Pandas：一种使用命名元组列表初始化数据框的简洁方法
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Pandas: A clean way to initialize data frame with a list of namedtuple
提问by Mai
I'm new to pandas, therefore perhaps I'm asking a very stupid question. Normally initialization of data frame in pandas would be column-wise, where I put in dict with key of column names and values of list-like object with same length.
我是Pandas的新手，因此也许我问了一个非常愚蠢的问题。通常，pandas 中数据帧的初始化是按列进行的，我将列名的键和具有相同长度的类列表对象的值放入 dict 中。
But I would love to initialize row-wise without dynamically concat-ing rows. Say I have a list of namedtuple, is there a optimized operation that will give me a pandas data frame directly from it?
但是我很想在没有动态连接行的情况下逐行初始化。假设我有一个namedtuple 列表，是否有优化的操作可以直接从它给我一个pandas 数据框？
Many many thanks
非常感谢
采纳答案by filmor
The function you want is from_records.
您想要的功能是from_records。
For namedtupleinstances you must pass the _fieldsproperty of the namedtuple to the columnsparameter of from_records, in addition to a list of namedtuples:
例如，除了命名元组列表之外，namedtuple您还必须将_fieldsnamedtuple的属性传递给 的columns参数from_records：
df = pd.DataFrame.from_records(
[namedtuple_instance1, namedtuple_instance2],
columns=namedtuple_type._fields
)
If you have dictionaries, you can use it directly as
如果你有字典，你可以直接使用它作为
df = pd.DataFrame.from_records([dict(a=1, b=2), dict(a=2, b=3)])
回答by Andy Hayden
In a similar vein to creating a Series from a namedtuple, you can use the _fieldsattribute:
与从 namedtuple 创建 Series类似，您可以使用该_fields属性：
In [11]: Point = namedtuple('Point', ['x', 'y'])
In [12]: points = [Point(1, 2), Point(3, 4)]
In [13]: pd.DataFrame(points, columns=Point._fields)
Out[13]:
x y
0 1 2
1 3 4
Assuming they are all of the same type, in this example all Points.
假设它们都是相同的类型，在这个例子中都是Points。
