从 dataframe 列表中的字典中提取元素

Question

假设我们有一个 dataframe 格式如下：

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

原始数据为 json 格式。 我想从每行列表中的第一个元素中提取'overall_prop'的值，这是我试图提取第一个元素的内容：

 df['col1'].str[0]

一切都很好，然后以下提取'overall_prop' ：

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

但抱怨：

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

因为{} （python dict 对象）变成了nan

然后我尝试了这个：

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来从可以处理 null 值的列表中的字典中提取元素。

Answer 1

编辑版本 1： col1 是字典列表， x[0] 具有整体属性

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，并从字典中的第一个元素中overall_prop值。

这里的假设是col1中的每一行都是一个字典，并且有overall_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)

output 将是：

                                                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

编辑第 2 版： col1 是字典列表和列表中的空字典

如果您有没有以overall_prop作为键的行，则可以使用它。

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)

output 将是：

                                                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

编辑版本 3： col1 具有不同类型的数据

df = pd.DataFrame({'col1':[[{'overall_prop': '0.001'},
                            {'overall_prop': '0.002'},
                            {'overall_prop': '0.003'}],
                           [{}],
                           {'bad':'0.999'},
                           {},
                           'just a bad string',
                           250,
                           35.25,
                           True,
                           False,
                           (10,20),
                           [{'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'}]]})

def prop_check(x):
    if isinstance(x,list) and isinstance(x[0],dict) and 'overall_prop' in x[0]:
        return x[0]['overall_prop']
    else: return np.NaN

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

output 将是：

                                                 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