Pandas:将复数列转换为模数和参数列

Question

情况
考虑以下包含复数的示例数据帧:

data = [
    [np.complex(+1.15208050, -2.48857386), np.complex(-0.85295162, +0.10011025), np.complex(-0.61440517, -1.15813006)],
    [np.complex(-1.36170542, -0.78118157), np.complex(+1.10912405, +0.87261775), np.complex(-0.55295896, +1.34406899)],
    [np.complex(-0.19407632, -0.61834442), np.complex(-0.14378835, +1.11290952), np.complex(-1.17956510, -0.47438966)],
    [np.complex(-0.09920323, -0.34497172), np.complex(-0.16600567, +0.81955786), np.complex(-1.54853844, -0.54138271)],
    [np.complex(-0.28935140, +0.10951172), np.complex(-1.32314178, -0.05319875), np.complex(-1.08771716, -1.09595183)],
]
columns = ["A", "B", "C"]
df = pd.DataFrame(data, columns=columns)

在控制台输出中如下所示:

                    A                   B                   C
0  1.152081-2.488574j -0.852952+0.100110j -0.614405-1.158130j
1 -1.361705-0.781182j  1.109124+0.872618j -0.552959+1.344069j
2 -0.194076-0.618344j -0.143788+1.112910j -1.179565-0.474390j
3 -0.099203-0.344972j -0.166006+0.819558j -1.548538-0.541383j
4 -0.289351+0.109512j -1.323142-0.053199j -1.087717-1.095952j

问题
我想将每一列转换为两列:一列是复数的模数，一列是复数的参数(以度为单位)。所需的结果数据框如下所示:

         A1          A2        B1          B2        C1          C2
0  2.742315  -65.158313  0.858806  173.305866  1.311014 -117.946614
1  1.569868 -150.158019  1.411247   38.194359  1.453370  112.362593
2  0.648086 -107.425218  1.122160   97.361855  1.271385 -158.091322
3  0.358952 -106.043604  0.836202  101.450632  1.640447 -160.729923
4  0.309382  159.269694  1.324211 -177.697584  1.544098 -134.783937

我将如何能够实现这一目标？

Answer 1

尝试:

df.agg([np.abs, np.angle])

输出(参数是辐射式的，您可以轻松转换为度数)

          A                   B                   C          
   absolute     angle  absolute     angle  absolute     angle
0  2.742315 -1.137227  0.858806  3.024758  1.311014 -2.058557
1  1.569868 -2.620752  1.411247  0.666617  1.453370  1.961097
2  0.648086 -1.874924  1.122160  1.699285  1.271385 -2.759214
3  0.358952 -1.850810  0.836202  1.770648  1.640447 -2.805266
4  0.309382  2.779781  1.324211 -3.101408  1.544098 -2.352423

或者您可以手动使用 pd.concat :

pd.concat([df.apply(np.abs).add_suffix(1),
           df.apply(np.angle, deg=True).add_suffix(2)
          ], axis=1
         ).sort_index(axis=1)

输出:

         A1          A2        B1          B2        C1          C2
0  2.742315  -65.158313  0.858806  173.305866  1.311014 -117.946614
1  1.569868 -150.158019  1.411247   38.194359  1.453370  112.362593
2  0.648086 -107.425218  1.122160   97.361855  1.271385 -158.091322
3  0.358952 -106.043604  0.836202  101.450632  1.640447 -160.729923
4  0.309382  159.269694  1.324211 -177.697584  1.544098 -134.783937