Pandas数据连接pd.concat的实现

pd.concat(objs, axis=0, join='outer', ignore_index=False, 
          keys=None, levels=None, names=None, sort=False,
          verify_integrity=False, copy=True)

import pandas as pd
df1 = pd.DataFrame({'x':[1,2],'y':[3,4]})
df2 = pd.DataFrame({'x':[5,6],'y':[7,8]})
res1 = pd.concat([df1,df2])
# 效果同上
res2 = df1.append(df2)

import pandas as pd
df1 = pd.DataFrame({'x':[1,2],'y':[3,4]})
df2 = pd.DataFrame({'x':[5,6,0],'y':[7,8,0]})
res = pd.concat([df1,df2], axis=1)

import pandas as pd
df1 = pd.DataFrame({'x':[1,2],'y':[3,4]})
df2 = pd.DataFrame({'x':[5,6,0],'y':[7,8,0]})
# 按列合并交集
# 传入join='inner'取得两个DataFrame的共有部分，去除了df1没有的第三行内容
res = pd.concat([df1,df2], axis=1, join='inner')

# 两种方法
res1 = pd.concat([df1,df2],axis=1).reindex(df1.index)
res2 = pd.concat([df1,df2.reindex(df1.index)],axis=1)

import pandas as pd
z = pd.Series([9,9],name='z')
df = pd.DataFrame({'x':[1,2],'y':[3,4]})
# 将序列加到新列
res = pd.concat([df,z],axis=1)

import pandas as pd
df1 = pd.DataFrame({'x':[1,2],'y':[3,4]})
df2 = pd.DataFrame({'x':[5,6],'y':[7,8]})
# 指定索引名
res1 = pd.concat([df1,df2], keys=['a','b'])
# 以字典形式传入
dict = {'a':df1, 'b':df2}
res2 = pd.concat(dict)
# 横向合并，指定索引
res3 = pd.concat([df1,df2], axis=1, keys=['a','b'])