Pandas Merge DataFrame - Python

Pandas is a popular Python library for data manipulation and analysis. It provides functionalities for importing, cleaning, transforming, merging, and analyzing data. In this article, we will focus on how to merge DataFrames in pandas.

The Merge Function

The merge() function in pandas is used to combine two DataFrames based on one or more common columns. The syntax of the merge() function is as follows:

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=True)

The parameters of the merge() function are defined as follows:

• left: The left DataFrame to be merged.
• right: The right DataFrame to be merged.
• how: The method used to combine the DataFrames. It can be 'inner', 'outer', 'left', and 'right'.
• on: The column(s) to be used for the merge. It can be a single column name or a list of column names.
• left_on: The column(s) to be used from the left DataFrame for the merge.
• right_on: The column(s) to be used from the right DataFrame for the merge.
• left_index: If True, use the index from the left DataFrame for the merge.
• right_index: If True, use the index from the right DataFrame for the merge.
• sort: Sort the result DataFrame by the columns involved in the merge.

Types of Merge

There are four types of merge operations that can be performed with the merge() function:

Inner Merge

The inner merge combines only the rows that have matching values in both DataFrames. To perform an inner merge, set the how parameter of the merge() function to 'inner'.

merged_inner = pd.merge(left, right, how='inner', on='key')

Outer Merge

The outer merge combines all the rows from both DataFrames, with NaN for missing values. To perform an outer merge, set the how parameter of the merge() function to 'outer'.

merged_outer = pd.merge(left, right, how='outer', on='key')

Left Merge

The left merge combines all the rows from the left DataFrame and matching rows from the right DataFrame. For non-matching rows from the right DataFrame, it will have NaN for the columns of the left DataFrame. To perform a left merge, set the how parameter of the merge() function to 'left'.

merged_left = pd.merge(left, right, how='left', on='key')

Right Merge

The right merge combines all the rows from the right DataFrame and matching rows from the left DataFrame. For non-matching rows from the left DataFrame, it will have NaN for the columns of the right DataFrame. To perform a right merge, set the how parameter of the merge() function to 'right'.

merged_right = pd.merge(left, right, how='right', on='key')

Conclusion

Merging DataFrames is an important operation in data analysis. Pandas provides a powerful function merge() to combine DataFrames based on common columns. With the different types of merge operations available in pandas, you can easily combine, clean, and analyze large datasets.