确切的代码对于您要执行的每个列都会有所不同，但是您可能会想使用map和apply函数.在某些情况下，您可以直接使用现有的列进行计算，因为这些列是Pandas Series对象，它们也可以作为Numpy数组使用，对于常规的数学运算，它们会自动以元素方式工作.

The exact code will vary for each of the columns you want to do, but it's likely you'll want to use the map and apply functions. In some cases you can just compute using the existing columns directly, since the columns are Pandas Series objects, which also work as Numpy arrays, which automatically work element-wise for usual mathematical operations.

>>> d
    A   B  C
0  11  13  5
1   6   7  4
2   8   3  6
3   4   8  7
4   0   1  7
>>> (d.A + d.B) / d.C
0    4.800000
1    3.250000
2    1.833333
3    1.714286
4    0.142857
>>> d.A > d.C
0     True
1     True
2     True
3    False
4    False

如果需要在一行中使用max和min之类的运算，则可以将apply与axis=1结合使用，以将所需的任何函数应用于每一行.这是一个计算min(A, B)-C的示例，它看起来像是您的下部灯芯":

If you need to use operations like max and min within a row, you can use apply with axis=1 to apply any function you like to each row. Here's an example that computes min(A, B)-C, which seems to be like your "lower wick":

>>> d.apply(lambda row: min([row['A'], row['B']])-row['C'], axis=1)
0    6
1    2
2   -3
3   -3
4   -7

希望这会让您对如何进行操作有所了解.

Hopefully that gives you some idea of how to proceed.

将行与相邻行进行比较，最简单的方法是对要比较的列进行切片，保留开始/结束的位置，然后比较所得切片.例如，这将告诉您A列中的元素的哪些行小于C列中下一行的元素:

to compare rows against neighboring rows, the simplest approach is to slice the columns you want to compare, leaving off the beginning/end, and then compare the resulting slices. For instance, this will tell you for which rows the element in column A is less than the next row's element in column C:

d['A'][:-1] < d['C'][1:]

这是另一种方法，告诉您哪些行的A小于上一行的C:

and this does it the other way, telling you which rows have A less than the preceding row's C:

d['A'][1:] < d['C'][:-1]

将['A"][:-1]切成A列的最后一个元素，而将['C'][1:]切成C列的第一个元素，因此当您将这两个元素排成一行并进行比较时，您正在将A中的每个元素与下一行的C.

Doing ['A"][:-1] slices off the last element of column A, and doing ['C'][1:] slices off the first element of column C, so when you line these two up and compare them, you're comparing each element in A with the C from the following row.