pandas 表横竖合concat，参考官方链接

竖合

# Merge, join, and concatenate
import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3', 'A4'],
                    'B': ['B0', 'B1', 'B2', 'B3', 'B4'],
                    'C': ['C0', 'C1', 'C2', 'C3', 'C4'],
                    'D': ['D0', 'D1', 'D2', 'D3', 'D4'],
                    'E': ['E0', 'E1', 'E2', 'E3', 'E4']
                   },
                           index=[0, 1, 2, 3,4])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'F': ['F4', 'F5', 'F6', 'F7'],
                    'D': ['D4', 'D5', 'D6', 'D7'],
                   },
                   index=[4, 5, 6, 7])

df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
                            'B': ['B8', 'B9', 'B10', 'B11'],
                            'C': ['C8', 'C9', 'C10', 'C11'],
                            'D': ['D8', 'D9', 'D10', 'D11']})

frames = [df1, df2, df3]
# 可以将三个表数据尾部追加到一起，但是每个表都有自己的index，df3是默认的1，2，3
# 合并之后index
'''concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
   keys=None, levels=None, names=None, verify_integrity=False, copy=True)
'''
result = pd.concat(frames)
print result

保留了各自的index以及各自数据，缺失的用
NaN填充，concat函数默认参数

join='inner'相当于求交集，ignore_index=True,忽略各自的index，合并后数据集新的index

result = pd.concat(frames, join='inner', ignore_index=True)
print result

可以用下面的形式修改index

# 指定'A'列数据为index
result.index = result['A']
# 使用reser_inex放弃之前的index，用0，1，2取代
rl = result.reset_index(drop=True)

横合

result = pd.concat([df1, df2], axis=1)
print result

使用join = inner参数

result = pd.concat([df1, df2], axis=1, join='inner')
print result

如果我们只想用df1中数据,等于以df1数据为准

result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])

类似于数据库操作merge

函数原型

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,
         validate=None)

对比数据库操作

left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3','K4'],
                     'A': ['A0', 'A1', 'A2', 'A3', 'A4'],
                     'B': ['B0', 'B1', 'B2', 'B3', 'B4']})

right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K5'],
                      'C': ['C0', 'C1', 'C2', 'C3', 'C5'],
                      'D': ['D0', 'D1', 'D2', 'D3', 'D5']}) 

left

result = pd.merge(left, right,how='left', on='key')
print result

right

result = pd.merge(left, right,how='right', on='key')

inner

result = pd.merge(left, right,how='inner', on='key')
print result

outer

result = pd.merge(left, right,how='outer', on='key')
print result

复杂一点的例子

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                     'key2': ['K0', 'K1', 'K0', 'K1'],
                     'A': ['A0', 'A1', 'A2', 'A3'],
                     'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],       
                      'key2': ['K0', 'K0', 'K0', 'K0'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})

left

result = pd.merge(left, right, how = 'left', on=['key1', 'key2'])
print result

right

result = pd.merge(left, right, how = 'right', on=['key1', 'key2'])
print result

inner

result = pd.merge(left, right, how = 'inner', on=['key1', 'key2'])
print result

outer

result = pd.merge(left, right, how = 'outer', on=['key1', 'key2'])
print result

通过left_index,right_index代替on

left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
                     'B': ['B0', 'B1', 'B2']},
                    index=['K0', 'K1', 'K2'])

right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
                      'D': ['D0', 'D2', 'D3']},
                      index=['K0', 'K2', 'K3'])

result = pd.merge(left, right, left_index=True, right_index=True, how='outer')
print result

下面两个表,以left表‘key’与right表index为标准merge

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'])

result = pd.merge(left, right, left_on='key', right_index=True,how='left', sort=False);
print result