Python：动态生成原始图，然后并排显示结果？

Question

我正在尝试为我的Pandas DataFrame的数字变量生成多个seaborn内核密度图。我在列表numberCol中拥有所有数字列的名称。目前，我可以为我明确命名的每个变量创建一个kdeplot，如下所示：

import seaborn as sbn
sbn.set_style('whitegrid')
sbn.kdeplot(np.array(df.v2), bw=0.5) # for pandas.core.frame.DataFrame input

是否有更好的方法来遍历numberCol列表，为sbn.kdeplot中的每个变量生成一个numberCol，然后与比类似的东西聪明地并排显示它们：

import matplotlib.pyplot as plt
import seaborn as sns

# Here we create a figure instance, and two subplots
fig = plt.figure(figsize = (20,20)) # width x height
ax1 = fig.add_subplot(3, 3, 1) # row, column, position
ax2 = fig.add_subplot(3, 3, 2)
ax3 = fig.add_subplot(3, 3, 3)

# We use ax parameter to tell seaborn which subplot to use for this plot
sns.heatmap(data=subset1.corr(), ax=ax1, cmap = cmap, square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})
sns.heatmap(data=subset2.corr(), ax=ax2, cmap = cmap, square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})
sns.heatmap(data=subset3.corr(), ax=ax3, cmap = cmap, square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})

Answer 1

如果我理解您的问题，这应该可以解决问题

Ncols = 9
cols = ['col_{:d}'.format(i) for i in range(Ncols)]
df = pd.DataFrame(np.random.random(size=(1000,Ncols)),columns=cols)

fig, axs = plt.subplots(3,3) # adjust the geometry based on your number of columns to plot
for ax,col in zip(axs.flatten(), cols):
    sns.kdeplot(df[col], ax=ax)