Interweave two dataframes

Question

Suppose I have two dataframes d1 and d2

d1 = pd.DataFrame(np.ones((3, 3), dtype=int), list('abc'), [0, 1, 2])
d2 = pd.DataFrame(np.zeros((3, 2), dtype=int), list('abc'), [3, 4])

---

d1

   0  1  2
a  1  1  1
b  1  1  1
c  1  1  1

---

d2

   3  4
a  0  0
b  0  0
c  0  0

---

What is an easy and generalized way to interweave two dataframes' columns. We can assume that the number of columns in d2 is always one less than the number of columns in d1. And, the indices are the same.

I want this:

pd.concat([d1[0], d2[3], d1[1], d2[4], d1[2]], axis=1)

   0  3  1  4  2
a  1  0  1  0  1
b  1  0  1  0  1
c  1  0  1  0  1

Answer 1

Using pd.concat to combine the DataFrames, and toolz.interleave reorder the columns:

from toolz import interleave

pd.concat([d1, d2], axis=1)[list(interleave([d1, d2]))]

The resulting output is as expected:

   0  3  1  4  2
a  1  0  1  0  1
b  1  0  1  0  1
c  1  0  1  0  1

Answer 2

Here's one NumPy approach -

def numpy_interweave(d1, d2):
    c1 = list(d1.columns)
    c2 = list(d2.columns)
    N = (len(c1)+len(c2))
    cols = [None]*N
    cols[::2] = c1
    cols[1::2] = c2

    out_dtype = np.result_type(d1.values.dtype, d2.values.dtype)
    out = np.empty((d1.shape[0],N),dtype=out_dtype)
    out[:,::2] = d1.values
    out[:,1::2] = d2.values

    df_out = pd.DataFrame(out, columns=cols, index=d1.index)
    return df_out

Sample run -

In [346]: d1
Out[346]: 
   x  y  z
a  6  7  4
b  3  5  6
c  4  6  2

In [347]: d2
Out[347]: 
   p  q
a  4  2
b  7  7
c  7  2

In [348]: numpy_interweave(d1, d2)
Out[348]: 
   x  p  y  q  z
a  6  4  7  2  4
b  3  7  5  7  6
c  4  7  6  2  2