Python 从数据帧列表中的字典中提取元素

假设我们有一个具有以下格式的数据帧：

col1
[{'overall_prop': '0.812'}, {'overall_prop': '0.125'}, {'overall_prop': '0.062'}]
{}

原始数据是json格式的。我想从每行列表的第一个元素中提取

'totall_prop'

的值，下面是我试图提取第一个元素的内容：

 df['col1'].str[0]

一切正常，然后提取以下内容以提取

“总体道具”

：

df['col1'].str[0].map(lambda x: x.get('overall_prop'))    

但他抱怨说：

{AttributeError}'float' object has no attribute 'get'

因为

{}

（python dict对象）变成了

nan

然后我试了一下：

df['col1'].where(df['col1'].notna(), lambda x: [{}]).str[0].map(lambda x: x.get('overall_prop'))

但这次：

{TypeError}argument of type 'NoneType' is not iterable

总之，我正在寻找一种解决方案，从列表中的字典中提取可以处理空值的元素。

编辑版本1:col1是dict的列表，x[0]具有全局属性 你可以这样做。使用

df.col1.apply（lambda x:x[0]['overall_prop']）

从列表中获取第一个元素，并从第一个元素的字典中获取

overall_prop

值

这里的假设是

col1

中的每一行都是一个字典，并且具有键

overall\u prop

import pandas as pd
df = pd.DataFrame({'col1':[[{'overall_prop': '0.001'},
                            {'overall_prop': '0.002'},
                            {'overall_prop': '0.003'}],
                           [{'overall_prop': '0.004'},
                            {'overall_prop': '0.005'},
                            {'overall_prop': '0.006'}],
                           [{'overall_prop': '0.007'},
                            {'overall_prop': '0.008'},
                            {'overall_prop': '0.009'}],
                           [{'overall_prop': '0.010'},
                            {'overall_prop': '0.011'},
                            {'overall_prop': '0.012'}],
                           [{'overall_prop': '0.013'},
                            {'overall_prop': '0.014'},
                            {'overall_prop': '0.015'}]]})

print (df)

df['overall_prop'] = df['col1'].apply(lambda x: x[0]['overall_prop'])
print (df)

其输出将为：

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1  [{'overall_prop': '0.004'}, {'overall_prop': '...        0.004
2  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
3  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
4  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                               [{}]          NaN
2  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
3  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
4  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
5  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                 col1 overall_prop
0   [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                                [{}]          NaN
2                                    {'bad': '0.999'}          NaN
3                                                  {}          NaN
4                                   just a bad string          NaN
5                                                 250          NaN
6                                               35.25          NaN
7                                                True          NaN
8                                               False          NaN
9                                            (10, 20)          NaN
10  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
11  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
12  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
13  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

编辑版本2:col1是dict列表，列表中的dict为空 如果您的行没有将

totall_prop

作为键，则可以使用此键

df = pd.DataFrame({'col1':[[{'overall_prop': '0.001'},
                            {'overall_prop': '0.002'},
                            {'overall_prop': '0.003'}],
                           [{}],
                           [{'incorrect_key': '0.004'},
                            {'overall_prop': '0.005'},
                            {'overall_prop': '0.006'}],
                           [{'overall_prop': '0.007'},
                            {'overall_prop': '0.008'},
                            {'overall_prop': '0.009'}],
                           [{'overall_prop': '0.010'},
                            {'overall_prop': '0.011'},
                            {'overall_prop': '0.012'}],
                           [{'overall_prop': '0.013'},
                            {'overall_prop': '0.014'},
                            {'overall_prop': '0.015'}]]})

import numpy as np

df['overall_prop'] = df['col1'].apply(lambda x: x[0]['overall_prop'] if 'overall_prop' in x[0] else np.NaN)

其输出将为：

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1  [{'overall_prop': '0.004'}, {'overall_prop': '...        0.004
2  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
3  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
4  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                               [{}]          NaN
2  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
3  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
4  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
5  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                 col1 overall_prop
0   [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                                [{}]          NaN
2                                    {'bad': '0.999'}          NaN
3                                                  {}          NaN
4                                   just a bad string          NaN
5                                                 250          NaN
6                                               35.25          NaN
7                                                True          NaN
8                                               False          NaN
9                                            (10, 20)          NaN
10  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
11  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
12  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
13  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

编辑版本3:col1具有不同类型的数据 其输出将为：

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1  [{'overall_prop': '0.004'}, {'overall_prop': '...        0.004
2  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
3  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
4  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                col1 overall_prop
0  [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                               [{}]          NaN
2  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
3  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
4  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
5  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

                                                 col1 overall_prop
0   [{'overall_prop': '0.001'}, {'overall_prop': '...        0.001
1                                                [{}]          NaN
2                                    {'bad': '0.999'}          NaN
3                                                  {}          NaN
4                                   just a bad string          NaN
5                                                 250          NaN
6                                               35.25          NaN
7                                                True          NaN
8                                               False          NaN
9                                            (10, 20)          NaN
10  [{'incorrect_key': '0.004'}, {'overall_prop': ...          NaN
11  [{'overall_prop': '0.007'}, {'overall_prop': '...        0.007
12  [{'overall_prop': '0.010'}, {'overall_prop': '...        0.010
13  [{'overall_prop': '0.013'}, {'overall_prop': '...        0.013

---

您是否尝试过

df['col1'].str.map（lambda x:x[0]['overall_prop']）

@JoeFerndz:StringMethods没有mapmy bad。我应该多加注意。您不能在str上使用map。而是使用

df.col1.apply（lambda x:x[0]['totall_prop']）

。下面是我的回答。我在描述中说过，问题是其中一行中有一个空的dictionary对象会破坏所有内容。这也解决了。请参阅我的答案部分底部的更新代码谢谢您更新解决方案，但仍在抱怨：{TypeError}'float'对象不是subscriptable我想原因是在您的示例中，列表中有空的dic。就我而言，空的dic不在列表中，明白了。我显式地检查它是否是一个列表，列表中是否有一个字典，以及字典的第一个元素是否是

totall\u prop

。看看这能否解决问题