Python 如何将模型对象列表转换为熊猫数据框？
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How to convert list of model objects to pandas dataframe?
提问by ezamur
I have an array of objects of this class
我有一个此类的对象数组
class CancerDataEntity(Model):
age = columns.Text(primary_key=True)
gender = columns.Text(primary_key=True)
cancer = columns.Text(primary_key=True)
deaths = columns.Integer()
...
When printed, array looks like this
打印时，数组看起来像这样
[CancerDataEntity(age=u'80-85+', gender=u'Female', cancer=u'All cancers (C00-97,B21)', deaths=15306), CancerDataEntity(...
I want to convert this to a data frame so I can play with it in a more suitable way to me - to aggregate, count, sum and similar. How I wish this data frame to look, would be something like this:
我想将其转换为数据框，以便我可以以更适合我的方式使用它 - 聚合、计数、求和等。我希望这个数据框看起来像这样：
age gender cancer deaths
0 80-85+ Female ... 15306
1 ...
Is there a way to achieve this using numpy/pandas easily, without manually processing the input array?
有没有办法使用 numpy/pandas 轻松实现这一点，而无需手动处理输入数组？
采纳答案by ezamur
Code that leads to desired result:
导致预期结果的代码：
variables = arr[0].keys()
df = pd.DataFrame([[getattr(i,j) for j in variables] for i in arr], columns = variables)
Thanks to @Serbitar for pointing me to the right direction.
感谢@Serbitar 为我指明了正确的方向。
回答by Serbitar
try:
尝试：
variables = list(array[0].keys())
dataframe = pandas.DataFrame([[getattr(i,j) for j in variables] for i in array], columns = variables)
回答by OregonTrail
A much cleaner way to to this is to define a to_dictmethod on your class and then use pandas.DataFrame.from_records
一个更简洁的方法是to_dict在你的类上定义一个方法，然后使用pandas.DataFrame.from_records
class Signal(object):
def __init__(self, x, y):
self.x = x
self.y = y
def to_dict(self):
return {
'x': self.x,
'y': self.y,
}
e.g.
例如
In [87]: signals = [Signal(3, 9), Signal(4, 16)]
In [88]: pandas.DataFrame.from_records([s.to_dict() for s in signals])
Out[88]:
x y
0 3 9
1 4 16
回答by Shital Shah
Just use:
只需使用：
DataFrame([o.__dict__ for o in my_objs])
Full example:
完整示例：
import pandas as pd
# define some class
class SomeThing:
def __init__(self, x, y):
self.x, self.y = x, y
# make an array of the class objects
things = [SomeThing(1,2), SomeThing(3,4), SomeThing(4,5)]
# fill dataframe with one row per object, one attribute per column
df = pd.DataFrame([t.__dict__ for t in things ])
print(df)
This prints:
这打印：
x y
0 1 2
1 3 4
2 4 5
回答by typhon04
I would like to emphasize Jim Hunziker's comment.
我想强调Jim Hunziker的评论。
pandas.DataFrame([vars(s) for s in signals])
It is far easier to write, less error-prone and you don't have to change the to_dict()function every time you add a new attribute.
编写起来要容易得多，不易出错，而且to_dict()每次添加新属性时都不必更改函数。
If you want the freedom to choose which attributes to keep, the columnsparameter could be used.
如果您希望自由选择要保留的属性，则可以使用columns参数。
pandas.DataFrame([vars(s) for s in signals], columns=['x', 'y'])
The downside is that it won't work for complex attributes, though that should rarely be the case.
缺点是它不适用于复杂的属性，尽管这种情况很少发生。
