Michael Hooreman Michael Hooreman - 1 month ago 8
Python Question

Python pandas: flatten with arrays in column

I have a pandas Data Frame having one column containing arrays. I'd like to "flatten" it by repeating the values of the other columns for each element of the arrays.

I succeed to make it by building a temporary list of values by iterating over every row, but it's using "pure python" and is slow.

Is there a way to do this in pandas/numpy? In other words, I try to improve the flatten function in the example below.

Thanks a lot.

toConvert = pd.DataFrame({
'x': [1, 2],
'y': [10, 20],
'z': [(101, 102, 103), (201, 202)]
})

def flatten(df):
tmp = []
def backend(r):
x = r['x']
y = r['y']
zz = r['z']
for z in zz:
tmp.append({'x': x, 'y': y, 'z': z})
df.apply(backend, axis=1)
return pd.DataFrame(tmp)

print(flatten(toConvert).to_string(index=False))


Which gives:

x y z
1 10 101
1 10 102
1 10 103
2 20 201
2 20 202

Answer

Here's a NumPy based solution -

np.column_stack((toConvert[['x','y']].values.\
     repeat(map(len,toConvert.z),axis=0),np.hstack(toConvert.z)))

Sample run -

In [78]: toConvert
Out[78]: 
   x   y                z
0  1  10  (101, 102, 103)
1  2  20       (201, 202)

In [79]: np.column_stack((toConvert[['x','y']].values.\
    ...:      repeat(map(len,toConvert.z),axis=0),np.hstack(toConvert.z)))
Out[79]: 
array([[  1,  10, 101],
       [  1,  10, 102],
       [  1,  10, 103],
       [  2,  20, 201],
       [  2,  20, 202]])
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