Read lists into columns of pandas DataFrame

Question

I want to load lists into columns of a pandas DataFrame but cannot seem to do this simply. This is an example of what I want using transpose() but I would think that is unnecessary:

In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: x = np.linspace(0,np.pi,10)
In [4]: y = np.sin(x)
In [5]: data = pd.DataFrame(data=[x,y]).transpose()
In [6]: data.columns = ['x', 'sin(x)']
In [7]: data
Out[7]: 
          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]

Is there a way to directly load each list into a column to eliminate the transpose and insert the column labels when creating the DataFrame?

Answer 1

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)'])

Answer 2

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)