Combining data with different schemas

Published on: 29th September 2022

Combining data with different schemas

This post was created while writing my Up & Running with Polars course. Check it out here with a free preview of the first chapters

You’ve got a bunch of data files in your project and they all follow a consistent data schema 😊

You get a new file and see that from now on there will be some useful extra columns. How are you going to combine this file with the old stuff?? 😣

A vertical concatenation won’t work as it doesn’t like schema changes.

This is where diagonal concatenation in Polars comes in.

# Old schema year, exporter, importer
dfTrades2020 = pl.DataFrame(
    [
        {"year":2020,"exporter":"China","importer":"USA"},
        {"year":2020,"exporter":"China","importer":"USA"},
    ]
)
# New schema includes value
dfTrades2021 = pl.DataFrame(
    [
        {"year":2021,"exporter":"China","importer":"USA","value":10},
        {"year":2021,"exporter":"China","importer":"USA","value":100},
    ]
)
# Diagonal concatenation
pl.concat([dfTrades2020,dfTrades2021],how="diagonal")

Diagonal concatenation appends your new records with their new columns, and add nulls to the new columns for the old records to show the data is missing. Sorted.

Learn more

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• check out my Polars course on Udemy
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or let me know if you would like a Polars workshop for your organisation.