How to unpack a dataframe column with tuples of different length?-CodePudding

I have a Pandas dataframe that looks like this:

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)]]})´´´

I want to split all the tuples in different columns and then unpack the tuples in columns with the numbers. Something like this:

How can I do that?

Thank you!

CodePudding user response：

If I understand you correctly, you want to expand the inner values in the tuples into separate columns:

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))

Prints:

   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