I'm trying to bin data and apply a float value based on the bin. I thought pandas.cut was the tool for this, but apparently it requires unique values for each bin label.

```
values = [0.6, 0.5, 0.5, 0.6, 0.8, 0.9]
bins = [0, 2, 5, 10, 15, 25, 200]
binned = pd.cut(original_table[field], bins, labels=values)

>>> ValueError: Categorical categories must be unique

```

My data (original\_table) is very large and doing anything iteratively is quite slow, which is why cut was an appealing tool. Is there a workaround to make pd.cut work for this?