最小最大列返回查找之间的熊猫

Question

I see a few questions about looking up a single value in between a range like this one , however I need something that loops over all rows and is a bit more performative.

# I have some dataset (10k to 1m rows)
values = pd.DataFrame([["foo", 5], ["bar", 15]], columns=["foobar", "values"])

# and a lookup table (25 rows)
lookups = pd.DataFrame([["A1", 0, 10], ["A2", 10, 20]], columns=["tier", "min", "max"])

My desired out come would be a lookup of tiers based on the value of values, and between the range of min & max on lookup table:

    foobar  values  tier
0      foo       5    A1
1      bar      15    A2

And i've got something working, but it's scaling really poorly:

def lookup(score):
    for idx, row in lookups.iterrows():
        if row["min"] <= score < row["max"]:
            return row["tier"]

values["tier"] = values["values"].apply(lookup)

My second thought would be to create a dataframe where the index is just (0-lookup.max.max()] with the tiers repeated/tiled, but was hoping there was a more built in option?

Thanks

Answer 1

This is pd.cut :

values['tier'] = pd.cut(values['values'], 
       bins=list(lookups['min']) + [lookups['max'].iloc[-1]],
       labels=lookups['tier']
      )

Output:

  foobar  values tier
0    foo       5   A1
1    bar      15   A2