multiple nested lists with permutations to pandas

Question

I have my first serious question in python.

I have a few nested lists that I need to convert to pandas DataFrame. Seems easy, but what makes it challenging for me: - the lists are huge (so the code needs to be fast) - they are nested - when they are nested, I need combinations.

So having this input:

la =  ['a', 'b', 'c', 'd', 'e']
lb = [[1], [2], [3, 33], [11,12,13], [4]]
lc = [[1], [2, 22], [3], [11,12,13], [4]]

I need the below as output

la      lb      lc
a       1       1
b       2       2
b       2       22
c       3       3
c       33      3
d       11      11
d       11      12
d       11      13
d       12      11
d       12      12
d       12      13
d       13      11
d       13      12
d       13      13
e       4       4

Note that I need all permutations whenever I have a nested list. At first I tried simply:

import pandas as pd
pd.DataFrame({'la' : [x for x in la],
              'lb' : [x for x in lb],
              'lc' : [x for x in lc]})

But looking for rows that need expanding and actually expanding (a huge) DataFrame seemed harder than tinkering around the way I create the DataFrame.

I looked at some great posts about itertools (Flattening a shallow list in Python ), the documentation (https://docs.python.org/3.6/library/itertools.html) and generators (What does the "yield" keyword do?), and came up with something like this:

import itertools

def f(la, lb, lc):
    tmp = len(la) == len(lb) == len(lc)
    if tmp:
        for item in range(len(la)):
            len_b = len(lb[item])
            len_c = len(lc[item])
            if ((len_b>1) or (len_c>1)):
                yield list(itertools.product(la[item], lb[item], lc[item]))
                ## above: list is not the result I need,
                ##        without it it breaks (not an iterable)
            else:
                yield (la[item], lb[item], lc[item])
    else:
        print('error: unequal length')

which I test

my_gen =f(lit1, lit2, lit3)
pd.DataFrame.from_records(my_gen)

which... well... breaks when i yield itertools (it has no length), and creates a wrong data structure after I cast itertools to an iterable.

My questions are as follow:

how can I fix that issue with yielding itertools?
is this efficient? In real application I will be creating the lists by parsing a file and they will be huge... Any performance tips or better solutions from more advanced colleagues? Right not it breaks/misbehaves so I can't even benchmark...
would it make sense to generate the lists element by element and then use my f function?

Thank you in advance!

Lambda · Accepted Answer · 2018-02-25 01:11:11Z

2

I have a solution:

import pandas as pd
from itertools import product

la =  ['a', 'b', 'c', 'd', 'e']
lb = [[1], [2], [3, 33], [11,12,13], [4]]
lc = [[1], [2, 22], [3], [11,12,13], [4]]

list_product = reduce(lambda x, y: x + y, [list(product(*_)) for _ in zip(la,lb,lc)])
df = pd.DataFrame(list_product, columns=["la", "lb", "lc"])
print(df)

result:

    la  lb  lc
0   a   1   1
1   b   2   2
2   b   2   22
3   c   3   3
4   c   33  3
5   d   11  11
6   d   11  12
7   d   11  13
8   d   12  11
9   d   12  12
10  d   12  13
11  d   13  11
12  d   13  12
13  d   13  13
14  e   4   4

answered Feb 25, 2018 at 1:11

Lambda

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1 Comment

rpl Over a year ago

Ingenious! Thank you!

Jim Factor · Accepted Answer · 2018-02-24 23:29:12Z

It's not an abstract solution, but it does get the results you are looking for. I look forward to seeing a more pandas-centric answer to this problem, but offer this up in the mean time.

import pandas as pd
la =  ['a', 'b', 'c', 'd', 'e']
lb = [[1], [2], [3, 33], [11,12,13], [4]]
lc = [[1], [2, 22], [3], [11,12,13], [4]]

l1 = []
l2 = []
l3 = []

l1Temp = []
l2Temp = []
l3Temp = []

for i, listInt in enumerate(lb):
    if type(listInt == list):
        for j, item in enumerate(listInt):
            # print('%s - %s' % (lb[i], lc[i][j]))
            l1Temp.append(la[i])
            l2Temp.append(lb[i][j])
            l3Temp.append(lc[i])
            # print('%s - %s' % (l1[i], l2[i]))
    else:
        l1Temp.append(la[i])
        l2Temp.append(lb[i])
        l3Temp.append(lc[i])
        # print('%s - %s' % (lb[i], lc[i]))

for i, listInt in enumerate(l3Temp):
    if type(listInt == list):
        for j, item in enumerate(listInt):
            l1.append(l1Temp[i])
            l2.append(l2Temp[i])
            l3.append(l3Temp[i][j])
    else:
        l1.append(l1Temp[i])
        l2.append(l2Temp[i])
        l3.append(l3Temp[i])

for i, item in enumerate(l3):
    print('%s - %s - %s' % (l1[i], l2[i], l3[i]))

df = pd.DataFrame({'la':[x for x in l1],
    'lb':[x for x in l2],
    'lc': [x for x in l3]})
print(df)

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