Construct pandas dataframe from nested list

Question

I have a nested list like this

a=[ [[12,23],[31,41]], [[53,68],[77,87]] ]

and I'd like to create a pandas data frame like this

id1  id2   c2   c3
1     1    12   31
1     2    53   77
2     1    23   41
2     2    68   87

My idea was to do this:

ids = {'id1':[1,1,2,2],'id2':[1,2,1,2]}
df1 = pd.DataFrame(ids)
L = [[t[idx] for t in l] for idx in [0,1] for l in a]
df2 = pd.DataFrame(L, columns=['c1','c2'])
pd.concat([df1,df2], axis=1)

Is there a way to achieve the same result without so many commands?

Show your work and research. What happens if you just do it directly? — Mad Physicist
– Mad Physicist, Commented Mar 22, 2018 at 20:39
I don't understand how you generate id1 and id2. Please explain @HappyPy — YOLO
– YOLO, Commented Mar 22, 2018 at 20:59

cs95 · Accepted Answer · 2018-03-22 21:18:11Z

2

from itertools import product

# generate id1 and id2 using `itertools.product`
v = np.arange(len(a))    
i = pd.DataFrame(np.array(list(product(v, v))) + 1)
# concatenate `a` along the 0th axis
j = pd.DataFrame(np.concatenate(a, axis=0))

# concatenate `i` and `j`
df = pd.concat([i, j], axis=1)
# set the column names
df.columns = ['id1', 'id2', 'c2', 'c3']

   id1  id2  c2  c3
0    1    1  12  23
1    1    2  31  41
2    2    1  53  68
3    2    2  77  87

answered Mar 22, 2018 at 21:18

cs95

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