实例：运算

import pandas as pd
a=pd.DataFrame({'name':['cindy','alice','ared'],'grade':[80,90,75],'age':[18,19,20]})
b=pd.DataFrame({'name':['cindy','alice','bob','ared'],'grade':[95,70,60,50]})
#表a的grade列计算极差
f=lambda x:x.max()-x.min()
print('a的grade列的极差：')
print(pd.DataFrame(a['grade']).apply(f,axis=0))

#默认按照索引相加，不匹配的索计算引结果为NaN
print('a+b:\n',a+b)
print('a与b的grade列相加:\n',pd.DataFrame(a['grade']+b['grade']))
print(a.loc[:,['grade']]+b.loc[:,['grade']])
#不同索引的相同字段值对应的指标相加：筛选并重置索引，然后计算
print('a的ared+b的ared:\n',pd.DataFrame(a.loc[a['name']=='ared',['grade']]).reset_index(drop=True)+pd.DataFrame(b.loc[b['name']=='ared',['grade']]).reset_index(drop=True))
#将计算结果的空值填充为0
print('将计算结果的空值填充为0:\n',(a+b).fillna(0))
#将不存在的索引对应的值填充为0,然后计算
print('将不存在的索引对应的值填充为0,然后计算:\n',pd.DataFrame(a['grade'].add(b['grade'],fill_value=0)))

runfile('C:/Users/xyy/.spyder-py3/temp.py', wdir='C:/Users/xyy/.spyder-py3')
a的grade列的极差：
grade    15
dtype: int64
a+b:
    age  grade        name
0  NaN  175.0  cindycindy
1  NaN  160.0  alicealice
2  NaN  135.0     aredbob
3  NaN    NaN         NaN
a与b的grade列相加:
    grade
0  175.0
1  160.0
2  135.0
3    NaN
   grade
0  175.0
1  160.0
2  135.0
3    NaN
a的ared+b的ared:
    grade
0    125
将计算结果的空值填充为0:
    age  grade        name
0  0.0  175.0  cindycindy
1  0.0  160.0  alicealice
2  0.0  135.0     aredbob
3  0.0    0.0           0
将不存在的索引对应的值填充为0,然后计算:
    grade
0  175.0
1  160.0
2  135.0
3   50.0

实例：计算两表中相同名称对应的指标和

import pandas as pd
a=pd.DataFrame({'name':['cindy','alice','ared'],'grade':[80,90,75],'age':[18,19,20]})
b=pd.DataFrame({'name':['cindy','alice','bob','ared'],'grade':[95,70,60,50]})
print('法1：按索引计算两表的指标列的和，然后合并两表，将合并结果与指标列的和再通过索引合并')
a_b=pd.merge(a,b,how='right',left_index=True,right_index=True,suffixes=('_a','_b')).fillna(0)
print('a右连接b:\n',a_b)
print('将不存在的索引对应的值填充为0,然后计算:\n',pd.merge(a_b,pd.DataFrame(a['grade'].add(b['grade'],fill_value=0)),left_index=True,right_index=True))
print('\n法2：直接合并表，并新增求和字段')
a_b['grade_sum']=a_b['grade_a']+a_b['grade_b']
print(a_b)

runfile('C:/Users/xyy/.spyder-py3/temp.py', wdir='C:/Users/xyy/.spyder-py3')
法1：按索引计算两表的指标列的和，然后合并两表，将合并结果与指标列的和再通过索引合并
a右连接b:
   name_a  grade_a   age name_b  grade_b
0  cindy     80.0  18.0  cindy       95
1  alice     90.0  19.0  alice       70
2   ared     75.0  20.0    bob       60
3      0      0.0   0.0   ared       50
将不存在的索引对应的值填充为0,然后计算:
   name_a  grade_a   age name_b  grade_b  grade
0  cindy     80.0  18.0  cindy       95  175.0
1  alice     90.0  19.0  alice       70  160.0
2   ared     75.0  20.0    bob       60  135.0
3      0      0.0   0.0   ared       50   50.0

法2：直接合并表，并新增求和字段
  name_a  grade_a   age name_b  grade_b  grade_sum
0  cindy     80.0  18.0  cindy       95      175.0
1  alice     90.0  19.0  alice       70      160.0
2   ared     75.0  20.0    bob       60      135.0
3      0      0.0   0.0   ared       50       50.0

实例：分类统计

import pandas as pd
import numpy as np
a=pd.DataFrame({'sex':['female','female','male','female'],'name':['cindy','alice','ared','cindy'],'grade':[80,90,75,70],'sale':[18,19,20,23]})
print('各人最大值：\n',a.groupby('name').max())
print('各性别各人最小值：\n',a.groupby(['sex','name']).min())
print('各性别各人销售总额：\n',a.groupby(['sex','name'])['sale'].sum())
print('各性别各人的指标个数、指标和、指标均值：\n',a.groupby(['sex','name'])['grade','sale'].agg([len,np.sum,np.mean]))
print('透视：\n',pd.pivot_table(a,index=['name'],columns=['sex'],values=['grade','sale'],aggfunc=[np.sum,np.mean],fill_value=0,margins=True))
print('透视总分、平均销售：\n',pd.pivot_table(a,index=['name'],columns=['sex'],values=['grade','sale'],aggfunc={'grade':np.sum,'sale':np.mean},fill_value=0,margins=True))

runfile('C:/Users/xyy/.spyder-py3/temp.py', wdir='C:/Users/xyy/.spyder-py3')
各人最大值：
           sex  grade  sale
name                      
alice  female     90    19
ared     male     75    20
cindy  female     80    23
各性别各人最小值：
               grade  sale
sex    name              
female alice     90    19
       cindy     70    18
male   ared      75    20
各性别各人销售总额：
 sex     name 
female  alice    19
        cindy    41
male    ared     20
Name: sale, dtype: int64
各性别各人的指标个数、指标和、指标均值：
              grade           sale          
               len  sum mean  len sum  mean
sex    name                                
female alice     1   90   90    1  19  19.0
       cindy     2  150   75    2  41  20.5
male   ared      1   75   75    1  20  20.0
透视：
          sum                             mean                             
       grade             sale           grade              sale           
sex   female male  All female male All female male   All female male   All
name                                                                      
alice     90    0   90     19    0  19     90    0  90.0   19.0    0  19.0
ared       0   75   75      0   20  20      0   75  75.0    0.0   20  20.0
cindy    150    0  150     41    0  41     75    0  75.0   20.5    0  20.5
All      240   75  315     60   20  80     80   75  78.0   20.0   20  20.0
透视总分、平均分、平均销售：
        grade             sale           
sex   female male  All female male   All
name                                    
alice     90    0   90   19.0    0  19.0
ared       0   75   75    0.0   20  20.0
cindy    150    0  150   20.5    0  20.5
All      240   75  315   20.0   20  20.0