Python Pandas，应用函数 [英] Python Pandas, apply function

问题描述

我正在尝试使用 apply 来避免函数中的 iterrows() 迭代器:

I am trying to use apply to avoid an iterrows() iterator in a function:

但是，pandas 方法的文档很差，我找不到有关如何使用它的示例，除了文档中的 .apply(sq.rt)... 没有示例如何使用参数等...

However that pandas method is poorly documented and I can't find example on how to use it, except for the lame .apply(sq.rt) in the documentation... No example on how to use arguments etc...

无论如何，这是一个关于我尝试做的事情的玩具示例.

Anyway, here a toy example on what I try to do.

根据我的理解，apply 实际上与 iterrows() 的作用相同，即迭代(如果轴 = 0，则遍历行).在每次迭代中，函数的输入 x 应该是迭代过的行.然而，我不断收到的错误消息反驳了这个假设......

In my understanding apply will actually do the same as iterrows(), ie, iterate (over the rows if axis=0). On each iteration the input x of the function should be the row iterated over. However the error messages I keep receiving sort of disprove that assumption...

grid = np.random.rand(5,2)
df = pd.DataFrame(grid)

def multiply(x):
    x[3]=x[0]*x[1]

df = df.apply(multiply, axis=0)

上面的例子返回一个空的 df.谁能解释一下我的误解?

The example above returns an empty df. Can anyone shed some light on my misunderstanding?

推荐答案

import pandas as pd
import numpy as np

grid = np.random.rand(5,2)
df = pd.DataFrame(grid)

def multiply(x):
    return x[0]*x[1]

df['multiply'] = df.apply(multiply, axis = 1)
print(df)

结果:

          0         1  multiply
0  0.550750  0.713054  0.392715
1  0.061949  0.661614  0.040987
2  0.472134  0.783479  0.369907
3  0.827371  0.277591  0.229670
4  0.961102  0.137510  0.132162

<小时>

说明:

您正在应用的函数需要返回一个值.您还将它应用于每一行，而不是列.轴 您传递的参数在这方面不正确.

The function you are applying, needs to return a value. You are also applying this to each row, not column. The axis parameter you passed was incorrect in this regard.

最后，请注意我将其设置为等于函数外的 'multiply' 列.您可以轻松地将其更改为 df[3] = ... ，就像您拥有的那样并获得这样的数据框:

Finally, notice that I am setting this equal to the 'multiply' column outside of my function. You can easily change this to be df[3] = ... like you have and get a dataframe like this:

          0         1         3
0  0.550750  0.713054  0.392715
1  0.061949  0.661614  0.040987
2  0.472134  0.783479  0.369907
3  0.827371  0.277591  0.229670
4  0.961102  0.137510  0.132162

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