有没有一种简单的方法可以从属性对象的Iterable构造一个PandasDataFrame？

vwhgwdsa 于 2022-12-02 发布在其他

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我们可以用dataclass es这样做：

from dataclasses import dataclass
import pandas as pd

@dataclass
class MyDataClass:
    i: int
    s: str

df = pd.DataFrame([MyDataClass("a", 1), MyDataClass("b", 2)])

这使得DataFramedf如人们所期望的那样具有列i和s。
对于attrs类，有没有简单的方法可以做到这一点？
我可以通过迭代对象的属性，构造一个类似dict[str, list]（在本例中为{"i": [1, 2], "s": ["a", "b"]}）类型的对象，并从中构造DataFrame来实现这一点，但直接支持attrs对象会更好。

pandas

来源：https://stackoverflow.com/questions/74618499/is-there-an-easy-way-to-construct-a-pandas-dataframe-from-an-iterable-of-attrs-o

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按热度按时间

gfttwv5a1#

You can access the dictionary at the heart of a dataclass like so

a = MyDataClass("a", 1)
a.__dict__

this outputs:

{'i': 'a', 's': 1}

Knowing this, if you have an iterable arr of type MyDataClass , you can access the __dict__ attribute and construct a dataframe

arr = [MyDataClass("a", 1), MyDataClass("b", 2)]
df = pd.DataFrame([x.__dict__ for x in arr])

df outputs:

i  s
0  a  1
1  b  2

The limitation with this approach that if the slots option is used, then this will not work.
Alternatively, it is possible to convert the data from a dataclass to a tuple or dictionary using dataclasses.astuple and dataclasses.asdict respectively.
The data frame can be also constructed using either of the following:

# using astuple
df = pd.DataFrame(
  [dataclasses.astuple(x) for x in arr], 
  columns=[f.name for f in dataclasses.fields(MyDataClass)]
)

# using asdict
df = pd.DataFrame([dataclasses.asdict(x) for x in arr])

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有没有一种简单的方法可以从属性对象的Iterable构造一个PandasDataFrame？

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