高级处理-合并
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
1 pd.concat 实现数据合并
- 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
 (1) (1) (1) (1).843652aa.png)
python
# 按照行索引进行
pd.concat([data, dummies], axis=1)# 按照行索引进行
pd.concat([data, dummies], axis=1)2 pd.merge
- pd.merge(left, right, how='inner', on=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: DataFrameright: 另一个 DataFrameon: 指定的共同键- how:按照什么方式连接
| 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 |
2.1 pd.merge 合并
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'])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']) (1) (1) (1) (1).0396979c.png)
- 左连接
python
result = pd.merge(left, right, how='left', on=['key1', 'key2'])result = pd.merge(left, right, how='left', on=['key1', 'key2']) (1) (1) (1).af355618.png)
- 右连接
python
result = pd.merge(left, right, how='right', on=['key1', 'key2'])result = pd.merge(left, right, how='right', on=['key1', 'key2']) (1) (1) (1).d01366d0.png)
- 外链接
python
result = pd.merge(left, right, how='outer', on=['key1', 'key2'])result = pd.merge(left, right, how='outer', on=['key1', 'key2']) (1) (1) (1).9dea0967.png)