How to merge dataframes to append missing values based on a matching column?

Merging DataFrames to Append Missing Values Based on a Matching Column

In the given scenario, the goal is to merge two dataframes, df1 and df2, based on the Name column. However, the desired output is to keep the information from df1 and fill missing values from df2 with NaN. The result should look like:

    Name  Age  Sex
0    Tom   34    M
1   Sara   18  NaN
2    Eva   44    F
3   Jack   27    M
4  Laura   30  NaN

Method 1: Using map by Series Created by set_index

This approach involves creating a Series from df2 by setting the Name column as the index. Then, use the map() method to match and fill the Sex values in df1.

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

print(df1)</code>

Method 2: Alternative Solution with Merge Using Left Join

An alternative solution is to merge df1 and df2 using the left join approach. This ensures that all rows from df1 are preserved, and missing values from df2 are filled with NaN.

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

print(df)</code>

Method 3: Mapping by Multiple Columns Using Merge with Left Join

If multiple columns are required for merging (e.g. Name and Year, Code), use merge with left join, specifying the desired columns.

<code class="python"># Merge by all columns
df = df1.merge(df2, on=['Year', 'Code'], how='left')

# Merge by specified columns
df = df1.merge(df2[['Year', 'Code', 'Val']], on=['Year', 'Code'], how='left')</code>

Handling Errors with Duplicate Keys

In some cases, duplicate Name values may exist, resulting in an error. To resolve this, consider removing duplicates or using dictionary-based mapping to ensure the last matching value is selected.

<code class="python"># Remove duplicates and create a Series for mapping
s = df2.drop_duplicates('Name').set_index('Name')['Val']
df1['New'] = df1['Name'].map(s)</code>

By employing any of these methods, you can effectively merge dataframes, preserving the information from the primary dataframe and filling missing values with NaN.

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