pandas 每行应用多列而不是列表

问题描述

使用apply时，我很难让熊猫返回多列.

I have trouble making pandas returning multiple columns when using apply.

示例:

import pandas as pd
import numpy as np
np.random.seed(1)

df = pd.DataFrame(index=range(2), columns=['a', 'b'])
df.loc[0] = [np.array((1,2,3))], 1
df.loc[1] = [np.array((4,5,6))], 1
df

             a  b
0  [[1, 2, 3]]  1
1  [[4, 5, 6]]  1

df2 = np.random.randint(1,9, size=(3,2))
df2

array([[4, 6],
       [8, 1],
       [1, 2]])

def example(x):
    return np.transpose(df2) @ x[0]

df3 = df['a'].apply(example)
df3

0    [23, 14]
1    [62, 41]

我希望df3具有两列，每行每列中每个元素一个元素，而不是一列，每行中两个元素都有一个元素.

I want df3 to have two columns with one element in each per column per row, not one column with both elements per row.

所以我想要类似的东西

df3Wanted
         col1  col2
    0    23    14
    1    62    41

有人知道如何解决此问题吗?

Does anybody know how to fix this?

推荐答案

要实现此目标，需要进行更改的夫妇:

Couple of changes are required to achieve this:

更新以下功能如下

def example(x):
    return [np.transpose(df2) @ x[0]]

并在df3

wantedDF3 = pd.concat(df3.apply(pd.DataFrame, columns=['col1','col2']).tolist())

print(wantedDF3)提供所需的输出:

 col1  col2
0    40    12
0    97    33

避免发生内存错误问题的另一种方法来做同样的事情:保持example函数和df3不变(与问题相同)现在，最重要的是，使用下面的代码生成wantedDF3

Another way to do the same thing, to avoid memory error issues:Keep your example function and df3 as it is (same as question)Now, just on top of that, use below code to generate wantedDF3

col1df = pd.DataFrame(df3.apply(lambda x: x[0]).values, columns=['col1'])
col2df = pd.DataFrame(df3.apply(lambda x: x[1]).values,  columns=['col2'])
wantedDF3 = col1df.join(col2df)

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