Pandas 数据透视表，其中一个值列由值列表组成

Question

我有一个 Pandas 数据框，我希望对其进行旋转，其中包含的

values

条目之一由值列表组成。

df

    l1_ratio    mse_path    coef    intercept
0   1.0 581499561.8597653   [253.03936312711443, 3649.109906786345, 14798.282876868554, 4810.759749988098, 8878.435479294187, 3276.7488397077536, 5171.81213816377, 9059.114207547327, 11.512129682487922, -0.0, 8710.623469210213, 0.0, 0.0, -598.4862056332136, 28425.47104579133, 0.0, -0.0, 0.0, 0.0, -2198.436487334907, -2418.25205363894, 0.0, 1692.592401606466, -0.0, 390.8734205415331, 4623.261872236998, 699.2497258078156, 62.239979130074225, 0.0, -0.0, 1399.6393449513669, 912.4450426439255, 0.0, -0.0, -0.0, -0.0, -209.73967722935762, 0.0, -684.1610010188986, 162.79546249454427, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0, -1070.3678099420492, -0.0, 0.0, 0.0, -0.0, 0.0, -1073.2954543338647, 0.0, 0.0, -0.0, -0.0, 951.6951496704327, -0.0, 0.0, -0.0, -0.0, 580.5064483922187, -0.0, -0.0, 0.0, 0.0, -172.30638224708616, 0.0, -0.0, 176.66963320427527, -0.0, 0.0, 2101.100923369119, -0.0, -0.0, 0.0, 1899.3349417983618, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0, -444.2106622645467, 4079.307030203663, 5932.224851788115, -0.0, -0.0, 0.0, -0.0, 1271.547535404467, 4589.157779146342, -0.0, -0.0, -0.0, 1546.2122719295912, 43.68350839455409, 799.1353377003892, -0.0, 0.0, -0.0, -0.0, -0.0, -0.0, 1997.1599067500804, 0.0, -0.0, -0.0, 0.0, -0.0, -139.11014781577043, -2134.548060887319, -2725.175680089511, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 762.0978139944438, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 2641.475746141273, 0.0, -0.0, 1665.684460419034, 0.0, 757.0523501978001, -0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0, -0.0, -370.42602621137394, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0, -1944.4825663547163, -591.9747091248928, 0.0, 0.0, -0.0, -0.0, -1838.374401828182, -0.0, 0.0, -0.0, 0.0, -517.5891475030328, 1476.6782408902266, 0.0, 0.0, -0.0, -0.0, -5174.996138309539, 0.0, -0.0, -3801.7738352516685, -0.0, 0.0, 0.0, -0.0, -0.0, 3963.0786330603796, -266.91651148289, -1241.125779832931, 0.0, -0.0, 577.5306432786302, -0.0, 0.0, -0.0, 0.0, -0.0, 747.9365019997231, -0.0, 0.0, -0.0, -0.0, 0.0, -0.0, -0.0, -182.83255241378126, 0.0, -0.0, -0.0, -0.0, -796.6213861438707, 0.0, -0.0, -0.0, -0.0, -0.0, -162.4390369338168, -4651.940469706817, 0.0, -4659.32104928002, -0.0, -0.0, 0.0, -0.0, -0.0, -552.1105441909145, 2193.112874094114, -0.0, 370.0503349786939, -0.0, -0.0, -0.0, -0.0, -0.0, 384.7492825385162, -0.0, -0.0, 0.0, 0.0, -819.7722957127545, -0.0, 0.0, 72.38445704768583, 0.0, -0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0, -0.0, 0.0, -0.0, 154.7098997509337, -0.0, -0.0, 0.0, 5001.135030874798, 0.0, -0.0, -0.0, 0.0, -0.0, -0.0, 0.0, 0.0]    180401.21694528882
0   0.99    2214034038.866108   [1288.5108755365814, 1141.4002144343365, 2896.4098231445964, 0.0, 1437.726183039458, 1569.5515270912845, 1936.6841301002705, 1823.1086710815543, 0.0, 423.2345294724227, 2480.5476534169384, 2546.30937757155, 1070.7509478625852, -0.0, 2916.119955036174, 980.4527182573493, -37.378802962765505, 1735.7361484508306, 938.1229701846534, 496.3570106270763, -295.7527553007795, 1920.1478016912283, 1684.65644459793, 483.04213977421233, 2049.0920392831786, 2168.5834067751116, 1076.238008628459, 1058.7703167992531, -106.97246191967314, 18.28220317948517, 471.1157781234777, 305.10803219890505, -0.0, 25.894030212031456, -57.28144005355844, -0.0, -395.7046573315114, -175.91804876091754, -190.40676758531208, 91.17482633148427, -577.5845817859226, -0.0, -47.09497144947905, -238.17589197161303, 1026.282311618595, 0.0, 96.01760755796542, -134.44726519764308, -96.23577659089972, -258.99863026275324, -254.82049933253407, 138.28152785044944, -15.594574791548254, -53.419363210838, 630.7985756100181, -720.4901156275829, 138.85484964691793, 249.4203033082621, 20.713356956941293, -844.2025321573319, 722.4689151000729, 0.0, -310.2623401629524, -0.0, -32.915850514256306, 471.6124459129323, -62.11862211556542, 0.0, -132.17418703748717, 251.0320144432172, 0.0, -0.0, -194.78961750141593, -189.65400031255203, 20.85964176300863, 0.0, 349.09806278077383, -435.11189433379064, -142.87977206520654, 0.0, 182.79611576906126, -356.63579947995066, -0.0, -378.27264014515623, -128.9808913603312, -586.6177554814338, -44.200078058630645, -0.0, 1170.4500814296446, 1673.9783693722238, -448.6418089397299, -89.06494894229179, -331.7417791922637, -18.536617814051603, 326.5829823034383, 1060.562730511171, 255.0468131982831, 190.05497434530736, -299.47501805430954, 244.60190846218444, 284.5540364646699, 285.8681195735776, -150.32896927509282, -0.0, -4.972625039681385, 0.0, -57.65656657850986, -59.22279196361179, 556.4109422397493, 263.2936227316695, -0.0, -0.0, -0.0, -183.4560408780339, -258.9898426045801, -353.33368501106855, -34.100549991687394, -63.652247300182076, -94.30027635409674, 41.26781180294321, 0.0, 601.5146707800469, -208.4615238479576, -173.82150371664653, -992.1349156402378, -5.202445989743139, 1065.0290066847888, -0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 11.183040696304912, 623.7881665925077, -0.0, -0.0, 169.81050210880974, -8.99546735793611, 496.59978201632106, -346.0610215064944, 0.0, -212.21802108495342, -107.52597574093022, 67.2604487664647, 26.18127976869913, -0.1859106121510271, 689.7833802221692, -295.7595004044681, -31.828934672723047, -0.0, -13.431770612003968, 47.34684657851442, -0.0, 456.7752948253894, -295.7097467369197, 217.75663833813476, -167.0240525154184, 13.119028988342224, -190.38680651312075, 67.26089494444646, -0.0, -0.0, 684.4056604135054, -256.26977952572673, -52.023214009686555, 547.1319773359934, -55.979446689306364, -1075.392831544414, 991.7328009993199, -289.4642763067799, 1042.614795123592, -1818.0125822592026, -316.0191242640419, -0.0, -67.50860076854983, 110.97572842589076, -1042.7942642855426, 1449.6554692979535, -202.8457512185241, 0.0, -0.0, -355.6609329403728, 55.99441520091489, -317.94628256217095, -0.0, -1235.8949254076197, -268.38614492459465, 223.77798861865043, -317.94218063833654, -19.485732547608414, 143.59190337259554, 1432.4716556753574, 0.0, -981.5310761970001, -316.65366054598894, -265.5204624571671, 1440.9518123212615, -158.70992249129722, -317.93827165503893, -356.0739471006797, -372.2717195206707, -32.76230852993284, 136.57315701079378, -35.57211729261106, -307.05951186249825, -67.39523106286269, 207.5873505271385, 70.99392538698746, -0.0, -86.92648016855802, -0.0, -74.2555658664915, -311.53928831040525, -326.07741269037325, -57.42460332942894, -991.8374742057182, 626.0598772128691, -220.31626712062035, -57.58038162029573, -0.0, 517.846849748521, -310.30485997062044, 434.4311428989644, -0.0, -1623.713494776604, -147.24790042773182, -75.08697695680965, -93.20446491616461, -0.0, -118.05622510867376, -71.83673868144035, 300.0590500084468, -87.82710434512016, 1283.226314913383, -1551.4156046961957, -134.62472833566693, 229.74025226894264, 754.9018930958974, -118.97884961404972, 696.281628642635, -173.77757446902064, -899.4327344671217, -403.98364401941393, -400.8475048015838, 56.13121758676302, -1137.1913929011605, -309.31673166928437, 297.977600702344, -400.8507800725531, -38.771944949119685, 410.98676359917056, -327.6428271259632, 0.0, -400.85996582844353, -127.83828668012485, 569.3934393329992, -96.82731711388608, 604.4531534927594, -0.0, 36.379889517416466, -135.49296057984154, -0.0, -16.196866480782283, 1182.6005299476797, -0.07870072095822686, -0.0, -603.0486105754311, -44.98469441598015, -0.0, -90.17559291390286, -379.08488477737984, 1166.8318977943532]    180401.21694528876
0   0.95    4477216372.464831   [451.90379889404005, 364.42705000899014, 1000.411275893526, -70.67834247340133, 632.9642092794054, 621.5537787066967, 649.9067608748471, 580.0671499591238, 0.0, 202.34565811997464, 838.5700412425219, 835.6333063594087, 348.49657416657107, -15.435525288027897, 944.254509860495, 338.0328035262107, -25.01372649044941, 661.1395852808022, 345.20131644220334, 187.72254522333623, -119.75812611787666, 643.8362889301778, 596.5734316125265, 265.95524492651293, 781.0370773108815, 790.8493276183372, 391.4790922420105, 392.28276353035255, -107.65109614935568, 20.49781579702922, 140.84646319735447, 89.21383666299006, -5.709537584686129, 29.585311060864015, -29.73170383999819, -0.0, -137.74419480613744, -74.7864616578633, -94.4276526086043, 53.42182824135511, -260.02253156825583, -3.7458522151629796, -57.312008847900074, -170.70164218535564, 423.50265683498435, -29.819573656467355, 17.959719899823106, -35.199059516069376, -47.36055181108414, -101.65546133033381, 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0   0.9 5295530119.621321   [251.97593564024584, 199.6252818805267, 559.4157970065792, -50.2762286812302, 369.3987053762267, 356.54031154076546, 360.38951544011144, 318.91545548846716, 0.0, 118.61147271515632, 466.3076634751436, 460.9401784466648, 192.32186554762092, -12.923216647133366, 519.7287513461932, 188.34702432552217, -15.441650383291199, 374.8995459455578, 195.6475337840008, 105.03488323836403, -70.76552829179302, 355.5664401118226, 332.96647036861344, 160.2062316550571, 443.71242163288287, 446.0840468860182, 220.40033093081306, 221.4348870270198, -68.56242469622417, 13.042105928681904, 76.17869818705489, 48.122477975635725, -5.2006843500336135, 18.434156745036734, -17.689726843055425, -0.0, -76.0629392470267, -42.977960480563205, -57.194265849729874, 33.07807632159702, -151.71882426687026, -4.363050458394881, -36.360820583438716, -105.05970152372988, 244.1870859239563, -24.59362540244982, 7.931297065737346, -18.364206952553698, -27.619171604684887, -59.52129426024859, 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...

df.dtypes
Out[178]: 
alpha        float64
l1_ratio     float64
mse_path     float64
coef          object
intercept    float64
dtype: object

这会引发错误：

df.pivot(columns = 'alpha', values = ['mse_path','coef'])

..

ValueError: Index contains duplicate entries, cannot reshape

这将返回一个数据透视表，但省略由值列表组成的

coef

变量。

df.pivot_table(columns = 'alpha', values = ['mse_path','coef'])
Out[179]: 
alpha           0.1           1.0     ...        250.0         1000.0
mse_path  5.335019e+08  6.061998e+08  ...  3.950346e+09  4.962799e+09

[1 rows x 9 columns]

我怎样才能实现这个目标？

Answer 1

实现的一种方法是首先确保所有条目都适当地聚合到列表中，然后手动构建旋转的 DataFrame。这里我只有您的数据样本，我需要添加一列

alpha

....需要手动处理：

data = {
    'alpha': [1.0, 0.99, 0.95, 0.9, 0.8, 0.75],
    'mse_path': [581499561.8597653, 2214034038.866108, 4477216372.464831, 5295530119.621321, 5841959239.549489, 5965993746.00419],
    'coef': [
        [253.039, 3649.109, -598.486, 28425.471],
        [1288.510, 1141.400, -37.378, 938.122],
        [451.903, 364.427, -119.758, 496.357],
        [251.975, 199.625, -70.678, 369.398],
        [134.197, 105.349, -30.138, 201.609],
        [108.898, 85.350, -25.102, 164.381]
    ],
    'intercept': [180401.21694528882] * 6
}

df = pd.DataFrame(data)

因此，您可以这样做：

pivot_columns = pd.unique(df['alpha'])
index = ['mse_path', 'coef']
pivot_df = pd.DataFrame(columns=pd.MultiIndex.from_product([pivot_columns, index], names=['alpha', 'variable']))

for alpha in pivot_columns:
    for var in index:
        pivot_df.loc[var, (alpha, var)] = list(df[df['alpha'] == alpha][var])

for col in pivot_columns:
    pivot_df.loc['mse_path', (col, 'mse_path')] = pivot_df.loc['mse_path', (col, 'mse_path')][0]

这给出了 或按印刷形式

alpha                 1.00                                              \
variable          mse_path                                        coef   
mse_path  581499561.859765                                         NaN   
coef                   NaN  [[253.039, 3649.109, -598.486, 28425.471]]   

alpha                  0.99                                         \
variable           mse_path                                   coef   
mse_path  2214034038.866108                                    NaN   
coef                    NaN  [[1288.51, 1141.4, -37.378, 938.122]]   

alpha                  0.95                                           \
variable           mse_path                                     coef   
mse_path  4477216372.464831                                      NaN   
coef                    NaN  [[451.903, 364.427, -119.758, 496.357]]   
...
alpha                 0.75                                        
variable          mse_path                                  coef  
mse_path  5965993746.00419                                   NaN  
coef                   NaN  [[108.898, 85.35, -25.102, 164.381]]