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主要使用to_numpy()

df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))                   A         B         C         D2013-01-01 -0.795050 -1.462931 -0.432862 -0.6215602013-01-02  0.831265 -1.166442 -0.783398 -0.6201672013-01-03 -0.801782 -0.775516  1.727844 -0.4587552013-01-04  0.383464  0.599018 -2.379950 -0.6501312013-01-05 -0.475707 -0.584573 -0.933035  1.5180112013-01-06  0.899921  1.430410 -0.686297 -0.565737df['A']2013-01-01   -0.6398552013-01-02    0.1898212013-01-03    1.3002502013-01-04   -1.8620902013-01-05   -0.2866242013-01-06   -0.601911Freq: D, Name: A, dtype: float64df['A'].to_numpy()[ 2.04318817 -0.52849963 -0.10039199  1.4426938   0.43951097 -0.76876768]df.to_numpy()[[-0.28396728 -1.46323108 -0.08070165  0.39642475] [-0.5914242  -0.53852397 -0.76549641  2.33995447] [-0.14468508 -1.12439976  0.67002192  0.67414771] [-0.18639919  0.45253985  0.31629746  1.60807975] [-1.52408677  1.29007297  0.8092461   0.94449666] [-0.65013327  0.21754823 -0.136869    0.00970518]]

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