How to Efficiently Create a New Column in a Pandas DataFrame Based on Another DataFrame

Learn how to speed up the process of creating a new column in a Pandas DataFrame based on another DataFrame's values using efficient techniques.
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How to Efficiently Create a New Column in a Pandas DataFrame Based on Another DataFrame

Creating a new column in a DataFrame can be a common yet complex task in data manipulation, especially when relying on values from another DataFrame. In this guide, we’ll tackle a situation where you need to determine which continent a team belongs to, based on home and away team data from a sports tournament. The following sections will guide you through the problem and provide an efficient solution.

The Problem:

You have two DataFrames: one lists countries alongside their corresponding continents while the other details match data, including home and away teams. You want to create a new column in the second DataFrame to indicate the continent if both teams belong to the same one. The challenge is that with a large dataset (over 39,000 rows), your existing method is running too slowly.

Example DataFrames:

Here's a simplified view of the two DataFrames you'll be working with:

DataFrame 1: country_continent

CountryContinentAfghanistanAsiaAlbaniaEuropeAlgeriaAfricaAmerican SamoaOceaniaDataFrame 2: df_cau2

datehome_teamaway_teamhome_scoreaway_scoretournamentcitycountryneutral1872-11-30ScotlandEngland00FriendlyGlasgowScotlandFalse1873-03-08EnglandScotland42FriendlyLondonEnglandFalse1874-03-07ScotlandEngland21FriendlyGlasgowScotlandFalseYou need to create a new column, continent, indicating if the home_team and away_team are from the same continent.

The Solution: Speeding Up the Process

Instead of using .apply() which can be slow for large DataFrames, we can take advantage of the Pandas map() function along with logical indexing. Here’s how you can efficiently map the continent information:

Step 1: Map the Continents

You will first need to map the continents for both the home and away teams based on your continent DataFrame:

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

Step 2: Compare the Continents

With both continents mapped, you can create a boolean mask to check where the home and away teams belong to the same continent:

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

Step 3: Assign Values to the New Column

Now that you have identified which rows match, you can assign the continent names to the new continent column for those rows:

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

Complete Code Example:

Here's how the complete implementation looks:

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

Expected Output

The updated DataFrame will show the continent for rows where teams played from the same continent:

home_teamaway_teamcontinentCanadaEnglandNaNFranceSpainEuropeChinaJapanAsiaConclusion

By using map() combined with logical indexing, you can efficiently create new columns in a Pandas DataFrame without experiencing the slowdowns associated with apply(). This method not only optimizes your runtime but also makes your code cleaner and easier to understand. Try implementing these techniques to handle larger datasets more effectively!