How to Merge DataFrames and Include Columns from Both?

Merging DataFrames with Column Inclusion

When merging two DataFrames, it's common to maintain information from the first while incorporating data from the second. Let's explore how to achieve this in Pandas.

Consider the following scenario:

• DataFrame df1 contains age information for individuals.
• DataFrame df2 contains sex information for individuals.

Our goal is to populate df1 with sex information while retaining information for individuals not present in df2.

Solution

Method 1: Using Pandas' merge Function with left Join

<code class="python">df = df1.merge(df2[['Name', 'Sex']], on='Name', how='left')</code>

This merge operation joins df1 on the Name column with df2 while retaining all rows from df1 (due to the left join) and updating values in Sex where available.

Method 2: Using Pandas' map Function

<code class="python">df1['Sex'] = df1['Name'].map(df2.set_index('Name')['Sex'])</code>

This approach uses the map function to map the Name column of df1 to the Sex column of df2 while setting Name as the index in df2. This effectively matches individuals in both DataFrames, populating missing values with NaN.

Considerations

If there are duplicate Name values in df2, the map approach may return inconsistent results. In such cases, consider de-duplicating df2 or using a dictionary-based mapping.

Furthermore, use the merge function with caution if Name contains missing values, as it will cause unmatched rows to be removed. If data integrity is critical, handle missing values appropriately before merging.

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