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A simple guide to effectively merging two pandas dataframes in Python to compute historical pricing references.
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How to Join Two Pandas Dataframes for Price Lookup Across Years

In the world of data analysis, it's not uncommon to find ourselves needing to combine data from multiple sources to derive meaningful insights. One common task in data manipulation is merging two pandas dataframes. In this guide, we will walk through a specific problem and its solution involving pandas, focusing on how to compute a past value based on a current dataset. This is especially useful in scenarios such as automotive pricing, where historical prices are crucial for forecasting and analysis.

The Problem: Dataframe Merging for Historical Price Lookup

Imagine you have two dataframes representing the pricing of car models over a few years. You have Dataframe 1, which contains information about car models, their year, and the price for the last year.

Dataframe 1:

car_modelyearprice_lastyearXXX201716411You want to calculate price_lastyear for 2017 by referencing Dataframe 2, which has prices for the same car model but for different years:

Dataframe 2:

car_modelyearpriceXXX201616411XXX201711432XXX201812345The Challenge

The challenge here is to populate the price_lastyear in Dataframe 1 by looking up the price from Dataframe 2 for the previous year, which is 2016. Simply put, we want to join these two dataframes on the car_model and the year minus one.

The Solution: Merging Dataframes in Pandas

Step 1: Preparing Your Dataframes

Before we merge the two dataframes, we need to manipulate Dataframe 2 slightly. Since you want to reference the price of the previous year (2016 for 2017), we'll adjust the year in Dataframe 2 by adding one to the current year.

Step 2: The Merge Operation

Use the merge function from pandas, adjusting Dataframe 2 as per our need. Here’s how you can write the code:

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

Explanation of the Code

df2.assign(year=df2['year']+ 1): This line modifies Dataframe 2, incrementing each year by 1 year. As a result, 2016 becomes 2017, which will allow us to link it correctly with Dataframe 1.

on=['car_model','year']: This portion specifies the columns on which to join the two dataframes. We are merging based on the car_model and the adjusted year.

how='left': This indicates that we want a left join, meaning all records from Dataframe 1 will be kept, while matching records from Dataframe 2 will be fetched.

Step 3: The Output

After performing the merge operation, your resulting dataframe will look like this:

car_modelyearprice_lastyearpriceXXX20171641116411As you can see, the price_lastyear has been successfully populated with the correct value from Dataframe 2.

Conclusion

The ability to join and manipulate dataframes is a powerful tool in Python's pandas library, allowing you to gain insights from historical data effectively. The above method illustrates how you can reference data from a previous year by simply modifying your joining criteria. Now, you can apply these techniques to your own datasets to make informed decisions and analyses.

With this approach, you should feel more confident in tackling similar challenges in your data processing tasks. Happy coding!