How to Merge DataFrames on a Column While Preserving Information from the Primary DataFrame?

Merging DataFrames on a Column while Preserving Information

When working with data in Python using Pandas, merging dataframes based on common columns is a common task. However, sometimes it's necessary to retain information from both dataframes, especially when they contain overlapping but incomplete data. This article explores a solution to merge dataframes on a column while ensuring that information from the primary dataframe is preserved.

Problem Statement

Consider two dataframes, df1 and df2. df1 contains information about individuals' ages, while df2 contains their gender. The goal is to merge df1 and df2 on the 'Name' column, but only keep information from df1. Individuals may not always be present in both dataframes.

Solution

To achieve this, we can use the map() method of the Series created by setting the index of one dataframe to the column on which we want to merge. The map() method allows us to apply a mapping function, which in this case will be a lookup in the other dataframe.

<code class="python"># Create the dataframes
df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'],
                    'Age': [34, 18, 44, 27, 30]})

df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Jack', 'Michelle'],
                    'Sex': ['M', 'M', 'F', 'M', 'F']})

# Set the index of df2 to Name
df2.set_index('Name', inplace=True)

# Perform the lookup using map
df1['Sex'] = df1['Name'].map(df2['Sex'])

# Display the merged dataframe
print(df1)</code>

Alternative Solution: Left Join

Alternatively, we can use a merge with a left join, which ensures that all rows from df1 are included in the merged dataframe, even if there is no corresponding row in df2.

<code class="python"># Perform the left join
df3 = df1.merge(df2, on='Name', how='left')

# Display the merged dataframe
print(df3)</code>

Note: If the dataframes contain multiple columns for merging, use merge(on=['Year', 'Code'], how='left') or specify the columns after the left merge (e.g., df1.merge(df2[['Year', 'Code', 'Val']], on=['Year', 'Code'], how='left')).

Handling Duplicates

If the columns used for merging have duplicates, it's important to handle duplicate values to prevent ambiguity. This can be done using drop_duplicates() or by using a dictionary to specify the mapping.

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