pandas 每行应用多列而不是列表 [英] Pandas apply multiple columns per row instead of list

问题描述

我在使用 apply 时无法让 Pandas 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 有两列，每行每列有一个元素，而不是每行有两个元素的一列.

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

有人知道如何解决这个问题吗?

Does anybody know how to fix this?

推荐答案

要实现这一点，需要进行一些更改:

Couple of changes are required to achieve this:

更新如下功能

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

并对df3

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

print(wantedDF3) 给出想要的输出:

 col1  col2
0    40    12
0    97    33

另一种做同样事情的方法，以避免内存错误问题:保持您的 example 函数和 df3 原样(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)

这篇关于 pandas 每行应用多列而不是列表的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！