目录
- 1.Merge
- 1.1 简单关联:left_on与right_on
- 1.2 使用how参数:指定连接方式
- 1.3 right_index与right_index
- 1.4 sort参数:排序
- 2.join
1.Merge
Pandas具有全功能的,高性能内存中连接操作,与关系型数据库中的连接操作类似。
语法:
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False)
1.1 简单关联:left_on与right_on
下面是Merge的一些实战案例:
(1)创建测试数组
import pandas as pd
df1 = pd.DataFrame({'studentNo':['A01','A02','A03','A04'],
'studentName':['Jack','Lucy','Marry','Tom'],
'studentAge':[18,19,21,17],
'classNo':['class01','class02','class03','class02']
})
df2 = pd.DataFrame({'classNo':['class01','class02','class03','class04'],
'className':['火箭1班','火箭2班','火箭3班','火箭4班']
})
# 学生信息
print(df1)
# 班级信息
print(df2)
(2)使用Merge,找出每个学生对应的班级名字
print(pd.merge(df1, df2, on="classNo")) # 这里的df1类似关系型数据库的主表,df2对应
# 如果存在多个键关联,则on = ['key1','key2']
输出结果:
1.2 使用how参数:指定连接方式
# 1.取交集:默认取交集
print('--------参数how = \'inner\'的结果--------------')
print(pd.merge(df1, df2, on="classNo",how='inner')) # 类似内连接
# 2.取并集,数据缺失则为NaN
print('--------参数how = \'outer\'的结果--------------')
print(pd.merge(df1, df2, on="classNo",how='outer')) # 类似全连接
# 3.左连接
print('--------参数how = \'left\'的结果--------------')
print(pd.merge(df1, df2, on="classNo",how='left'))
# 4.右连接
print('--------参数how = \'right\'的结果--------------')
print(pd.merge(df1, df2, on="classNo",how='right'))
1.3 right_index与right_index
import pandas as pd
df1 = pd.DataFrame({'left_key':list('abcd'),
'data1':range(4)})
df2 = pd.DataFrame({'right_key':list('acef'),
'data2':range(4)})
# df1 以‘lkey’为键,df2以'rkey'为键
print(pd.merge(df1,df2,left_on='left_key',right_on='right_key'))
df1 = pd.DataFrame({'key':list('abcdfeg'),
'data1':range(7)})
df2 = pd.DataFrame({'data2':range(1,6)},
index = list('abcde'))
print('----------df1-------------')
print(df1)
print('----------df2-------------')
print(df2)
print('----------df1,df2,参数:left_on=\'key\',right_index=True-------------')
print(pd.merge(df1,df2,left_on='key',right_index=True))
# df1采用key列值作为关联数据,df2采用index作为关联数据
# left_index:为True时,第一个df以index为键,默认False
# right_index:为True时,第二个df以index为键,默认False
# 所以left_on, right_on, left_index, right_index可以相互组合
1.4 sort参数:排序
import pandas as pd
# 参数sort
df1 = pd.DataFrame({'key':list('baecd'),
'data1':[10,2,4,7,5]})
df2 = pd.DataFrame({'key':list('abc'),
'data2':[11,2,33]})
print('-------------df1-------------')
print(df1)
print('-------------df2-------------')
print(df2)
y1 = pd.merge(df1,df2, on = 'key', how = 'outer')
y2 = pd.merge(df1,df2, on = 'key', sort=True, how = 'outer')
print('-------------df1与df2全连接的结果-------------')
print(y1)
print('-------------df1与df2全连接并根据连接键排序的结果-------------')
print(y2)
# sort:按照字典顺序通过 连接键 对结果DataFrame进行排序。默认为False,设置为False会大幅提高性能
print('-------------df1与df2全连接的结果根据data1排序-------------')
# 也可以连接完毕后直接使用DataFrame的排序方法
print(y2.sort_values('data1'))
2.join
import pandas as pd
# pd.join 通过索引连接
left = pd.DataFrame({'A':['A01','A02','A03'],
'B':['B01','B02','B03']
},index=['k1','k2','k3'])
right = pd.DataFrame({'C':['C01','C02','C03','C04'],
'D':['D01','D02','D03','D04']
},index=['k1','k2','k3','k4'])
print(left,'-----------left-----------\n')
print(left,'-----------right-----------\n')
print(left.join(right),'-----------left.join(right)-----------\n')
print(left.join(right,how='outer'),'-----------left.join(right)-----------\n')
# 上述语句等价于 pd.merge(left, right, left_index=True, right_index=True, how='outer')
df1 = pd.DataFrame({'key':list('bbacde'),
'data1':[1,3,5,7,2,4]})
df2 = pd.DataFrame({'key':list('abc'),
'data2':[11,2,15]
})
print('-----------df1-----------')
print(df1)
print('-----------df2-----------')
print(df2)
# 指定参数suffixes
print('-' * 50)
print(pd.merge(df1, df2, left_index=True, right_index=True, suffixes=('_1', '_2')))
# 用df1中的索引与df2['data2']中的索引连接
print('-' * 50)
# print(df2['data2'])
print(df1.join(df2['data2']))
left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'key': ['K0', 'K1', 'K0', 'K1']})
right = pd.DataFrame({'C': ['C0', 'C1'],
'D': ['D0', 'D1']},
index=['K0', 'K1'])
print(left)
print(right)
print(left.join(right, on = 'key'))
# 等价于pd.merge(left, right, left_on='key', right_index=True, how='left', sort=False);
# left的‘key’和right的index
输出结果:
原文地址:https://www.cnblogs.com/OliverQin/p/12332422.html
时间: 2024-10-18 08:31:57