What is the difference between join and merge in Pandas?

Pandas is a powerful library used for data manipulation and analysis. One of its most useful features is merging or joining two DataFrames together to create a new DataFrame that combines the data from both original DataFrames.

In this article, we’ll explore the differences between using the join method and the merge method in Pandas. We’ll delve into the underlying functionality, usage, and best practices for each method.

Introduction

When working with DataFrames in Pandas, it’s common to have two separate DataFrames that contain related data. Merging or joining these DataFrames together is a crucial step in data analysis, as it allows us to combine the data from both sources and perform further analysis.

In this article, we’ll examine the join method and the merge method in Pandas, discussing their differences and usage scenarios.

The merge function

The merge function is an underlying function used for all merge/join behavior. It’s a part of the pandas namespace and provides a flexible way to join two DataFrames together.

DataFrames provide the merge() and join() methods as a convenient way to access the capabilities of the merge() function.

For example, df1.merge(right=df2, ...) is equivalent to pandas.merge(left=df1, right=df2, ...). This means that when you use the join() method, you’re essentially calling the merge() function under the hood.

Differences between join and merge

There are several key differences between using the join method and the merge method in Pandas:

1. Lookup on right table

The join method always joins via the index of the right table, whereas the merge method can join to one or more columns of the right table (default) or to the index of the right table (right_index=True).

For example:

left = pd.DataFrame({'key1': ['foo', 'bar'], 'lval': [1, 2]})
right = pd.DataFrame({'key2': ['foo', 'bar'], 'rval': [4, 5]})

# Joining via index of right table
result = left.join(right)

# Joining to columns of right table (default)
result = left.merge(right, on='key1')

# Joining to index of right table (with right_index=True)
result = left.merge(right, left_on='key1', right_index=True)

2. Lookup on left table

By default, the join method uses the index of the left table and the columns of the right table for joining. The merge method uses column(s) of the left table by default.

For example:

left = pd.DataFrame({'key1': ['foo', 'bar'], 'lval': [1, 2]})
right = pd.DataFrame({'key2': ['foo', 'bar'], 'rval': [4, 5]})

# Joining to columns of left table (default)
result = left.join(right)

# Joining to index of right table
result = left.merge(right, on='key2')

3. Inner join vs. outer join

By default, the merge method performs an inner join, whereas the join method can be used for both inner and outer joins.

For example:

left = pd.DataFrame({'key1': ['foo', 'bar'], 'lval': [1, 2]})
right = pd.DataFrame({'key2': ['foo', 'baz'], 'rval': [4, 5]})

# Inner join (default for merge)
result = left.merge(right, on='key1')

# Outer join
result = left.join(right)

Best practices

While both join and merge methods can be used to combine DataFrames, there are some best practices to keep in mind:

• Use the merge method when you need more control over the joining process or when performing inner joins.
• Use the join method when you want to join on the index of one table or when performing outer joins.
• Always specify the columns used for joining, especially when working with multiple tables.

Conclusion

In conclusion, while both join and merge methods can be used to combine DataFrames in Pandas, there are key differences between them. Understanding these differences is crucial for effective data analysis and manipulation using Pandas.

By following best practices and choosing the right method for your use case, you’ll be able to efficiently merge or join DataFrames in your Python code.

Related resources

• Pandas documentation on merging
• Database-style DataFrame joining and merging

Last modified on 2023-09-19