DataFrame中交叉表使用

1、运用场景：交叉表(cross-tabulation, 简称crosstab)是一种常用的分类汇总表格，用于计算分组频率的特殊透视表,主要价值在于描述了变量间关系的深刻含义。虽然两个（或以上）变量可以是分类的或数量的，但是以都是分类的情形最为常见。

2、Python中函数说明：

pd.crosstab( index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False, )
 Docstring: Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed.
作用：计算两个(或多个)变量（因子）的简单交叉表。默认情况下计算变量（因子）之间的的频率，
如果传递聚合函数有数组或值，将按照设置的内容计算变量之间的关系，具体详见参数说明。

Parameters
----------
index : array-like, Series, or list of arrays/Series
    Values to group by in the rows.()

columns : array-like, Series, or list of arrays/Series
    Values to group by in the columns.
values : array-like, optional
    Array of values to aggregate according to the factors.
    Requires `aggfunc` be specified.
rownames : sequence, default None
    If passed, must match number of row arrays passed.
colnames : sequence, default None
    If passed, must match number of column arrays passed.
aggfunc : function, optional
    If specified, requires `values` be specified as well.
margins : bool, default False
    Add row/column margins (subtotals).
margins_name : str, default 'All'
    Name of the row/column that will contain the totals
    when margins is True.

    .. versionadded:: 0.21.0

dropna : bool, default True
    Do not include columns whose entries are all NaN.
normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
    Normalize by dividing all values by the sum of values.

    - If passed 'all' or `True`, will normalize over all values.
    - If passed 'index' will normalize over each row.
    - If passed 'columns' will normalize over each column.
    - If margins is `True`, will also normalize margin values.

    .. versionadded:: 0.18.1

Returns
-------
DataFrame
    Cross tabulation of the data.

See Also
--------
DataFrame.pivot : Reshape data based on column values.
pivot_table : Create a pivot table as a DataFrame.

Notes
-----
Any Series passed will have their name attributes used unless row or column
names for the cross-tabulation are specified.

Any input passed containing Categorical data will have **all** of its
categories included in the cross-tabulation, even if the actual data does
not contain any instances of a particular category.

In the event that there aren't overlapping indexes an empty DataFrame will
be returned.

3、Examples

--------

import pandas as pd
import numpy as np

df = pd.DataFrame({'A':[1,8,8,8,1],
                   'B':[6,6,4,4,4],
                   'C':[1,1,2,1,1]})
print('df:\n',df)

df:
A B C
0 1 6 1
1 8 6 1
2 8 4 2
3 8 4 1
4 1 4 1

data1=pd.crosstab(df['A'],df['B'])
print('data1:\n',data1)

data1:
B 4 6
A
1 1 1
8 2 1

##normalize=True表示统计交叉表中各项的相对频率（即所占百分比）
data2=pd.crosstab(df['A'],df['B'],normalize=True)
print("data2:\n",data2)

data2:
B 4 6
A
1 0.20 0.20
8 0.40 0.20

#values:根据因子聚合的值数组
#aggfunc：如果未传递values数组，则计算频率表，如果传递数组，则按照指定计算
data3 =pd.crosstab(df['A'],df['B'],values=df['C'],aggfunc=np.sum)
print('data3:\n',data3)

data3:
B 4 6
A
1 1 1
8 3 1

#margins:布尔值，默认值False，当其为True时，表示：添加行/列边距（小计）,
# 还可以通过margins_name设置总计行（列）的名称（默认名称是“All”）。
data4=pd.crosstab(df['A'],df['B'],values=df['C'],aggfunc=np.sum,margins=True)
print('data4:\n',data4)

data4:
B 4 6 All
A
1 1 1 2
8 3 1 4
All 4 2 6

# 分层交叉:crosstab()的参数index和columns可以接受列表传入，构建分层交叉表
data5=pd.crosstab([df['A'],df['B']],df['C'])
print('data5:\n',data5)

data5:
C 1 2
A B
1 4 1 0
6 1 0
8 4 1 1
6 1 0