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交叉分析通常用于两个或两个以上分组变量之间的关系，以交叉表形式进行变量间关系的对比分析。我们将两个具有一定联系的变量设置为行变量和列变量，把统计数据制作成二维交叉表格(数据透视表)。通常使用的函数是pivot_table()。
pivot_table(values, index, columns, aggfunc, fill_value)
参数说明如下：
参数描述
values数据透视表中的值
index数据透视表中的行
columns数据透视表中的列
aggfunc统计函数
fill_valueNA值的统一替换
可以对比excel中的数据透视表
我们用最熟悉的泰坦尼克号的数据来举例。我现在想知道年龄和舱室等级对存活率有什么影响。
#对年龄进行分组 bins = np.arange(0, 90, 10) age_groups = pd.cut(data['Age'], bins)
data.pivot_table(values=['Survived'], index=['Pclass'], columns=age_groups,
aggfunc=[np.mean])
这张表的含义是一等舱中0到10岁的人的存活率为0.755，三等舱中30-40岁的人的存活率为0.253，其他的数据是一样的解读方法。很容易看出几乎每个年龄段的存活率都是一等舱的最高。
下面进行可视化：
for i in [1, 2, 3]: plt.figure(figsize=(8, 8)) new_df.loc[i].plot(kind='bar',
title='Pclass'+str(i)+' survival rate') plt.xlabel('年龄段') plt.ylabel('生存率')
交叉分析中的交叉维度最多两个维度即可，如果分的越多分的越细，就越找不到重点了，就越难发现问题和规律。
