如果这是一个奇怪的问题，我很抱歉，但我正在尝试处理来自世界银行的一些数据，它的格式很奇怪。
这是数据：
!pip install wbgapi
import wbgapi as wb
import pandas as pd
df = wb.data.DataFrame(['EN.ATM.CO2E.KT'])
df
YR1960 YR1961 YR1962 YR1963 YR1964 YR1965 YR1966 YR1967 YR1968 YR1969 YR1970 YR1971 YR1972 YR1973 YR1974 YR1975 YR1976 YR1977 YR1978 YR1979 YR1980 YR1981 YR1982 YR1983 YR1984 YR1985 YR1986 YR1987 YR1988 YR1989 YR1990 YR1991 YR1992 YR1993 YR1994 YR1995 YR1996 YR1997 YR1998 YR1999 YR2000 YR2001 YR2002 YR2003 YR2004 YR2005 YR2006 YR2007 YR2008 YR2009 YR2010 YR2011 YR2012 YR2013 YR2014 YR2015 YR2016 YR2017 YR2018 YR2019 YR2020
economy
ABW 11092.675000 11576.71900 12713.489000 12178.10700 11840.743000 10623.299000 9933.903000 12236.77900 11378.701000 14891.68700 16655.514000 14495.651000 14055.611000 15592.084000 14132.618000 10234.59700 21862.654000 11419.038000 9724.884000 10201.594000 10498.62100 9999.909000 11180.683000 5746.189000 14348.971000 16794.860000 179.683000 447.374000 612.389000 649.05900 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
AFE 118545.901306 123758.90333 128093.897815 132810.33253 144345.352398 155803.780096 157932.257312 165066.04049 174004.892685 182939.98432 191243.301798 213550.946401 217837.985316 223290.233235 226298.370019 231987.50871 240665.983329 246129.511761 250630.436423 268619.094385 280929.89417 308901.806081 329541.384584 344021.803024 365909.758291 374516.789662 385101.670967 388106.047579 404605.677834 401747.94789 309980.825443 304407.723573 300329.971872 306574.577075 312677.028664 330892.998964 341412.366429 357963.108675 366634.479152 352429.271543 358460.646325 397075.660289 409057.179216 434533.137679 466883.238781 468365.697902 476590.299877 500147.184546 533634.514563 515682.543832 543808.651821 535604.357505 559333.857277 580510.924989 601860.163983 586385.004029 592299.593959 601323.394691 600351.133333 NaN NaN
AFG 414.371000 491.37800 689.396000 707.73100 839.743000 1008.425000 1092.766000 1283.45000 1224.778000 942.41900 1672.152000 1895.839000 1532.806000 1639.149000 1917.841000 2126.86000 1987.514000 2390.884000 2159.863000 2240.537000 1760.16000 1983.847000 2101.191000 2522.896000 2830.924000 3509.319000 3142.619000 3124.284000 2867.594000 2775.91900 2960.000000 2740.000000 1430.000000 1360.000000 1300.000000 1250.000000 1180.000000 1100.000000 1050.000000 820.000000 770.000000 810.000000 1100.000000 1350.000000 1130.000000 1640.000000 1940.000000 2360.000000 4390.000000 6000.000000 8670.000000 12260.000000 10450.000000 8510.000000 7810.000000 7990.000000 7390.000000 7380.000000 7440.000000 NaN NaN
AFW 8760.463000 9376.51900 9710.216000 11540.04900 13985.938000 19827.469000 21246.598000 21239.26400 16527.169000 23743.82500 36398.642000 48301.724000 58059.611000 69676.667000 84392.338000 69995.69600 76900.657000 74154.074000 75045.155000 98319.604000 98913.65800 96889.474000 98755.977000 91337.636000 101154.195000 104344.485000 99977.088000 89694.820000 103820.104000 82562.50500 90210.000000 99880.000000 111590.000000 115080.000000 112220.000000 113980.000000 121890.000000 120450.000000 116930.000000 118000.000000 119510.000000 130120.000000 133840.000000 137380.000000 141040.000000 155650.000000 153210.000000 153350.000000 157600.000000 145550.000000 165750.000000 179070.000000 181740.000000 191990.000000 198440.000000 193060.000000 195120.000000 201900.000000 224380.000000 NaN NaN
AGO 550.050000 454.70800 1180.774000 1151.43800 1224.778000 1188.108000 1554.808000 993.75700 1672.152000 2786.92000 3582.659000 3410.310000 4506.743000 4880.777000 4873.443000 4415.06800 3285.632000 3534.988000 5412.492000 5504.167000 5346.48600 5280.480000 4649.756000 5115.465000 5009.122000 4701.094000 4660.757000 5815.862000 5130.133000 5009.12200 6330.000000 6530.000000 6370.000000 6900.000000 6690.000000 10780.000000 11410.000000 11730.000000 11370.000000 12650.000000 12370.000000 12910.000000 13620.000000 18010.000000 19290.000000 18210.000000 20340.000000 23490.000000 25080.000000 27150.000000 28530.000000 29460.000000 30250.000000 32820.000000 34630.000000 35160.000000 35410.000000 30840.000000 27340.000000 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
XKX 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 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
YEM 58.672000 73.34000 69.673000 80.67400 99.009000 102.676000 99.009000 102.67600 128.345000 135.67900 168.682000 216.353000 253.023000 326.363000 370.367000 605.05500 729.733000 865.412000 949.753000 1045.095000 1195.44200 1360.457000 1958.178000 2489.893000 2948.268000 3157.287000 3153.620000 3355.305000 3406.643000 3498.31800 6640.000000 8500.000000 9130.000000 8120.000000 8480.000000 9870.000000 10080.000000 10850.000000 11730.000000 13170.000000 13890.000000 15080.000000 15140.000000 17400.000000 18400.000000 19380.000000 19410.000000 20630.000000 22010.000000 25650.000000 23990.000000 20690.000000 19680.000000 26350.000000 26710.000000 14210.000000 10880.000000 10060.000000 9310.000000 NaN NaN
ZAF 97934.569000 102213.95800 105767.281000 109826.65000 119657.877000 128260.659000 128356.001000 133885.83700 138084.552000 143280.69100 149763.947000 168568.323000 171725.610000 173533.441000 176734.732000 185201.83500 193115.221000 199950.509000 202099.371000 218908.899000 228454.10000 257368.395000 280749.187000 292230.564000 315948.720000 324214.138000 330855.075000 329025.242000 343055.184000 341108.00700 247660.000000 242330.000000 238820.000000 246430.000000 252010.000000 264190.000000 273980.000000 288920.000000 296510.000000 278470.000000 284730.000000 320600.000000 331420.000000 353110.000000 379970.000000 377640.000000 379560.000000 396900.000000 426560.000000 404020.000000 425110.000000 409120.000000 426710.000000 436870.000000 447980.000000 424880.000000 425180.000000 435140.000000 433250.000000 NaN NaN
ZMB NaN NaN NaN NaN 3278.298000 3916.356000 3501.985000 4792.76900 4572.749000 4275.72200 3769.676000 3791.678000 4066.703000 4591.084000 4202.382000 4081.37100 4026.366000 3744.007000 3476.316000 3604.661000 3531.32100 3366.306000 3520.320000 3270.964000 2819.923000 2753.917000 2889.596000 2702.579000 3142.619000 2603.57000 2740.000000 2880.000000 2850.000000 2520.000000 2140.000000 2130.000000 1760.000000 2280.000000 2170.000000 1760.000000 1810.000000 1830.000000 1920.000000 2090.000000 2110.000000 2290.000000 2180.000000 1980.000000 2180.000000 2470.000000 2640.000000 3060.000000 4020.000000 4240.000000 4800.000000 5070.000000 5590.000000 6990.000000 7740.000000 NaN NaN
ZWE NaN NaN NaN NaN 4473.740000 5214.474000 6046.883000 5298.81500 6384.247000 6750.94700 8162.742000 8742.128000 8225.081000 9281.177000 9057.490000 8320.42300 10868.988000 9299.512000 9295.845000 9449.859000 9636.87600 9435.191000 8811.801000 10461.951000 9922.902000 10263.933000 13127.860000 15240.052000 16101.797000 16186.13800 16530.000000 18300.000000 18470.000000 17080.000000 15960.000000 15480.000000 14860.000000 13830.000000 14070.000000 15820.000000 13700.000000 13900.000000 12490.000000 10180.000000 9770.000000 10510.000000 9830.000000 9760.000000 7600.000000 7750.000000 9810.000000 11490.000000 12110.000000 12360.000000 12150.000000 12400.000000 10990.000000 10230.000000 12270.000000 NaN NaN
266 rows × 61 columns
但不是每一列都是年份，我希望数据格式如下：经济，年份，价值
我想我需要调整表格，但我不知道如何调整才能使年份和经济与价值保持一致。
欢迎任何建议。