Python 熊猫适用，但仅适用于满足条件的行
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Pandas apply but only for rows where a condition is met
提问by mgoldwasser
I would like to use Pandas df.applybut only for certain rows
我想使用 Pandasdf.apply但仅适用于某些行
As an example, I want to do something like this, but my actual issue is a little more complicated:
举个例子，我想做这样的事情，但我的实际问题有点复杂：
import pandas as pd
import math
z = pd.DataFrame({'a':[4.0,5.0,6.0,7.0,8.0],'b':[6.0,0,5.0,0,1.0]})
z.where(z['b'] != 0, z['a'] / z['b'].apply(lambda l: math.log(l)), 0)
What I want in this example is the value in 'a' divided by the log of the value in 'b' for each row, and for rows where 'b' is 0, I simply want to return 0.
在这个例子中，我想要的是 'a' 中的值除以每行 'b' 中值的对数，对于 'b' 为 0 的行，我只想返回 0。
采纳答案by jakevdp
The other answers are excellent, but I thought I'd add one other approach that can be faster in some circumstances – using broadcasting and masking to achieve the same result:
其他答案非常好，但我想我会添加另一种在某些情况下可以更快的方法 - 使用广播和屏蔽来实现相同的结果：
import numpy as np
mask = (z['b'] != 0)
z_valid = z[mask]
z['c'] = 0
z.loc[mask, 'c'] = z_valid['a'] / np.log(z_valid['b'])
Especially with very large dataframes, this approach will generally be faster than solutions based on apply().
特别是对于非常大的数据帧，这种方法通常比基于apply().
回答by Liam Foley
You can just use an if statement in a lambda function.
您可以只在 lambda 函数中使用 if 语句。
z['c'] = z.apply(lambda row: 0 if row['b'] in (0,1) else row['a'] / math.log(row['b']), axis=1)
I also excluded 1, because log(1) is zero.
我也排除了 1，因为 log(1) 为零。
Output:
输出：
a b c
0 4 6 2.232443
1 5 0 0.000000
2 6 5 3.728010
3 7 0 0.000000
4 8 1 0.000000
回答by bananafish
You can use a lambda with a conditional to return 0 if the input value is 0 and skip the whole whereclause:
如果输入值为 0，您可以使用带条件的 lambda 返回 0 并跳过整个where子句：
z['c'] = z.apply(lambda x: math.log(x.b) if x.b > 0 else 0, axis=1)
You also have to assign the results to a new column (z['c']).
您还必须将结果分配给新列 ( z['c'])。
回答by maswadkar
Hope this helps. It is easy and readable
希望这可以帮助。它简单易读
df['c']=df['b'].apply(lambda x: 0 if x ==0 else math.log(x))
