我想在一个海洋直方图(分布图)上创建五个子图(一个数据框的特定列中的每个类别一个)。
我的数据集是:
prog score
cool 1.9
cool 3.7
yay 4.5
yay 2.6
neat 1.4
neat 7
neat 6
wow 4.1
wow 1.7
wow 1.4
hooray 6.6
hooray 5.6
hooray 4.9
yikes 1.2
yikes 3.9
yikes 6.9
我不希望绘制所有的'prog',只列出一个列表:
prog_list = ['cool', 'yay', 'neat', 'yikes', 'wow']
scores = df['score']
f, axes = plt.subplots(3, 2, figsize=(15, 15))
# Delete last chart since there are only 5 subplots I need
f.delaxes(ax = axes[2,1])
for i, axes in enumerate(f.axes):
scores = df.loc[(df['prog'] == prog_list[i])]['score']
axes = sns.distplot(scores, norm_hist=True, color='b')
sigma = round(scores.std(), 3)
mu = round(scores.mean(), 2)
axes.set_xlim(1,7)
axes.set_xticks(range(2,8))
axes.set_xlabel('Score - Mean: {} (σ {})'.format(mu, sigma))
axes.set_ylabel('Density')
但是,当我这样做时,它只是将每个子集绘制到同一图上(这很酷,但绝对不是我想要的)。
答案 0 :(得分:2)
尝试一下:
# your code use axes and redefine it after every iteration
# I think this would be better
for prog, ax in zip(prog_list, axes.flatten()[:5]):
scores = df.loc[(df['prog'] == prog)]['score']
# note how I put 'ax' here
sns.distplot(scores, norm_hist=True, ax=ax, color='b')
# change all the axes into ax
sigma = round(scores.std(), 3)
mu = round(scores.mean(), 2)
ax.set_xlim(1,7)
ax.set_xticks(range(2,8))
ax.set_xlabel('Score - Mean: {} (σ {})'.format(mu, sigma))
ax.set_ylabel('Density')
plt.show()
输出: