title: 'DataFrame' object has no attribute 'data' but I have added it in my dataset
body: <p>I was following this tutorial on using deep belief networks <a href="https://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/" rel="nofollow noreferrer">https://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/</a> then I decided to attach my external dataset and I get the following error :</p>
  
  <pre><code>Traceback (most recent call last):
    File "C:\Users\Allan\Desktop\aaaaaaa.py", line 21, in &lt;module&gt;
      dataset.data , dataset.target, test_size = 0.33)
    File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 3614, in __getattr__
      return object.__getattribute__(self, name)
  AttributeError: 'DataFrame' object has no attribute 'data'  
  </code></pre>
  
  <p>this is the code that Im using:</p>
  
  <pre><code>#import the necessary packages
  import pandas as pd
  from sklearn.cross_validation import train_test_split
  from sklearn.metrics import classification_report
  from sklearn import datasets
  from nolearn.dbn import DBN
  import numpy as np
  
  
  #loading my dataset
  dataset = pd.read_csv("C:\Users\Allan\Desktop\qqew3.csv")
  
  
  
  #construct the training and testing splits
  (trainX, testX, trainY, testY) = train_test_split(
      dataset.data , dataset.target, test_size = 0.33)
  
  #training
  dbn = DBN(
      [trainX.shape[1], 800, 800, 10],
      learn_rates = 0.3,
      learn_rate_decays = 0.9,
      epochs = 10,
      verbose = 1)
  dbn.fit(trainX, trainY)
  
  
  preds = dbn.predict(testX)
  print (classification_report(testY, preds))
  </code></pre>
  
  <p>The structure of my dataset is as follows:</p>
  
  <pre><code>{'target_names': array(['JURISDICTION NAME','COUNT PARTICIPANTS','COUNT FEMALE','PERCENT FEMALE','COUNT MALE','PERCENT MALE','COUNT GENDER UNKNOWN','PERCENT GENDER UNKNOWN','COUNT GENDER TOTAL','PERCENT GENDER TOTAL','COUNT PACIFIC ISLANDER','PERCENT PACIFIC ISLANDER','COUNT HISPANIC LATINO','PERCENT HISPANIC LATINO','COUNT AMERICAN INDIAN','PERCENT AMERICAN INDIAN','COUNT ASIAN NON HISPANIC','PERCENT ASIAN NON HISPANIC','COUNT WHITE NON HISPANIC','PERCENT WHITE NON HISPANIC','COUNT BLACK NON HISPANIC','PERCENT BLACK NON HISPANIC','COUNT OTHER ETHNICITY','PERCENT OTHER ETHNICITY','COUNT ETHNICITY UNKNOWN','PERCENT ETHNICITY UNKNOWN','COUNT ETHNICITY TOTAL','PERCENT ETHNICITY TOTAL','COUNT PERMANENT RESIDENT ALIEN','PERCENT PERMANENT RESIDENT ALIEN','COUNT US CITIZEN','PERCENT US CITIZEN','COUNT OTHER CITIZEN STATUS','PERCENT OTHER CITIZEN STATUS','COUNT CITIZEN STATUS UNKNOWN','PERCENT CITIZEN STATUS UNKNOWN','COUNT CITIZEN STATUS TOTAL','PERCENT CITIZEN STATUS TOTAL','COUNT RECEIVES PUBLIC ASSISTANCE','PERCENT RECEIVES PUBLIC ASSISTANCE','COUNT NRECEIVES PUBLIC ASSISTANCE','PERCENT NRECEIVES PUBLIC ASSISTANCE','COUNT PUBLIC ASSISTANCE UNKNOWN','PERCENT PUBLIC ASSISTANCE UNKNOWN','COUNT PUBLIC ASSISTANCE TOTAL','PERCENT PUBLIC ASSISTANCE TOTAL']),'data': array([
  [10001,44,22,0.5,22,0.5,0,0,44,100,0,0,16,0.36,0,0,3,0.07,1,0.02,21,0.48,3,0.07,0,0,44,100,2,0.05,42,0.95,0,0,0,0,44,100,20,0.45,24,0.55,0,0,44,100],
  [10002,35,19,0.54,16,0.46,0,0,35,100,0,0,1,0.03,0,0,28,0.8,6,0.17,0,0,0,0,0,0,35,100,2,0.06,33,0.94,0,0,0,0,35,100,2,0.06,33,0.94,0,0,35,100],
  [10003,1,1,1,0,0,0,0,1,100,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,100,0,0,1,1,0,0,0,0,1,100,0,0,1,1,0,0,1,100],
  [10004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
  [10005,2,2,1,0,0,0,0,2,100,0,0,0,0,0,0,1,0.5,0,0,1,0.5,0,0,0,0,2,100,1,0.5,1,0.5,0,0,0,0,2,100,0,0,2,1,0,0,2,100],
  [10006,6,2,0.33,4,0.67,0,0,6,100,0,0,2,0.33,0,0,0,0,1,0.17,3,0.5,0,0,0,0,6,100,0,0,6,1,0,0,0,0,6,100,0,0,6,1,0,0,6,100],
  [10007,1,0,0,1,1,0,0,1,100,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,100,0,0,1,1,0,0,0,0,1,100,1,1,0,0,0,0,1,100],
  [10009,2,0,0,2,1,0,0,2,100,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,2,100,0,0,2,1,0,0,0,0,2,100,0,0,2,1,0,0,2,100],
  [10010,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]),
   'target':array([0,1,1,1,0,0,1,0,1])}
  </code></pre>
  
  <p>As you can see my dataset has the 'data' attribute ,which contains the data to be used for training</p>
  
  <p>Thank you in advance!</p>