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