Build pandas data frame from list of numpy arrays

As @MaxGhenis pointed out in the comments, from_items is deprecated as of version 0.23. The link suggests to use from_dict instead, so the old answer can be modified to:

pd.DataFrame.from_dict(dict(zip(names, data)))

--------------------------------------------------OLD ANSWER-------------------------------------------------------------

I would use .from_items:

pd.DataFrame.from_items(zip(names, data))

which gives

  data1  data2  data3
0      0      0      0
1      1      1      1
2      2      2      2
3      3      3      3
4      4      4      4
5      5      5      5
6      6      6      6
7      7      7      7
8      8      8      8
9      9      9      9

That should also be faster than transposing:

%timeit pd.DataFrame.from_items(zip(names, data))

1000 loops, best of 3: 281 µs per loop

%timeit pd.DataFrame(data, index=names).T

1000 loops, best of 3: 730 µs per loop

Adding a fourth column is then also fairly simple:

df['data4'] = range(1, 11)

which gives

  data1  data2  data3  data4
0      0      0      0      1
1      1      1      1      2
2      2      2      2      3
3      3      3      3      4
4      4      4      4      5
5      5      5      5      6
6      6      6      6      7
7      7      7      7      8
8      8      8      8      9
9      9      9      9     10

As mentioned by @jezrael in the comments, a third option would be (beware: order not guaranteed)

pd.DataFrame(dict(zip(names, data)), columns=names)

Timing:

%timeit pd.DataFrame(dict(zip(names, data)))

1000 loops, best of 3: 281 µs per loop

There are many ways to solve your problem, but the easiest way seems to be df.T (T being shorthand for pandas.DataFrame.transpose):

>>> df = pd.DataFrame(data=data, index=names)
>>> df
       0  1  2  3  4  5  6  7  8  9
data1  0  1  2  3  4  5  6  7  8  9
data2  0  1  2  3  4  5  6  7  8  9
data3  0  1  2  3  4  5  6  7  8  9

>>> df.T 
   data1  data2  data3
0      0      0      0
1      1      1      1
2      2      2      2
3      3      3      3
4      4      4      4
5      5      5      5
6      6      6      6
7      7      7      7
8      8      8      8
9      9      9      9