dataframe join

A DataFrame join is a method for combining two or more DataFrames into a single DataFrame based on a common column. The join operation matches rows from each DataFrame where the values in the common column(s) are equal.

There are several types of joins available in pandas:

1. Inner join: Only the rows with matching values in both DataFrames are included in the result.

2. Left join: All the rows from the left DataFrame and the matching rows from the right DataFrame are included in the result. If there is no match, the result will contain NaN values for the right DataFrame columns.

3. Right join: All the rows from the right DataFrame and the matching rows from the left DataFrame are included in the result. If there is no match, the result will contain NaN values for the left DataFrame columns.

4. Outer join: All the rows from both DataFrames are included in the result. If there is no match, the result will contain NaN values for the columns from the DataFrame that does not have a match.

To perform a join in pandas, you can use the `merge()` method and specify the type of join using the `how` parameter. You also need to specify the common column(s) using the `on` parameter.

For example, to perform an inner join on two DataFrames `df1` and `df2` based on a common column `key`, you can use the following code:

result = pd.merge(df1, df2, on='key', how='inner')