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DataFrame访问字典中列表中的嵌套键

[英]DataFrame accessing nested keys in list within dictionary

The following data is part of a much larger dataframe with a lot of nested keys. 以下数据是具有大量嵌套键的更大dataframe一部分。 Say I want to access "humidity" or "windSpeed" How would I do that? 说我想要访问“湿度”或“windSpeed”我该怎么做?

df = pd.DataFrame({"data":[{"time":1422802800,"humidity":0.62,"windSpeed":2.62}]})

The purpose is to select only certain keys and append them to a CSV file, rather then appending the entire dataframe to the CSV file. 目的是仅选择某些键并将它们附加到CSV文件,而不是将整个dataframe附加到CSV文件。

You'd need to use apply with a lambda and index into the dict: 你需要在字典中使用带有lambda和index的apply

In[69]:
df['data'].apply(lambda x: x['time'])

Out[69]: 
0    1422802800
Name: data, dtype: int64

and like-wise for humidity: 和湿度一样:

In[71]:
df['data'].apply(lambda x: x['humidity'])

Out[71]: 
0    0.62
Name: data, dtype: float64

I'd advise against storing non-scalar values in a df, it's non-performant as you lose any vectorised advantages of using dataframes 我建议不要在df中存储非标量值,因为你失去了使用数据帧的任何矢量化优势,所以它不具备性能

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