pandas之concat and merge
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
Merge method | SQL Join Name | Description |
|---|---|---|
left | LEFT OUTER JOIN | Use keys from left frame only |
right | RIGHT OUTER JOIN | Use keys from right frame only |
outer | FULL OUTER JOIN | Use union of keys from both frames |
inner | INNER JOIN | Use intersection of keys from both frames |
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
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原始发表：2018.04.07 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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