How to Add Missing Values from One DataFrame to Another Using Pandas

Learn how to efficiently add missing data from one DataFrame to another in Python using Pandas with a step-by-step guide.
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Introduction

Dealing with missing data is a common challenge faced by data analysts and data scientists. If you're using Python and Pandas for data manipulation, the need to combine information from multiple DataFrames can arise frequently. In this guide, we'll explore how to add missing values from one DataFrame to another by merging two different Excel files.

The Problem

Imagine you have two Excel files that you have read into Pandas as DataFrames. The first DataFrame (df1) is a master file containing several columns, one of which includes company names and their IDs. The second DataFrame (df2) has information about company IDs along with additional details, but it may not include all company IDs from the first DataFrame.

For example, df1 might look like this:

company_idfound_keywordsno_of_urlcompany_nameIQ137156215insurance15Zühlke Technology Group AGIQ3806173insurance15BT España, Compañía de Servicios Globales de T...IQ40333012insurance4Technoserv GroupIQ51614192insurance15Octo Telematics S.p.A.You want to add those company IDs and company names from df1 that are not present in df2.

The Solution

To achieve this, we can use the merge function in Pandas. The steps below will guide you through the process.

Step 1: Read the Excel Files

First, ensure you have read your Excel files into DataFrames using Pandas. Here’s how you would typically do that:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Rename Columns

Next, for clarity and to match the columns for merging, we need to rename the columns in df1 to have the same names as those in df2.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Merge the DataFrames

We can now merge the two DataFrames using the merge function with how='outer'. This will keep all records from both DataFrames and fill in missing values with NaN where applicable.

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Display the Output

You can then display the merged DataFrame to see the result:

[[See Video to Reveal this Text or Code Snippet]]

The output will look like this:

By using the merge function with the appropriate parameters, you can effectively combine two DataFrames in Pandas, ensuring that you retain all relevant data, even if some entries are missing from one DataFrame. This technique is invaluable for data analysis and manipulation in Python.

Now, whenever you find yourself needing to combine multiple sources of data in Pandas, remember this step-by-step method to successfully manage missing values.