Pandas DataFrame 应用
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Pandas DataFrame Apply
提问by Freddie Witherden
I have a Pandas DataFramewith four columns, A, B, C, D. It turns out that, sometimes, the values of Band Ccan be 0. I therefore wish to obtain the following:
我有一个熊猫DataFrame四列，A, B, C, D。事实证明，有时，值B及C能0。因此，我希望获得以下信息：
B[i] = B[i] if B[i] else min(A[i], D[i])
C[i] = C[i] if C[i] else max(A[i], D[i])
where I have used ito indicate a run over all rows of the frame. With Pandas it is easy to find the rows which contain zero columns:
我曾经用来i表示在框架的所有行上运行。使用 Pandas 很容易找到包含零列的行：
df[df.B == 0] and df[df.C == 0]
however I have no idea how to easily perform the above transformation. I can think of various inefficient and inelegant methods (forloops over the entire frame) but nothing simple.
但是我不知道如何轻松执行上述转换。我可以想到各种低效和不优雅的方法（for在整个框架上循环），但没有什么简单的。
回答by Wouter Overmeire
A combination of boolean indexing and apply can do the trick. Below an example on replacing zero element for column C.
布尔索引和应用的组合可以解决问题。下面是替换 C 列的零元素的示例。
In [22]: df
Out[22]:
A B C D
0 8 3 5 8
1 9 4 0 4
2 5 4 3 8
3 4 8 5 1
In [23]: bi = df.C==0
In [24]: df.ix[bi, 'C'] = df[bi][['A', 'D']].apply(max, axis=1)
In [25]: df
Out[25]:
A B C D
0 8 3 5 8
1 9 4 9 4
2 5 4 3 8
3 4 8 5 1
回答by THM
Try 'iterrows' DataFrame class method for efficiently iterating through the rows of a DataFrame.See chapter 6.7.2 of the pandas 0.8.1 guide.
尝试使用“iterrows”DataFrame 类方法来有效地遍历 DataFrame 的行。请参阅 pandas 0.8.1 指南的第 6.7.2 章。
from pandas import *
import numpy as np
df = DataFrame({'A' : [5,6,3], 'B' : [0,0,0], 'C':[0,0,0], 'D' : [3,4,5]})
for idx, row in df.iterrows():
if row['B'] == 0:
row['B'] = min(row['A'], row['D'])
if row['C'] == 0:
row['C'] = min(row['A'], row['D'])
