How to convert list of model objects to pandas dataframe?

Question

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?

Answer 1

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 

Answer 2

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