pd.crosstab(index, # 分组依据
   columns, # 列
   values=None, # 聚合计算的值
   rownames=None, # 列名称
   colnames=None, # 行名称
   aggfunc=None, # 聚合函数
   margins=False, # 总计行/列
   dropna=True, # 是否删除缺失值
   normalize=False # 
   )

import os
import numpy as np
import pandas as pd

file_name = os.path.join(path, 'Excel_test.xls')
df = pd.read_excel(io=file_name, # 工作簿路径
     sheetname='透视表', # 工作表名称
     skiprows=1, # 要忽略的行数
     parse_cols='A:D' # 读入的列
     )
df

pd.crosstab(df['客户名称'], df['产品类别'])

pd.crosstab(index=df['客户名称'],
   columns=df['产品类别'],
   values=df['销量'],
   aggfunc='sum',
   margins=True
   ).round(0).fillna(0).astype('int')

df.groupby(['客户名称', '产品类别']).apply(sum)

c_tbl = df.groupby(['客户名称', '产品类别']).apply(sum)['销量'].unstack()
c_tbl['总计'] = c_tbl.sum(axis=1) # 添加总计列
c_tbl.fillna(0).round(0).astype('int')

quality price
0  bad 18
1  bad 17
2  great  52
3  good  28
4  excellent  88
5  great  63
6  bad 8
7  good  22
8  good  68
9  excellent  98
10 great  53
11 bad 13
12 great  62
13 good  48
14 excellent  78
15 great  63
16 good  37
17 great  69
18 good  28
19 excellent  81
20 great  43
21 good  32
22 great  62
23 good  28
24 excellent  82
25 great  53

  import pandas as pd
  from pandas import DataFrame, Series
  #生成数据
  df = DataFrame([['bad', 18], ['bad', 17], ['great', 52], ['good', 28], ['excellent', 88], ['great', 63]
        , ['bad', 8], ['good', 22], ['good', 68], ['excellent', 98], ['great', 53]
        , ['bad', 13], ['great', 62], ['good', 48], ['excellent', 78], ['great', 63]
        , ['good', 37], ['great', 69], ['good', 28], ['excellent', 81], ['great', 43]
        , ['good', 32], ['great', 62], ['good', 28], ['excellent', 82], ['great', 53]], columns = ['quality', 'price'])
#广播使用的函数
def quality_cut(data):
  s = Series(pd.cut(data['price'], np.arange(0, 100, 10)))
  return pd.groupby(s, s).count()
#进行分组处理
df.groupby(df['quality']).apply(quality_cut)

<class 'pandas.core.frame.DataFrame'>
  quality price
0   bad   18
1   bad   17
6   bad   8
11   bad   13

  price
(0, 10]   1
(10, 20]  3
(20, 30]  0
(30, 40]  0
(40, 50]  0
(50, 60]  0
(60, 70]  0
(70, 80]  0
(80, 90]  0