Python 使用 lambda 来应用 pd.DataFrame 而不是嵌套循环有可能吗？
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Python using lambda to apply pd.DataFrame instead for nested loop is it possible?
提问by JPC
I'm trying to avoid nested loop in python here by using lambda apply to create a new column using this argument below :
我试图通过使用 lambda apply 使用下面的这个参数创建一个新列来避免 python 中的嵌套循环：
from pandas import *
import pandas as pd
df = pd.DataFrame((np.random.rand(100, 4)*100), columns=list('ABCD'))
df['C'] = df.apply(lambda A,B: A+B)
TypeError: ('() takes exactly 2 arguments (1 given)', u'occurred at index A')
类型错误: ('() 正好需要 2 个参数 (1 给定)', u'occurred at index A')
Obviously this doesn't work any recommendation ?
显然这没有任何建议？
采纳答案by miku
Do you want to add column Aand column Band store the result in C? Then you can have it simpler:
是否要添加列A和列B并将结果存储在C? 然后你可以让它更简单：
df.C = df.A + df.B
As @EdChum points out in the comment, the argument to the function in applyis a series, by default on axis 0which are rows (axis 1means columns):
正如@EdChum 在评论中指出的那样，函数 in 的参数apply是一个系列，默认情况下在轴上0是行（轴1表示列）：
>>> df.apply(lambda s: s)[:3]
A B C D
0 57.890858 72.344298 16.348960 84.109071
1 85.534617 53.067682 95.212719 36.677814
2 23.202907 3.788458 66.717430 1.466331
Here, we add the first and the second row:
在这里，我们添加第一行和第二行：
>>> df.apply(lambda s: s[0] + s[1])
A 143.425475
B 125.411981
C 111.561680
D 120.786886
dtype: float64
To work on columns, use axis=1keyword parameter:
要处理列，请使用axis=1关键字参数：
>>> df.apply(lambda s: s[0] + s[1], axis=1)
0 130.235156
1 138.602299
2 26.991364
3 143.229523
...
98 152.640811
99 90.266934
Which yield the same result as referring to the columns by name:
产生与按名称引用列相同的结果：
>>> (df.apply(lambda s: s[0] + s[1], axis=1) ==
df.apply(lambda s: s['A'] + s['B'], axis=1))
0 True
1 True
2 True
3 True
...
98 True
99 True
