使用熊猫数据透视表创建子图

Question

鉴于下面的数据框，我想创建一个包含两个子图的图形，每个半球一个。 Plts应该显示针对差异的均值。

df = pd.DataFrame({'id':[1,1,1,1,2,2,2,2],
                   'eye':['l','r','l','r','l','r','l','r'],
                   'trial':[1,1,2,2,1,1,2,2],
                   'S':[2,2,3,3,5,5,7,7],
                   'I':[2,2,1,1,4,4,3,3]})

df = df.melt(id_vars=['id','eye','trial'],
             value_vars=['S','I'],
             var_name='Hemisphere',
             value_name='Thickness')

df = df.pivot_table(index=['id','eye','Hemisphere'],
                    columns='trial',
                    values='Thickness')

df['diffs'] = df[1] - df[2]
df['means'] = np.mean([df[1], df[2]], axis=0)

df = df.unstack(level=2)

df.plot('means','diffs',subplots=True,kind='scatter')

Answer 1

groupby与axis=1

axes = df[['diffs', 'means']].groupby(axis=1, level=1).plot.scatter('means', 'diffs')

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遍历groupby

更好地控制

colors = iter('gr')
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 8))
for i, (k, d) in enumerate(df.groupby(axis=1, level=1)):
    d.xs(k, axis=1, level=1).plot.scatter(
        'means', 'diffs', title=k, ax=axes[i], c=next(colors))

fig.tight_layout()