Python中pandas函数应用！

Pandas 可直接使用 NumPy 的 ufunc（元素级数组方法） 函数：

>>> 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：

>>> 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：

>>> 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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