How to Merge DataFrames on a Column While Preserving the Initial Information in One DataFrame?

Merging DataFrames on a Column While Preserving Initial Information

Despite using Pandas' merge function, you are experiencing difficulties with merging dataframes df1 and df2 on the 'Name' column while retaining df1's information.

Issue:

In your merge operation:
df1 = pd.merge(df1, df2, on = 'Name', how = 'outer')

You are performing an outer join, which includes individuals from both dataframes. This causes the inclusion of individuals from df2 in df1, even if they do not appear in df1 originally.

Solution:

To address this issue, you can utilize one of the following methods:

Method 1: Using map by Series created by set_index:

df1['Sex'] = df1['Name'].map(df2.set_index('Name')['Sex'])

This approach establishes a Series with 'Name' as the index from df2's 'Sex' column. Then, df1's 'Name' column is mapped to this Series to assign the matching 'Sex' values. Missing values are resolved by setting them to NaN.

Method 2: Performing a left join:

df = df1.merge(df2[['Name','Sex']], on='Name', how='left')

A left join ensures that individuals from df1 are prioritized, with missing values filled with NaN if they are not present in df2.

Considerations:

• Multiple Merge Columns: If merging on multiple columns, specify them in the 'on' parameter, e.g., on=['Year','Code'].
• Duplicate Values: Duplicate merge column values can cause issues. Consider removing duplicates prior to merging or using map by a dictionary to select the appropriate value.

The above is the detailed content of How to Merge DataFrames on a Column While Preserving the Initial Information in One DataFrame?. For more information, please follow other related articles on the PHP Chinese website!