如何打开具有不同长度元组的 dataframe 列？

Question

我有一个 Pandas dataframe 看起来像这样：

df = pd.DataFrame({'c1': [10, 20, 30, 40, 50,60,70],
                     'c2': [[(0.14507, 0.163054, 0.330704), (0.069578, 0.075506, 0.145531), (0.039168, 0.042362, 0.097616), (0.038133, 0.04009, 0.079482), (0.039872, 0.041051, 0.06478), (0.039158, 0.039898, 0.054787), (0.03744, 0.037917, 0.047526), (0.035354, 0.035669, 0.041999), (0.033199, 0.03341, 0.037646), (0.031113, 0.031256, 0.034127), (0.02916, 0.029257, 0.031222), (0.027361, 0.027428, 0.028784), (0.02572, 0.025767, 0.026709)],
                                     np.nan,
                                     [(0.000341, 0.000391, 0.000991), (0.006122, 0.006682, 0.013834), (0.008017, 0.008671, 0.01998), (0.011591, 0.012186, 0.02416), (0.016047, 0.016522, 0.026072), (0.019587, 0.019957, 0.027405), (0.022358, 0.022643, 0.028382), (0.024514, 0.024733, 0.029122), (0.02619, 0.026356, 0.029698), (0.027492, 0.027618, 0.030155), (0.028507, 0.028602, 0.030523), (0.0293, 0.029373, 0.030825), (0.029924, 0.029979, 0.031075)],
                                     [(0.000341, 0.000391, 0.000991), (0.006122, 0.006682, 0.013834), (0.008017, 0.008671, 0.01998), (0.011591, 0.012186, 0.02416), (0.016047, 0.016522, 0.026072), (0.019587, 0.019957, 0.027405), (0.022358, 0.022643, 0.028382), (0.024514, 0.024733, 0.029122), (0.02619, 0.026356, 0.029698), (0.027492, 0.027618, 0.030155), (0.028507, 0.028602, 0.030523), (0.0293, 0.029373, 0.030825), (0.029924, 0.029979, 0.031075)],
                                     [(0.041119, 0.046924, 0.111008), (0.027748, 0.030228, 0.061034), (0.019769, 0.02138, 0.049267), (0.021487, 0.02259, 0.044787), (0.024792, 0.025526, 0.04028), (0.026614, 0.027117, 0.037236), (0.027595, 0.027947, 0.03503), (0.028072, 0.028322, 0.033348), (0.028237, 0.028416, 0.03202), (0.028208, 0.028337, 0.03094), (0.02806, 0.028153, 0.030044), (0.02784, 0.027908, 0.029288), (0.02758, 0.02763, 0.02864)],
                                     [(5.25, 6.16, 7.7)],
                                     [(0.003174, 0.003642, 0.009186), (0.007794, 0.008506, 0.017575), (0.009007, 0.009741, 0.022447), (0.012455, 0.013094, 0.025961), (0.016847, 0.017345, 0.027372), (0.020272, 0.020655, 0.028363), (0.022917, 0.023209, 0.029091), (0.024952, 0.025175, 0.029642), (0.026517, 0.026685, 0.03007), (0.027721, 0.027848, 0.030406), (0.028649, 0.028745, 0.030676), (0.029367, 0.02944, 0.030895), (0.029924, 0.029979, 0.031075)]]})´´´

我想拆分不同列中的所有元组，然后用数字解压列中的元组。 像这样的东西：

我怎样才能做到这一点？

谢谢！

Answer 1

如果我理解正确，您希望将元组中的内部值扩展为单独的列：

x = df["c2"].explode().explode().groupby(level=0).agg(list).apply(pd.Series)
x.columns = [f"c{c+3}" for c in x.columns]
print(pd.concat([df["c1"], x], axis=1))

印刷：

   c1        c3        c4        c5        c6        c7        c8        c9       c10       c11       c12       c13       c14       c15       c16       c17       c18       c19       c20       c21       c22       c23       c24       c25       c26       c27       c28       c29       c30       c31       c32       c33       c34       c35       c36       c37       c38       c39       c40       c41
0  10  0.145070  0.163054  0.330704  0.069578  0.075506  0.145531  0.039168  0.042362  0.097616  0.038133  0.040090  0.079482  0.039872  0.041051  0.064780  0.039158  0.039898  0.054787  0.037440  0.037917  0.047526  0.035354  0.035669  0.041999  0.033199  0.033410  0.037646  0.031113  0.031256  0.034127  0.029160  0.029257  0.031222  0.027361  0.027428  0.028784  0.025720  0.025767  0.026709
1  20       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN
2  30  0.000341  0.000391  0.000991  0.006122  0.006682  0.013834  0.008017  0.008671  0.019980  0.011591  0.012186  0.024160  0.016047  0.016522  0.026072  0.019587  0.019957  0.027405  0.022358  0.022643  0.028382  0.024514  0.024733  0.029122  0.026190  0.026356  0.029698  0.027492  0.027618  0.030155  0.028507  0.028602  0.030523  0.029300  0.029373  0.030825  0.029924  0.029979  0.031075
3  40  0.000341  0.000391  0.000991  0.006122  0.006682  0.013834  0.008017  0.008671  0.019980  0.011591  0.012186  0.024160  0.016047  0.016522  0.026072  0.019587  0.019957  0.027405  0.022358  0.022643  0.028382  0.024514  0.024733  0.029122  0.026190  0.026356  0.029698  0.027492  0.027618  0.030155  0.028507  0.028602  0.030523  0.029300  0.029373  0.030825  0.029924  0.029979  0.031075
4  50  0.041119  0.046924  0.111008  0.027748  0.030228  0.061034  0.019769  0.021380  0.049267  0.021487  0.022590  0.044787  0.024792  0.025526  0.040280  0.026614  0.027117  0.037236  0.027595  0.027947  0.035030  0.028072  0.028322  0.033348  0.028237  0.028416  0.032020  0.028208  0.028337  0.030940  0.028060  0.028153  0.030044  0.027840  0.027908  0.029288  0.027580  0.027630  0.028640
5  60  5.250000  6.160000  7.700000       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN       NaN
6  70  0.003174  0.003642  0.009186  0.007794  0.008506  0.017575  0.009007  0.009741  0.022447  0.012455  0.013094  0.025961  0.016847  0.017345  0.027372  0.020272  0.020655  0.028363  0.022917  0.023209  0.029091  0.024952  0.025175  0.029642  0.026517  0.026685  0.030070  0.027721  0.027848  0.030406  0.028649  0.028745  0.030676  0.029367  0.029440  0.030895  0.029924  0.029979  0.031075