Read lists into columns of pandas DataFrame

Someone just recommended creating a dictionary from the data then loading that into the DataFrame like this:

In [8]: data = pd.DataFrame({'x': x, 'sin(x)': y})
In [9]: data
Out[9]: 
          x        sin(x)
0  0.000000  0.000000e+00
1  0.349066  3.420201e-01
2  0.698132  6.427876e-01
3  1.047198  8.660254e-01
4  1.396263  9.848078e-01
5  1.745329  9.848078e-01
6  2.094395  8.660254e-01
7  2.443461  6.427876e-01
8  2.792527  3.420201e-01
9  3.141593  1.224647e-16

[10 rows x 2 columns]

Note than a dictionary is an unordered set of key-value pairs. If you care about the column orders, you should pass a list of the ordered key values to be used (you can also use this list to only include some of the dict entries):

data = pd.DataFrame({'x': x, 'sin(x)': y}, columns=['x', 'sin(x)'])

If you don't care about the column names, you can use this:

pd.DataFrame(zip(*[x,y]))

run-time-wise it is as fast as the dict option, and both are much faster than using transpose.

Here's another 1-line solution preserving the specified order, without typing x and sin(x) twice:

data = pd.concat([pd.Series(x,name='x'),pd.Series(y,name='sin(x)')], axis=1)