Explain how inner and left joins work in Python and provide examples.

Question

Assisting AI · Accepted Answer

In pandas, joins are performed using the `merge` function, which aligns rows from two DataFrames based on key columns. An **inner join** returns only rows with matching keys in both DataFrames, analogous to SQL's INNER JOIN. A **left join** (or left outer join) returns all rows from the left DataFrame and matches from the right; unmatched rows get NaN.

Example:

```python
import pandas as pd

# Left DataFrame
left = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['Alice', 'Bob', 'Charlie']
})

# Right DataFrame
right = pd.DataFrame({
    'id': [2, 3, 4],
    'score': [88, 92, 75]
})

# Inner join on 'id'
inner = pd.merge(left, right, on='id', how='inner')
print('Inner join:
', inner)

# Left join on 'id'
left_join = pd.merge(left, right, on='id', how='left')
print('
Left join:
', left_join)
```

Output:
```
Inner join:
    id     name  score
0   2      Bob     88
1   3  Charlie     92

Left join:
    id     name  score
0   1    Alice    NaN
1   2      Bob   88.0
2   3  Charlie   92.0
```

Key points: `how='inner'` keeps only matching keys; `how='left'` keeps all left keys. You can also specify `right`, `outer`, or `cross` for other join types. Understanding these options lets you manipulate relational data efficiently in pandas.

Explain how inner and left joins work in Python and provide examples.

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