[英]Convert string containing list of dictionaries in DataFrame to list of dictionaries
I'm having an issue that I haven't been able to solve for two days now, despite massive amount of googling.尽管进行了大量的谷歌搜索,但我遇到了一个问题,我已经两天没能解决了。 I've been downloading data from crunchbase.com.
我一直在从 crunchbase.com 下载数据。 I stored the raw data in a DataFrame.
我将原始数据存储在 DataFrame 中。 However, one variable is stored as a string, which should really be a list of dictionaries.
但是,一个变量存储为一个字符串,它实际上应该是一个字典列表。
Looking at a specific element of the pandas Series yields a string:查看熊猫系列的特定元素会产生一个字符串:
"[{'entity_def_id': 'category', 'permalink': 'media-and-entertainment', 'uuid': '78b58810-ad58-a623-2a80-2a0e3603a544', 'value': 'Media and Entertainment'}, {'entity_def_id': 'category', 'permalink': 'tv', 'uuid': '86d91a85-ff9d-93db-4688-3b608fee756c', 'value': 'TV'}, {'entity_def_id': 'category', 'permalink': 'tv-production', 'uuid': '47592b2e-aaaa-6aa3-d0e9-82ab5e525c2d', 'value': 'TV Production'}]"
Note that some observations in the Series in which this str of list of dicts is stored are missing (if that matters).请注意,存储此字典列表 str 的系列中的一些观察结果丢失(如果这很重要)。
I would like to create new columns in my DataFrame where the column name corresponds to the key and for each observation the corresponding value from the dict;我想在我的 DataFrame 中创建新列,其中列名对应于键,并且对于每个观察,字典中的对应值; however, I don't know how to do that since it is a string, which I can only index with integers, rather than accessing the dictionaries directly.
但是,我不知道该怎么做,因为它是一个字符串,我只能用整数索引它,而不是直接访问字典。 In fact, what
事实上,什么
I've tried to use json.loads, which gives me a TypeError: the JSON object must be str, bytes or bytearray, not Series.我尝试使用 json.loads,这给了我一个 TypeError:JSON 对象必须是 str、bytes 或 bytearray,而不是 Series。
I also tried ast.literal_eval(), which gives me a ValueError: malformed node or string: 0.我也试过 ast.literal_eval(),它给了我一个 ValueError: malformed node or string: 0。
Grateful for any hints and apologies if my formatting/style is not good, it's my first time posting here.如果我的格式/风格不好,感谢任何提示和道歉,这是我第一次在这里发帖。
Just use the eval()
function只需使用
eval()
函数
import pandas as pd
s = "[{'entity_def_id': 'category', 'permalink': 'media-and-entertainment', 'uuid': '78b58810-ad58-a623-2a80-2a0e3603a544', 'value': 'Media and Entertainment'}, {'entity_def_id': 'category', 'permalink': 'tv', 'uuid': '86d91a85-ff9d-93db-4688-3b608fee756c', 'value': 'TV'}, {'entity_def_id': 'category', 'permalink': 'tv-production', 'uuid': '47592b2e-aaaa-6aa3-d0e9-82ab5e525c2d', 'value': 'TV Production'}]"
l = eval(s)
df = pd.DataFrame(l)
Out[1]:
entity_def_id ... value
0 category ... Media and Entertainment
1 category ... TV
2 category ... TV Production
[3 rows x 4 columns]
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