sparc_spread - 10 months ago 133

Python Question

The pandas

documentation states that: "Out of bounds values will be NA in the resulting Categorical object." This makes it difficult when the upper bound is not necessarily clear or important. For example:`cut (weight, bins=[10,50,100,200])`

Will produce the bins:

`[(10, 50] < (50, 100] < (100, 200]]`

So

`cut (250, bins=[10,50,100,200])`

`NaN`

`cut (5, bins=[10,50,100,200])`

`> 200`

`< 10`

I realize I could do

`cut (weight, bins=[float("inf"),10,50,100,200,float("inf")])`

`(200, inf]`

`labels`

`cut()`

`bins`

Have I exhausted all the possibilities, or is there something in

`cut()`

`pandas`

`cut()`

Answer Source

After waiting a few days, still no answers posted - I think that's probably because there really is no way around this other than writing the `cut()`

wrapper function. I am posting my version of it here and marking the question as answered. I will change that if new answers come along.

```
def my_cut (x, bins,
lower_infinite=True, upper_infinite=True,
**kwargs):
r"""Wrapper around pandas cut() to create infinite lower/upper bounds with proper labeling.
Takes all the same arguments as pandas cut(), plus two more.
Args :
lower_infinite (bool, optional) : set whether the lower bound is infinite
Default is True. If true, and your first bin element is something like 20, the
first bin label will be '<= 20' (depending on other cut() parameters)
upper_infinite (bool, optional) : set whether the upper bound is infinite
Default is True. If true, and your last bin element is something like 20, the
first bin label will be '> 20' (depending on other cut() parameters)
**kwargs : any standard pandas cut() labeled parameters
Returns :
out : same as pandas cut() return value
bins : same as pandas cut() return value
"""
# Quick passthru if no infinite bounds
if not lower_infinite and not upper_infinite:
return pd.cut(x, bins, **kwargs)
# Setup
num_labels = len(bins) - 1
include_lowest = kwargs.get("include_lowest", False)
right = kwargs.get("right", True)
# Prepend/Append infinities where indiciated
bins_final = bins.copy()
if upper_infinite:
bins_final.insert(len(bins),float("inf"))
num_labels += 1
if lower_infinite:
bins_final.insert(0,float("-inf"))
num_labels += 1
# Decide all boundary symbols based on traditional cut() parameters
symbol_lower = "<=" if include_lowest and right else "<"
left_bracket = "(" if right else "["
right_bracket = "]" if right else ")"
symbol_upper = ">" if right else ">="
# Inner function reused in multiple clauses for labeling
def make_label(i, lb=left_bracket, rb=right_bracket):
return "{0}{1}, {2}{3}".format(lb, bins_final[i], bins_final[i+1], rb)
# Create custom labels
labels=[]
for i in range(0,num_labels):
new_label = None
if i == 0:
if lower_infinite:
new_label = "{0} {1}".format(symbol_lower, bins_final[i+1])
elif include_lowest:
new_label = make_label(i, lb="[")
else:
new_label = make_label(i)
elif upper_infinite and i == (num_labels - 1):
new_label = "{0} {1}".format(symbol_upper, bins_final[i])
else:
new_label = make_label(i)
labels.append(new_label)
# Pass thru to pandas cut()
return pd.cut(x, bins_final, labels=labels, **kwargs)
```