Pandas 可直接使用 NumPy 的 ufunc（元素级数组方法） 函数：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | >>> import pandas as pd
>>> import numpy as np
>>> obj = pd.DataFrame(np.random.randn(5,4) - 1)
>>> obj
0 1 2 3
0 -0.228107 1.377709 -1.096528 -2.051001
1 -2.477144 -0.500013 -0.040695 -0.267452
2 -0.485999 -1.232930 -0.390701 -1.947984
3 -0.839161 -0.702802 -1.756359 -1.873149
4 0.853121 -1.540105 0.621614 -0.583360
>>>
>>> np.abs(obj)
0 1 2 3
0 0.228107 1.377709 1.096528 2.051001
1 2.477144 0.500013 0.040695 0.267452
2 0.485999 1.232930 0.390701 1.947984
3 0.839161 0.702802 1.756359 1.873149
4 0.853121 1.540105 0.621614 0.583360
|
函数映射：在 Pandas 中 apply 方法可以将函数应用到列或行上，可以通过设置 axis 参数来指定行或列，默认 axis = 0，即按列映射：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | >>> import pandas as pd
>>> import numpy as np
>>> obj = pd.DataFrame(np.random.randn(5,4) - 1)
>>> obj
0 1 2 3
0 -0.707028 -0.755552 -2.196480 -0.529676
1 -0.772668 0.127485 -2.015699 -0.283654
2 0.248200 -1.940189 -1.068028 -1.751737
3 -0.872904 -0.465371 -1.327951 -2.883160
4 -0.092664 0.258351 -1.010747 -2.313039
>>>
>>> obj.apply(lambda x : x.max())
0 0.248200
1 0.258351
2 -1.010747
3 -0.283654
dtype: float64
>>>
>>> obj.apply(lambda x : x.max(), axis=1)
0 -0.529676
1 0.127485
2 0.248200
3 -0.465371
4 0.258351
dtype: float64
|
另外还可以通过 applymap 将函数映射到每个数据上：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | >>> import pandas as pd
>>> import numpy as np
>>> obj = pd.DataFrame(np.random.randn(5,4) - 1)
>>> obj
0 1 2 3
0 -0.772463 -1.597008 -3.196100 -1.948486
1 -1.765108 -1.646421 -0.687175 -0.401782
2 0.275699 -3.115184 -1.429063 -1.075610
3 -0.251734 -0.448399 -3.077677 -0.294674
4 -1.495896 -1.689729 -0.560376 -1.808794
>>>
>>> obj.applymap(lambda x : '%.2f' % x)
0 1 2 3
0 -0.77 -1.60 -3.20 -1.95
1 -1.77 -1.65 -0.69 -0.40
2 0.28 -3.12 -1.43 -1.08
3 -0.25 -0.45 -3.08 -0.29
4 -1.50 -1.69 -0.56 -1.81
|
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