Reshaping with pd.wide_to_long

So far, we have encountered two very viable ways of converting data from wide to long format, whether it be through the use of the pd.DataFrame.stack method, introduced in our Reshaping with pd.DataFrame.stack and pd.DataFrame.unstack recipe, or through the use of the pd.DataFrame.melt, as we saw in the Reshaping with pd.DataFrame.melt recipe.

If those aren’t enough, pandas offers the pd.wide_to_long function, which can help with that conversion given that your columns follow a particular naming pattern, as we will see in this recipe.

How to do it

Let’s assume we have the following pd.DataFrame, where we have one id variable of widget and four columns representing sales from a business quarter. Each column of sales begins with "quarter_":

df = pd.DataFrame([
    ["Widget 1", 1, 2, 4, 8],
    ["Widget 2", 16, 32, 64, 128],
], columns=["widget", "quarter_1", "quarter_2...