访问同一 DataFrame 列中的先前值

这是我的代码：

a = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], columns=['A', 'B'])

print(a)

a['C'] = 1 # or np.nan or is there a way to avoid this?

b = lambda i : i['A'] + i['B'] + i['C'] # actually what is needed if to access a previous element, like i['C'].shift()

a['C'] = a.apply(b, axis=1)

print(a)

效果很好，但在 lambda 中，我想访问i['C'].shift(1)但如果这样使用它，我会得到以下异常；

Traceback (most recent call last):
  File "C:\Users\Development\workspace\TestPython\TestPython.py", line 31, in <module>
    a['C'] = a.apply(b, axis=1)
  File "C:\Program Files\Python36\lib\site-packages\pandas\core\frame.py", line 4262, in apply
    ignore_failures=ignore_failures)
  File "C:\Program Files\Python36\lib\site-packages\pandas\core\frame.py", line 4358, in _apply_standard
    results[i] = func(v)
  File "C:\Users\Development\workspace\TestPython\TestPython.py", line 29, in <lambda>
    b = lambda i : i['A'] + i['B'] + i['C'].shift() # actually what is needed if to access a previous element, like i['C'].shift()
AttributeError: ("'numpy.int64' object has no attribute 'shift'", 'occurred at index 0')

而且我想避免初始化a['C'] = 1，如果可能的话，这意味着 a['C'] 是在此操作中添加的新列。

有什么建议或替代方法来实现这一目标吗？

我想你需要这个：

a['C'] = a['A'] + a['B']
a['D'] = a['C'].cumsum()

因为与前一个元素相加是累积和。