Convert list object to dataframe python

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?

asked Jan 25, 2016 at 16:15

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

answered Jan 20, 2017 at 11:10

OregonTrailOregonTrail

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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

answered Mar 4, 2019 at 2:11

Shital ShahShital Shah

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2

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.

answered Aug 6, 2019 at 21:19

typhon04typhon04

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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.

answered Jan 25, 2016 at 20:57

ezamurezamur

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1

try:

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

answered Jan 25, 2016 at 16:26

SerbitarSerbitar

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