[英]Pandas - Extracting values from a Dataframe column
I have a Dataframe in the below format:我有一个 Dataframe 格式如下:
cust_id, cust_details
101, [{'self': 'https://website.com/rest/api/2/customFieldOption/1', 'value': 'Type-A', 'id': '1'},
{'self': 'https://website.com/rest/api/2/customFieldOption/2', 'value': 'Type-B', 'id': '2'},
{'self': 'https://website.com/rest/api/2/customFieldOption/3', 'value': 'Type-C', 'id': '3'},
{'self': 'https://website.com/rest/api/2/customFieldOption/4', 'value': 'Type-D', 'id': '4'}]
102, [{'self': 'https://website.com/rest/api/2/customFieldOption/5', 'value': 'Type-X', 'id': '5'},
{'self': 'https://website.com/rest/api/2/customFieldOption/6', 'value': 'Type-Y', 'id': '6'}]
I am trying to extract for every cust_id all cust_detail values我正在尝试为每个 cust_id 提取所有 cust_detail 值
Expected output:预期 output:
cust_id, new_value
101,Type-A, Type-B, Type-C, Type-D
102,Type-X, Type-Y
Easy answer:简单的回答:
df['new_value'] = df.cust_details.apply(lambda ds: [d['value'] for d in ds])
More complex, potentially better answer:更复杂,可能更好的答案:
Rather than storing lists of dictionaries in the first place, I'd recommend making each dictionary a row in the original dataframe.我建议不要首先存储字典列表,而是在原始 dataframe 中将每个字典设为一行。
df = pd.concat([
df['cust_id'],
pd.DataFrame(
df['cust_details'].explode().values.tolist(),
index=df['cust_details'].explode().index
)
], axis=1)
If you need to group values by id, you can do so via standard groupby methods:如果需要按 id 对值进行分组,可以通过标准的 groupby 方法进行:
df.groupby('cust_id')['value'].apply(list)
This may seem more complex, but depending on your use case might save you effort in the long-run.这可能看起来更复杂,但根据您的用例,从长远来看可能会节省您的精力。
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