How to convert list of model objects to pandas dataframe?

I have an array of objects of this class

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

[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:

     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?

A much cleaner way to to this is to define a to_dict method on your class and then use 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.

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

Just use:

DataFrame([o.__dict__ for o in my_objs])

Full example:

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:

   x  y
0  1  2
1  3  4
2  4  5

Code that leads to desired result:

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.

I would like to emphasize Jim Hunziker's comment.

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.

If you want the freedom to choose which attributes to keep, the columns parameter could be used.

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.

try:

variables = list(array[0].keys())
dataframe = pandas.DataFrame([[getattr(i,j) for j in variables] for i in array], columns = variables)