Make sume of specific value of 2d arrays-CodePudding

No values here. Closing this!!

CodePudding user response：

Assuming all ranges are exclusive, that is, no range overlaps another range, you could iterate over this 2D array and reduce to a dict:

from collections import defaultdict

data = [['Less than $40K', '0.569'], ['$60K - $80K' ,'0'], ...]

res = defaultdict(float)

for tup in data:
  rng, amt = tup
  res[rng]  = float(amt)

print(list(res.items()))

[('Less than $40K', 4.3919999999999995), ('$60K - $80K', 0.545), ('$80K - $120K', 0.625), ('Unknown', 0.6659999999999999), ('$40K - $60K', 0.37200000000000005), ('$120K  ', 0.4)]

CodePudding user response：

Create a dictionary that uses the first column value in your avgUtilArray as key. We just check if there is a value stored for that key already, in which case, we add to it. Otherwise, we make a new entry in dictionary with that key.

output = {}

for row in avgUtilArray:
    key = row[0]
    value = float(row[1])
    if key in output:
        output[key]  = value
    else:
        output[key] = value

print(output)

The output:

{'Less than $40K': 4.3919999999999995, '$60K - $80K': 0.545, '$80K - $120K': 0.625, 'Unknown': 0.6659999999999999, '$40K - $60K': 0.37200000000000005, '$120K  ': 0.4}

Also, I corrected your 2d list for you:)

avgUtilArray = [

['Less than $40K', '0.569'],
['$60K - $80K', '0'],
['Less than $40K', '0.3'],
['$80K - $120K', '0.045'],
['Unknown', '0.091'],
['$40K - $60K', '0'],
['$80K - $120K', '0.056'],
['$60K - $80K', '0'],
['Less than $40K', '0.595'],
['$120K  ', '0.097'],
['$80K - $120K', '0.066'],
['$60K - $80K', '0.216'],
['$120K  ', '0.031'],
['Less than $40K', '0.316'],
['Less than $40K', '0.045'],
['$60K - $80K', '0.065'],
['$80K - $120K', '0.051'],
['Less than $40K', '0.342'],
['$80K - $120K', '0.041'],
['Unknown', '0'],
['$40K - $60K', '0'],
['$40K - $60K', '0'],
['Less than $40K', '0'],
['$80K - $120K', '0'],
['Unknown', '0'],
['Unknown', '0.575'],
['$60K - $80K', '0.032'],
['Less than $40K', '0.505'],
['$120K  ', '0.05'],
['$80K - $120K', '0.366'],
['Less than $40K', '0.182'],
['Less than $40K', '0.332'],
['Less than $40K', '0.194'],
['$40K - $60K', '0.058'],
['Less than $40K', '0.228'],
['$120K  ', '0.038'],
['Less than $40K', '0.155'],
['Less than $40K', '0.371'],
['$60K - $80K', '0'],
['Less than $40K', '0.258'],
['$120K  ', '0.036'],
['$120K  ', '0.148'],
['$60K - $80K', '0.037'],
['$120K  ', '0'],
['Unknown', '0'],
['$60K - $80K', '0.195'],
['$60K - $80K', '0'],
['$40K - $60K', '0.229'],
['$40K - $60K', '0.085'],
['Unknown', '0']
]