title: NaNs suddenly appearing for sklearn KFolds
body: <p>I'm trying to run cross validation on my data set. The data appears to be clean, but then when I try to run it, some of my data gets replaced by NaNs. I'm not sure why. Has anybody seen this before?</p>
  
  <pre><code>y, X = np.ravel(df_test['labels']), df_test[['variation', 'length', 'tempo']]
  X_train, X_test, y_train, y_test = cv.train_test_split(X,y,test_size=.30, random_state=4444)
  </code></pre>
  
  <p>This is what my X data looked like before KFolds:
  <code>
      variation   length  tempo
  0   0.005144    1183.148118 135.999178
  1   0.002595    720.165442  117.453835
  2   0.008146    397.500952  112.347147
  3   0.005367    1109.819501 172.265625
  4   0.001631    509.931973  135.999178
  5   0.001620    560.365714  151.999081
  6   0.002513    763.377778  107.666016
  7   0.009262    502.083628  99.384014
  8   0.000610    500.017052  143.554688
  9   0.000733    269.001723  117.453835
  </code></p>
  
  <p>My Y data looks like this:
  <code>
  array([ True, False, False,  True,  True,  True,  True, False,  True, False], dtype=bool)
  </code></p>
  
  <p>Now when I try to do the cross val:</p>
  
  <pre><code>kf = KFold(X_train.shape[0], n_folds=4, shuffle=True)
  
  for train_index, val_index in kf:
      cv_train_x = X_train.ix[train_index]
      cv_val_x = X_train.ix[val_index]
      cv_train_y = y_train[train_index]
      cv_val_y = y_train[val_index]
      print cv_train_x
  
      logreg = LogisticRegression(C = .01)
      logreg.fit(cv_train_x, cv_train_y)
      pred = logreg.predict(cv_val_x)
      print accuracy_score(cv_val_y, pred)
  </code></pre>
  
  <p>When I try to run this, I error out with the below error, so I add the print statement.<br>
  <code>ValueError: Input contains NaN, infinity or a value too large for dtype('float64').</code></p>
  
  <p>In my print statement, this is what it printed, some data became NaNs.
  <code>
     variation       length       tempo
  0        NaN          NaN         NaN
  1        NaN          NaN         NaN
  2   0.008146   397.500952  112.347147
  3   0.005367  1109.819501  172.265625
  4   0.001631   509.931973  135.999178
  </code></p>
  
  <p>I'm sure I'm doing something wrong, any ideas? As always, thank you so much!</p>