I am trying to use the following function multiple times. I do not need help with binning my data as I have already figured that out. My question pertains more to looping through the same function multiple times, with a string input rather than a i = 1, 2, 3,... type input
##create rate impact bucket function
def bucket(rateimpact):
if rateimpact < -0.50:
return '1: Over -50%'
if rateimpact < -0.30:
return '2: (-50%,-30%)'
if rateimpact < -0.20:
return '3: (-20%,-30%)'
if rateimpact < -0.10:
return '4: (-10%,-20%)'
if rateimpact < -0.05:
return '5: (-10%,-5%)'
if rateimpact < 0.0:
return '6: (-5%,0%)'
if rateimpact < 0.05:
return '7: (0%,5%)'
if rateimpact < 0.10:
return '8: (5%,10%)'
if rateimpact < 0.20:
return '9: (10%, 20%)'
if rateimpact < 0.30:
return '10: (20%,30%)'
if rateimpact < 0.30:
return '11: (30%,50%)'
else:
return '12: Over 50%'
##create new column for rate impact bucket
bucket = policytable['Proposed_RateImpact'].apply(lambda rateimpact:
bucket(rateimpact))
bucket = bucket.astype('category')
rateimpact = policytable['Proposed_RateImpact']
How can I apply the same function above (bucket(rateimpact)) on a different column without creating the same function all over again:
##create rate impact bucket function for scenario #1
def bucket(scenario1_rateimpact):
if scenario1_rateimpact < -0.50:
return '1: Over -50%'
if scenario1_rateimpact < -0.30:
return '2: (-50%,-30%)'
if scenario1_rateimpact < -0.20:
return '3: (-30%,-20%)'
if scenario1_rateimpact < -0.10:
return '4: (-20%,-10%)'
if scenario1_rateimpact < -0.05:
return '5: (-10%,-5%)'
if scenario1_rateimpact < 0.0:
return '6: (-5%,0%)'
if scenario1_rateimpact < 0.05:
return '7: (0%,5%)'
if scenario1_rateimpact < 0.10:
return '8: (5%,10%)'
if scenario1_rateimpact < 0.20:
return '9: (10%, 20%)'
if scenario1_rateimpact < 0.30:
return '10: (20%,30%)'
if scenario1_rateimpact < 0.30:
return '11: (30%,50%)'
else:
return '12: Over 50%'
##create new column for rate impact bucket
scenario1_bucket = policytable['Scenario1_RateImpact'].apply(lambda
scenario1_rateimpact: bucket(scenario1_rateimpact))
scenario1_bucket = scenario1_bucket.astype('category')
scenario1_rateimpact = policytable['Scenario1_RateImpact']
I tried to change the apply(lambda scenario1_rateimpact: bucket(scenario1_rateimpact)) line of code to apply(lambda rateimpact: bucket(rateimpact)) in an attempt to call the original function, but I get an error if the bucket(sccenario1_rateimpact) code block is removed.
What I am hoping to do is have 1 bucket(rateimpact) function that I can apply to different columns in my dataframe, instead of 2 separate bucket functions (1 for each column). Is this possible? Maybe a loop would achieve what I am trying to do here?
