数据：
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我正在使用Kaggle的Titanic数据集。我试图找出每个Pclass男女中存活的百分比。
分组示例：
train_df.groupby(['Pclass','Sex','Survived']).apply(lambda x: len(x)).unstack(2).plot(kind='bar')
这向我展示了每个班级中有多少男人和女人幸存下来，有多少人没有幸存下来，但从视觉上看，每个班级中有多少男人和女人得以幸存。
所需结果：
train_df.groupby(['Pclass','Sex','Survived']).apply(lambda x: len(x)).unstack(2)[1]/(train_df.groupby(['Pclass','Sex','Survived']).apply(lambda x: len(x)).unstack(2)[1]+train_df.groupby(['Pclass','Sex','Survived']).apply(lambda x: len(x)).unstack(2)[0])
这看起来好像获得了预期的结果，但是我想知道是否还有更多的Python方式可以做到这一点？就像normalize = True选项一样。
最终目标：
每个Pclass中每种性别幸存的比率的条形图
答案 0 :(得分：2)
您在len和unstack的轨道上。但是，您可以使用count来简化此操作：
v = df.groupby(['Pclass','Sex','Survived']).Sex.count().unstack(2)
现在，找到平均值。
v[1] / v.sum(1)
Pclass Sex
1 female 1.000000
male 0.166667
2 female 0.750000
male 0.500000
3 female 0.647059
male 0.066667
dtype: float64
用(v[1] / v.sum(1)).plot(kind='bar')作图。
答案 1 :(得分：2)
groupby df.groupby(['Pclass', 'Sex']).Survived.mean().unstack()
Sex female male
Pclass
1 1.000000 0.166667
2 0.750000 0.500000
3 0.647059 0.066667
pivot_table df.pivot_table('Survived', 'Pclass', 'Sex')
Sex female male
Pclass
1 1.000000 0.166667
2 0.750000 0.500000
3 0.647059 0.066667
答案 2 :(得分：2)
您可以将Groupby sum和size用作：
g = df.groupby(['Pclass','Sex'])['Survived']
g.sum()/g.size()
Pclass Sex
1 female 1.000000
male 0.166667
2 female 0.750000
male 0.500000
3 female 0.647059
male 0.066667
Name: Survived, dtype: float64
(g.sum()/g.size()).unstack()
Sex female male
Pclass
1 1.000000 0.166667
2 0.750000 0.500000
3 0.647059 0.066667