Function not returning proper values (Python)

Question

I have a function below that should return standardized outputs based on numeric ranges:

`def incident(count):
if count['incident_ct']<= 4: 
    val = 1 
elif count['incident_ct']>4 & count['incident_ct']<= 13: # 25 to 50%
    val = 2
elif count['incident_ct'] >13 & count['incident_ct']<=31: # 50 to 75%
    val = 4
elif count['incident_ct'] >31 & count['incident_ct']<=100: # 75 to 95%
    val = 8
else: 
    val = 16
return val`

Then applied to new row in the data:

`intersections['v_counts'] = intersections.apply(incident, axis = 1)`

However, the output is not giving what I specified in the ranges (only 1 or 2 in the v_count) When looking at my code, the incident_ct = 34 should be 8 and where incident_ct = 172 should be 16

BENY · Accepted Answer · 2020-05-05 00:55:16Z

1

Let us try use pd.cut

pd.cut(intersections['incident_ct'],bins=[4,13,31,100,..],labels=[1,2,4,8,16])

Fix your code

def incident(count):
...     if count['incident_ct']<= 4:
...         val = 1
...     elif count['incident_ct']>4 and count['incident_ct']<= 13: # 25 to 50%
...         val = 2
...     elif count['incident_ct'] >13 and count['incident_ct']<=31: # 50 to 75%
...         val = 4
...     elif count['incident_ct'] >31 and count['incident_ct']<=100: # 75 to 95%
...         val = 8
...     else:
...         val = 16
...     return val

edited May 5, 2020 at 0:55

answered May 5, 2020 at 0:44

BENY

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2 Comments

sTonystork Over a year ago

ok this works to an extent but there are a couple more variables where I will be using conditions from multiple columns. I'd prefer if I can get this if-else function to work properly and not use pd.cut. Do you have any torubleshooting recommendations?

sTonystork Over a year ago

that was it! I also just figured out another simple way by adding ( ) between the &.

sTonystork · Accepted Answer · 2020-05-05 01:00:12Z

Use parentheses if using the '&' def incident(count): if count['incident_ct']<= 4: # lowest percent val = 1 elif (count['incident_ct']>4) & (count['incident_ct']<= 13): # 25 to 50% val = 2 elif (count['incident_ct'] >13) & (count['incident_ct']<=31): # 50 to 75% val = 4 elif (count['incident_ct'] >31) & (count['incident_ct']<=100): # 75 to 95% val = 8 else: # upper 5% val = 16 return val

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