[英]Create pandas DataFrame from numpy array of dictionaries
I have very long numpy array that has multiple dictionnaries as elements:我有很长的 numpy 数组,它有多个字典作为元素:
np.array([{'col1': 'somevalue', 'col2': 2}, {'col1': 'someotherval', 'col2': 4}, {'col1': 'zzzzz', 'col2': 47}], dtype=object)
Is there any way to create a pandas DataFrame where each dictionary would be a row?有没有办法创建一个 pandas DataFrame ,其中每个字典都是一行?
Result should be:结果应该是:
col1 ![]() |
col2![]() |
---|---|
'somevalue' ![]() |
2 ![]() |
'someotherval' ![]() |
4 ![]() |
'zzzzz' ![]() |
47 ![]() |
Also conerting the numpy array to a list would not work as I need to keep memory usage low, so I can't go with pd.DataFrame(list(my_array))
将 numpy 数组转换为列表也不起作用,因为我需要保持 memory 的使用率较低,所以我不能使用
pd.DataFrame(list(my_array))
You can use iter()
to avoid making an intermediate list:您可以使用
iter()
来避免制作中间列表:
pd.DataFrame(iter(arr))
This outputs:这输出:
col1 col2
0 somevalue 2
1 someotherval 4
2 zzzzz 47
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