[英]Plot seaborn catplots for multiple columns
我有一個包含 93 個特征和 9 個類標簽的數據框。 我想用各自的類標簽繪制每個特征的值,但是,我想生成一個包含 93 個圖的子圖,每個圖代表數據集中的一個特征。 我可以制作一個情節,它看起來像這樣:
sns.catplot(x="feat_1", y="target", data=train)
現在我基本上想重復同樣的事情,但是以刻面網格的形式重復 93 次。 我嘗試創建一個包含 5 列和 19 行的子圖,然后循環遍歷軸但失敗了......感謝您的幫助,我的數據看起來像這樣(93 個特征列和一個目標列):
feat_1 feat_2 feat_3 feat_4 feat_5 feat_6 feat_7 feat_8 feat_9 feat_10 ... feat_85 feat_86 feat_87 feat_88 feat_89 feat_90 feat_91 feat_92 feat_93 target
id
32518 0 0 0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 Class_6
31734 0 1 7 5 0 0 0 0 0 1 ... 0 0 0 1 2 0 1 4 0 Class_6
57027 0 0 0 0 0 0 0 2 0 0 ... 0 0 0 0 0 0 1 0 0 Class_9
31629 0 1 0 0 0 0 0 1 1 0 ... 0 0 0 1 2 0 0 0 0 Class_6
14216 2 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 0 0 0 0 0 Class_2
17376 0 0 0 0 0 0 0 0 0 0 ... 0 2 0 1 0 0 0 0 0 Class_2
10520 1 0 0 0 0 0 0 0 0 0 ... 0 3 0 0 0 0 0 0 0 Class_2
7665 0 0 0 0 0 0 0 0 0 0 ... 0 2 0 3 0 0 0 0 0 Class_2
26692 0 0 0 0 0 0 0 0 0 0 ... 4 0 0 0 0 0 0 0 0 Class_4
36809 0 0 3 4 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1 0 Class_6
47959 0 1 0 3 0 2 1 0 0 1 ... 6 0 0 0 1 1 0 0 1 Class_7
22649 0 0 0 0 1 0 0 0 0 1 ... 21 0 1 0 0 2 0 0 0 Class_3
34550 0 0 1 2 0 0 1 0 0 0 ... 0 0 1 0 0 1 1 1 1 Class_6
39943 3 0 0 0 0 0 0 0 0 0 ... 0 0 2 0 0 0 0 0 0 Class_6
38900 1 0 6 14 0 0 1 0 0 0 ... 0 0 1 0 0 0 0 0 0 Class_6
26333 0 0 1 0 0 0 1 1 0 0 ... 0 0 1 1 0 0 0 0 0 Class_4
16126 0 0 0 0 0 0 0 0 0 0 ... 0 0 1 10 0 0 0 0 0 Class_2
10490 0 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 3 0 Class_2
58603 0 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 28 0 1 Class_9
52668 0 0 1 2 0 0 0 4 0 0 ... 0 0 0 0 4 0 0 0 0 Class_8
要利用 seaborn 的FacetGrid
(由catplot
),您需要將數據catplot
從“寬”轉換為“長”
# dummy dataframe
N=20
N_features = 10
N_classes = 5
df = pd.DataFrame({f'feat_{i+1}': np.random.random(size=(N,)) for i in range(N_features)})
df['target'] = np.random.choice([f'Class_{i+1}' for i in range(N_classes)], size=(N,))
# transform from wide to long, then plot using the column 'features' to facet
df2 = df.melt(id_vars=['target'], var_name='features')
sns.catplot(data=df2, x='value', y='target', col='features', col_wrap=5, height=3, aspect=0.5)
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