高级处理-合并
学习目标
- 目标
- 应用pd.concat实现数据的合并
- 应用pd.merge实现数据的合并
- 应用
- 无
- 4.8.1 pd.concat实现合并
- 4.8.2 pd.merge实现合并
- 4.8.3 总结
如果你的数据由多张表组成，那么有时候需要将不同的内容合并在一起分析
4.8.1 pd.concat实现合并
- pd.concat([data1, data2], axis=1)
- 按照行或列进行合并,axis=0为列索引，axis=1为行索引
比如我们将刚才处理好的one-hot编码与原数据合并
# 按照行索引进行
pd.concat([data, dummies], axis=1)
4.8.2 pd.merge实现合并
- 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)
- 可以指定按照两组数据的共同键值对合并或者左右各自
-
left: A DataFrame object -
right: Another DataFrame object -
on: Columns (names) to join on. Must be found in both the left and right DataFrame objects. - left_on=None, right_on=None：指定左右键
| 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 |
示例：
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']})
# 默认内连接
result = pd.merge(left, right, on=['key1', 'key2'])
- 左连接
result = pd.merge(left, right, how='left', on=['key1', 'key2'])
- 右连接
result = pd.merge(left, right, how='right', on=['key1', 'key2'])
- 外链接
result = pd.merge(left, right, how='outer', on=['key1', 'key2'])
4.8.3 总结
- concat进行按照索引的合并
- merge进行按照键合并