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MYYA
一种简单的方法是用列表重新分配数据帧,并根据需要重新排列。这就是你现在所拥有的:In [6]: dfOut[6]: 0 1 2 3 4 mean0 0.445598 0.173835 0.343415 0.682252 0.582616 0.4455431 0.881592 0.696942 0.702232 0.696724 0.373551 0.6702082 0.662527 0.955193 0.131016 0.609548 0.804694 0.6325963 0.260919 0.783467 0.593433 0.033426 0.512019 0.4366534 0.131842 0.799367 0.182828 0.683330 0.019485 0.3633715 0.498784 0.873495 0.383811 0.699289 0.480447 0.5871656 0.388771 0.395757 0.745237 0.628406 0.784473 0.5885297 0.147986 0.459451 0.310961 0.706435 0.100914 0.3451498 0.394947 0.863494 0.585030 0.565944 0.356561 0.5531959 0.689260 0.865243 0.136481 0.386582 0.730399 0.561593In [7]: cols = df.columns.tolist()In [8]: colsOut[8]: [0L, 1L, 2L, 3L, 4L, 'mean']重排cols以任何你想要的方式。下面是我将最后一个元素移到第一个位置的方式:In [12]: cols = cols[-1:] + cols[:-1]In [13]: colsOut[13]: ['mean', 0L, 1L, 2L, 3L, 4L]然后像这样重新排序数据:In [16]: df = df[cols] # OR df = df.ix[:, cols]In [17]: dfOut[17]: mean 0 1 2 3 40 0.445543 0.445598 0.173835 0.343415 0.682252 0.5826161 0.670208 0.881592 0.696942 0.702232 0.696724 0.3735512 0.632596 0.662527 0.955193 0.131016 0.609548 0.8046943 0.436653 0.260919 0.783467 0.593433 0.033426 0.5120194 0.363371 0.131842 0.799367 0.182828 0.683330 0.0194855 0.587165 0.498784 0.873495 0.383811 0.699289 0.4804476 0.588529 0.388771 0.395757 0.745237 0.628406 0.7844737 0.345149 0.147986 0.459451 0.310961 0.706435 0.1009148 0.553195 0.394947 0.863494 0.585030 0.565944 0.3565619 0.561593 0.689260 0.865243 0.136481 0.386582 0.730399
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慕斯709654
你也可以这样做:df = df[['mean', '0', '1', '2', '3']]您可以通过以下方法获得列的列表:cols = list(df.columns.values)产出将产生:['0', '1', '2', '3', 'mean'].然后很容易在将其放入第一个函数之前手动重新排列。
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红糖糍粑
只需按所需的顺序分配列名:In [39]: dfOut[39]: 0 1 2 3 4 mean0 0.172742 0.915661 0.043387 0.712833 0.190717 11 0.128186 0.424771 0.590779 0.771080 0.617472 12 0.125709 0.085894 0.989798 0.829491 0.155563 13 0.742578 0.104061 0.299708 0.616751 0.951802 14 0.721118 0.528156 0.421360 0.105886 0.322311 15 0.900878 0.082047 0.224656 0.195162 0.736652 16 0.897832 0.558108 0.318016 0.586563 0.507564 17 0.027178 0.375183 0.930248 0.921786 0.337060 18 0.763028 0.182905 0.931756 0.110675 0.423398 19 0.848996 0.310562 0.140873 0.304561 0.417808 1In [40]: df = df[['mean', 4,3,2,1]]现在,“卑鄙”列出现在前面:In [41]: dfOut[41]: mean 4 3 2 10 1 0.190717 0.712833 0.043387 0.9156611 1 0.617472 0.771080 0.590779 0.4247712 1 0.155563 0.829491 0.989798 0.0858943 1 0.951802 0.616751 0.299708 0.1040614 1 0.322311 0.105886 0.421360 0.5281565 1 0.736652 0.195162 0.224656 0.0820476 1 0.507564 0.586563 0.318016 0.5581087 1 0.337060 0.921786 0.930248 0.3751838 1 0.423398 0.110675 0.931756 0.1829059 1 0.417808 0.304561 0.140873 0.310562