1: 'sepal width [cm]',
              2: 'petal length [cm]',
              3: 'petal width [cm]'}

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 6))

idx_feature = 0

#Plot on 2 x 2 ax object

for i in range(ax.shape[0]):
    for j in range(0, ax.shape[1]):
        for idx_target, label_target  in list(D_targets.items()):
            plot = sns.distplot(DF_data.as_matrix()[Se_targets.as_matrix()==idx_target, idx_feature],
                         label=D_features[idx_feature],
                         kde=False,
                         bins=10,
                         ax=ax[i][j])
            plot.set_xlabel(D_features[idx_feature])
        idx_feature += 1 

plt.legend(loc='upper right', fancybox=True, fontsize=8)

plt.tight_layout()

Answer 2

或者你可以这样做

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

iris = sns.load_dataset("iris")
iris_long = pd.melt(iris, "species", var_name="measurement")
g = sns.FacetGrid(iris_long, hue="species", col="measurement", col_wrap=2, sharex=False)
g.map(plt.hist, "value", alpha=.4)