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Creating a data frame from an object

I am provided with a collection of objects and need to store their attributes in a dataframe. All of these objects have the same attributes, but different values.

I have tried

dicts = [x.__dict__ for x in objectCollection]
df = pd.concat(dicts)

but it will give me

TypeError: cannot concatenate a non-NDFrame object

Next, for reproduction purposes, my object r

(it contains 4 dictionaries)

In[94]: r
Out[94]: 
[{'JB': 0.5261750636924186,
  'JBar': 0.5261750636925998,
  'U': 0.3294737627050343,
  'VB': 0,
  'VBar': 0,
  'WB': 0.3879376586708586,
  'WBar': 0.38793765867087865,
  'YB': 0.48616322013005736,
  'YBar': 0.4861632201302899,
  'pB': 1,
  'pBar': 1.0000000000002274,
  'theta': 30.452217802750784,
  'thetaB': 15.226108901371752,
  'thetaBar': 15.226108901379034,
  'u': 0.027673559739652746,
  'vB': 0.4213606342845696,
  'vBar': 0.4213606342847711,
  'wB': 0.40542217802756686,
  'wBar': 0.4054221780275895},
 {'JB': 0.591730026927601,
  'JBar': 0.5888459364503311,
  'U': 0.3574380470030322,
  'VB': 0,
  'VBar': 0,
  'WB': 0.4133529860815169,
  'WBar': 0.42286537327529117,
  'YB': 0.4450754942945968,
  'YBar': 0.4420654404537785,
  'pB': 1,
  'pBar': 1.0068090684451363,
  'theta': 35.72497708330982,
  'thetaB': 17.92309565619036,
  'thetaBar': 17.801881427119454,
  'u': 0.02414497177678684,
  'vB': 0.43275263877136705,
  'vBar': 0.4298259246315051,
  'wB': 0.43134506957181085,
  'wBar': 0.44209406710077587},
 {'JB': 0.46774949304709174,
  'JBar': 0.783373528458816,
  'U': 0.4259229207218348,
  'VB': 0,
  'VBar': 0,
  'WB': 0.47789508661595614,
  'WBar': 0.5129644238839255,
  'YB': 0.5805290540642507,
  'YBar': 0.39605190181885574,
  'pB': 1,
  'pBar': 1.4657903456561598,
  'theta': 39.39305475097706,
  'thetaB': 12.929841051766449,
  'thetaBar': 26.463213699210606,
  'u': 0.02341904411689341,
  'vB': 0.302804518015738,
  'vBar': 0.6197431690965912,
  'wB': 0.4948305475091409,
  'wBar': 0.5414095820747569},
 {'JB': 0.5246012340492012,
  'JBar': 0.8731358801035817,
  'U': 0.4675627478175433,
  'VB': 0,
  'VBar': 0,
  'WB': 0.5155637079793887,
  'WBar': 0.5645778456068301,
  'YB': 0.5312340916010532,
  'YBar': 0.3593021864121634,
  'pB': 1,
  'pBar': 1.4785161674233223,
  'theta': 45.929330938192074,
  'thetaB': 15.162892309068837,
  'thetaBar': 30.766438629123233,
  'u': 0.020410094185461556,
  'vB': 0.3094760601521056,
  'vBar': 0.6279459101716279,
  'wB': 0.5334306672268627,
  'wBar': 0.5960674456434281}]
+3


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2 answers


The pandas DataFrame constructor takes a list of dicts and can parse them into a DataFrame. This works for me

df = pd.DataFrame(r)


This gave me a framework with columns as keys and their attributes as strings. Is this what you were trying to achieve?

+6


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Try to build pd.DataFrame in a list comprehension:



import pandas as pd
pd.concat([pd.DataFrame(x[1], index=[x[0]]) for x in enumerate(r)])
+4


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