Merging DataFrames in Pandas: Techniques and Best Practices

Merging multiple data sources is a common task in data analysis and processing, and the Pandas library provides a powerful set of tools for performing this operation in Python.

The ability to combine data from different sources allows us to gain insights, draw conclusions, and make better decisions based on a more complete and integrated view of the data.

However, joining data can be a complex and error-prone process if not done correctly, and it is essential to have a clear understanding of the available methods and best practices to ensure accurate and efficient results.

This topic covers the four key ways for combining DataFrames using pandas in Python.

1. Inner Join

An inner join returns only the matching rows from both dataframes based on a specified column. As a result, it retains the rows with matching keys in both dataframes.

In simpler terms, it is similar to the intersection of two sets.

Assuming we have two dataframes, and want to join them on the ID column, we would follow this syntax. Also, we can use pd.merge to perform the join.

The syntax is as follows:

# Perfoming an inner join
df1.merge(df2, on = 'ID', how  = 'inner'

# Using pd.merge to perform an inner join
pd.merge(df1, df2, on = 'ID' how = 'inner')

The result rows from df1 and df2 have matching rows based on the key id.

1. Left Join

A left join returns all the rows from the left dataframe and the matching rows from the right dataframe based on the specified column.

Referring to the set diagram, a left join will return the left circle and the intersection between the two circles.

The syntax is as follows:

# Performing a left Join
df1.merge(df2, on = 'ID', how = 'left'

#Using pd.merge
pd.merge(df1, df2, on = 'ID' how = 'left')

The result is all rows from the left dataframe, and matching rows in the right dataframe.

1. Right Join

A right join is similar to a left join but it returns all the rows from the right dataframe and the matching rows from the left dataframe based on the specified column.

The syntax is as follows:

# Performing a left Join 
df1.merge(df2, on = 'ID', how = 'right' 

#Using pd.merge 
pd.merge(df1, df2, on = 'ID' how = 'right')

The result is all rows from the right dataframe and matching rows from the left dataframe.

1. Outer Join

An outer join will return all the rows from both dataframes, and fills null values where there are no matching rows.

The syntax of an outer join in Pandas is as follows:

# Performing a left Join 
df1.merge(df2, on = 'ID', how = 'outer' 

#Using pd.merge 
pd.merge(df1, df2, on = 'ID' how = 'outer')

Conclusion

While working on data, we may find ourselves having to use multiple dataframes or tables. Pandas provides several techniques for combining dataframes effectively. This makes it easy to combine the data based on how we would like to use the data. It, therefore, remains important to choose the right type of join based on the data and requirements of the analysis.