s1 = Series([4, 3, 7, 2, 8], index=['z', 'y', 'j', 'i', 'e']), d1 = DataFrame({'e': [4, 2, 6, 1], 'f': [0, 5, 4, 2]})
时间: 2023-10-23 13:12:22 浏览: 24
To merge s1 and d1 based on the common index 'e', we can use the following code:
```
merged = pd.merge(s1, d1, left_index=True, right_index=True)
```
This will result in a DataFrame that contains the values from s1 and d1 that have a common index 'e'. The resulting DataFrame will have two columns, one for the values from s1 and one for the values from d1.
Alternatively, we can use the join method of s1 to join it with d1 based on the common index 'e':
```
merged = s1.join(d1, how='inner')
```
This will also result in a DataFrame that contains the values from s1 and d1 that have a common index 'e'. The resulting DataFrame will have two columns, one for the values from s1 and one for the values from d1. The 'inner' join type is used here to only include the rows that have a common index in both s1 and d1.
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