类型错误:应用自定义函数时插入列的索引与框架索引不兼容 [英] TypeError: incompatible index of inserted column with frame index when applying a custom function

问题描述

我想对数据框的组应用一个函数，并将函数输出作为一个新列.

这是我写的函数:

def get_centroids(sample):# 理想情况下，re = complex_function(sample) 返回与样本长度相同的一维数组# 为简单起见，我们使用 np.random.rand(len(sample))re = pd.DataFrame({'B': np.random.rand(len(sample))})打印(重新)打印(重新索引)返回

函数打印，

 B0 0.1760831 0.984371范围索引(开始=0，停止=2，步骤=1)

让我们看看这个数据框.为简单起见，它只有一组a".

df = pd.DataFrame({'A': 'a a'.split(),'B': [1,43],'C': [4,2]})乙丙0 1 41 43​​ 2打印(df.index)范围索引(开始=0，停止=2，步骤=1)

当我应用如下函数时，

df['test'] = df.groupby('A')[['B']].apply(get_centroids)

它抛出类型错误:插入列的索引与帧索引不兼容"；尽管 df 和 re 具有相似类型的索引.任何帮助将不胜感激.

解决方案

当我在考虑这些建议时，我意识到 df.groupby('A')[['B']].apply(get_centroids) 单独工作正常，分配导致错误.

换句话说，df 的接收效果不佳df.groupby('A')[['B']].apply(get_centroids).然后我决定检查 df.groupby('A')[['B']].apply(get_centroids).index 这是

MultiIndex([('a', 0),('a', 1)],名称=['A'，无])

df 的索引是 RangeIndex(start=0, stop=2, step=1).因此，RangeIndex 与 MultiIndex 不匹配导致了问题.

这可以通过如下重置和设置df.groupby('A')[['B']].apply(get_centroids)的索引来解决.

df['test'] = df.groupby('A')[['B']].apply(get_centroids).reset_index().set_index('level_1').drop('A',轴=1)

此处https://stackoverflow.com/a/66709059/8561039提出了相同的解决方案.>

I want to apply a function on groups of a data frame and get the function output as a new column.

Here is the function that I wrote:

def get_centroids(sample):
    
    # Ideally, re = complex_function(sample) that returns 1d array which has the same length as sample
    # for simplicity let's use np.random.rand(len(sample))

    re = pd.DataFrame({'B': np.random.rand(len(sample))})
    print(re)
    print(re.index)  
    return re

The function prints,

   B
0  0.176083
1  0.984371

RangeIndex(start=0, stop=2, step=1)

Let's look at this data frame. For simplicity, it has only one group 'a'.

df = pd.DataFrame({'A': 'a a'.split(),
                   'B': [1,43],
                   'C': [4,2]})

    A   B   C
0   a   1   4
1   a   43  2

print(df.index)
RangeIndex(start=0, stop=2, step=1)

When I apply the function as below,

df['test'] = df.groupby('A')[['B']].apply(get_centroids)

it throws "TypeError: incompatible index of inserted column with frame index" though df and re has the similar type of indexes. Any help would be appreciated.

解决方案

While I was playing around with the suggestions, I realised that df.groupby('A')[['B']].apply(get_centroids) alone works fine, and the assignment causes the error.

In other words, df does not receive well df.groupby('A')[['B']].apply(get_centroids). I then decided to check for df.groupby('A')[['B']].apply(get_centroids).index which is

MultiIndex([('a', 0),
            ('a', 1)],
           names=['A', None])

The index of df was RangeIndex(start=0, stop=2, step=1). Therefore, RangeIndex vs MultiIndex mismatach caused the issue.

This can be solved by resetting and setting the index of df.groupby('A')[['B']].apply(get_centroids) as below.

df['test'] = df.groupby('A')[['B']].apply(get_centroids).reset_index().set_index('level_1').drop('A',axis=1)

The same solution has been proposed here https://stackoverflow.com/a/66709059/8561039.

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