如何打開具有不同長度元組的 dataframe 列？

Question

我有一個 Pandas dataframe 看起來像這樣：

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組，然后用數字解壓列中的元組。 像這樣的東西：

我怎樣才能做到這一點？

謝謝！

Answer 1

如果我理解正確，您希望將元組中的內部值擴展為單獨的列：

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