使用 if 语句循环的有效方法

Question

I have a sample data look like this (real dataset has more columns):

data = {'stringID':['AB CD Efdadasfd','RFDS EDSfdsadf dsa','FDSADFDSADFFDSA'],'IDct':[1,3,4]}
data = pd.DataFrame(data)
data['Index1'] = [[3,6],[7,9],[5,6]]
data['Index2'] = [[4,8],[10,13],[8,9]]

What i want to achieve is i want to slice stringID column based on second elment in Index1 and Index2 (both are list), only if IDct value is bigger than 1, otherwise return NaN.

I tried this, it works as Output1 column, but there must be a better way (i mean faster when apply to a large dataset) to do it, please kindly advise, thanks!

data['pos'] = data.Index1.map(lambda x: x[1])
data['pos1'] = data.Index2.map(lambda x: x[1])

def cal(m):
    if m['IDct'] > 1:
        return m['stringID'][m['pos']:m['pos1']]
    else:
        return 'NaN'

data['Output1'] = data.apply(cal,axis=1)

Answer 1

I love pandas - but realistically speaking it's just one of many tools that belong in your tool belt.

pandas and numpy really shine for computation and analysis. It's okay to use pandas to visualize and analyze your data - but that doesn't mean it's the right tool for the job.

This kind of problem is better suited for regular python. Assuming we can, let's move StringID and IDct out of the dict and back into lists. If we assume the result is regular in shape (all lists are of equal length)

StringID = ['AB CD Efdadasfd','RFDS EDSfdsadf dsa','FDSADFDSADFFDSA'],
IDct = [1,3,4]
Index1 = [[3,6],[7,9],[5,6]]
Index2 = [[4,8],[10,13],[8,9]]

for stringID, IDct, Index1, Index2 in zip(stringID, IDct, Index1, Index2):
    result = []
    if IDct > 1:
       result.append(your_indexing_goes_here())
    else:
       result.append(None) 

You can then blend the result data back in as you see fit.

data = {
    'StringID': StringID,
    'IDct': IDct,
    'Index1': Index1,
    'Index2': Index2,
    'Result': result
}

pd.DataFrame(data)